SYSTEMS AND METHODS FOR PREDICTIVE HARVESTING LOGISTICS

FIELD OF THE DESCRIPTION

[0001] The present description relates to agriculture. More specifically, the present description relates to agricultural harvesting operations.

BACKGROUND

[0002] There are a wide variety of different types of agricultural machines. Some agricultural machines include harvesters, such as combine harvesters, sugarcane harvesters, cotton harvesters, forage harvesters, and windrowers. Some harvesters can also be fitted with different types of headers to harvest different types of crops. Some agricultural machines include receiving machines, such as tractors and grain carts and trucks and trailers. The receiving machines receive and transport material harvested by harvesters.

[0003] The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.

SUMMARY

[0004] One or more maps are obtained by an agricultural system. The one or more maps map characteristic values at different geographic locations in a worksite, such as a field. The agricultural system identifies one or more operational constraints. The agricultural system generates a control output to control operation of a mobile machine operating in an agricultural harvesting operation based on the one or more operational constraints.

[0005] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006] FIG. 1 is a partial pictorial, partial schematic illustration of a self-propelled agricultural harvester. [0007] FIG. 2 is a partial plan view, partial pictorial illustration of one example of an agricultural harvesting system, including an agricultural harvester and receiving machine(s), according to some examples of the present disclosure.

[0008] FIG. 3 is a block diagram showing some portions of an agricultural harvesting system, including a mobile machine, such as an agricultural harvester, in more detail, according to some examples of the present disclosure.

[0009] FIG. 4 is a block diagram showing one example of a predictive model generator and predictive map generator.

[0010] FIG. 5 is a block diagram showing one example of a predictive model generator and predictive map generator.

[0011] FIGS. 6A-6B (collectively referred to herein as FIG. 6) is a flow diagram illustrating one example of operation of an agricultural harvesting system in generating a map.

[ 0012 ] FIG. 7 is a block diagram showing one example of a harvesting logistics module in more detail.

[0013] FIG. 8A is a pictorial illustration showing one example of a harvesting logistics module in the course of controlling an agricultural harvesting operation.

[0014] FIG. 8B is a pictorial illustration showing one example of a harvesting logistics module in the course of controlling an agricultural harvesting operation.

[0015] FIG. 9 is a flow diagram illustrating one example of operation of an agricultural harvesting system for controlling a harvesting operation.

[0016] FIG. 10 is a block diagram showing one example of a mobile machine in communication with a remote server environment.

[0017] FIGS. 11-13 show examples of mobile devices that can be used in an agricultural harvesting system.

[0018] FIG. 14 is a block diagram showing one example of a computing environment that can be used in an agricultural harvesting system.

DETAILED DESCRIPTION

[0019] For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are fully contemplated as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, and/or steps described with respect to one example may be combined with the features, components, and/or steps described with respect to other examples of the present disclosure.

[0020] In some examples, the present description relates to using in-situ data taken concurrently with an operation, such as an agricultural harvesting operation, in combination with prior or predicted data, such as prior or predicted data represented in a map, to generate a predictive model and a predictive map. In some examples, the predictive map can be used to control a mobile machine, such as an agricultural harvester or a receiving machine, or both.

[0021] During an agricultural harvesting operation an agricultural harvester engages crop plants at a worksite (e.g., field) and harvests the crop. The harvested crop material (e.g., grain, etc.), is transferred from the agricultural harvester to material receptacle of a receiving machine, such as a grain cart towed by a tractor, or a trailer towed by a truck. In this way, the harvested crop material can be transported, by the receiving machine, from the field to another location, such as a storage location (e.g., storage bin, storage bunk, silo, barn, etc.) or to a purchasing facility (e.g., a mill, etc.). In some examples, the crop material can be transferred from one receiving machine to another receiving machine. For instance, the crop material may initially be transferred from the harvester to a towed grain cart. The towed grain cart may include a material transfer subsystem that can be used to transfer material from the grain cart to another receiving machine, such as a trailer towed by a truck.

[0022] During such an operation, the receiving machines may travel across the field multiple times throughout the operation. This increased traffic, along with the additional weight of the harvested material, can increase compaction at the field. Compaction can have detrimental effects on future operations (e.g., future tillage or planting operations) as well as detrimental effects on the growing conditions of the field. For instance, compacted soil may retain less moisture, may be a less ideal growing environment for plants, as well as various other effects. Ideally, the receiving machines are filled to capacity prior to traveling away from the field. However, for various reasons, it may be more desirable to fill the receiving machine to less than capacity. For instance, the additional weight associated with filling to capacity may increase compaction at the field. [ 0023] Additionally, the dynamics of the machine (e.g., pitch and roll), due to the terrain, may make material spill or shift more likely, particularly when the machine is filled to or beyond a certain level.

[ 0024 ] Further, the operators operating in the given harvesting operation may be of variable status, such as variable skill, experience, and/or fatigue. Thus, it may be desirable to limit operational parameters of the receiving machine or the harvester based on the operator status, such as one or more of the operator skill, the operator experience, the operator fatigue, as well as various other operator status characteristics.

[ 0025] The present description relates to a system that can identify one or more of compaction-based operational constraints, field-based operational constraints (e.g., terrain constraints or field feature constraints), operator-based operational constraints, and other operational constraints (e.g., distance travelled-based operational constraints) during a harvesting operation and can control a harvesting operation based on one or more of the compaction-based constraints, field-based constraints, and operator-based constraints.

[ 0026] In one example, the present description relates to obtaining an information map such as a vegetative index map. A vegetative index map illustratively maps georeferenced vegetative index values (which may be indicative of vegetative growth or plant health) across different geographic locations in a field of interest. One example of a vegetive index includes a normalized difference vegetation index (ND VI). There are many other vegetative indices that are within the scope of the present disclosure. In some examples, a vegetive index map be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the plants. Without limitations, these bands may be in the microwave, infrared, visible or ultraviolet portions of the electromagnetic spectrum. A vegetative index map can be used to identify the presence and location of vegetation. In some examples, these maps enable vegetation to be identified and georeferenced in the presence of bare soil, crop residue, or other plants, including crop or other weeds. The sensor readings can be taken at various times during the growing season (or otherwise prior to spraying), such as during satellite observation of the field of interest, a fly over operation (e.g., manned or unmanned aerial vehicles), sensor readings during a prior operation (e.g., prior to spraying or prior to a particular spraying operation) at the field of interest, as well as during a human scouting operation. The vegetative index map can be generated in a variety of other ways. [0027] In one example, the present description relates to obtaining an information map, such as a topographic map. A topographic map illustratively maps topographic characteristic values across different geographic locations in a field of interest, such as elevations of the ground across different geographic locations in a field of interest. Since ground slope is indicative of a change in elevation, having two or more elevation values allows for calculation of slope across the areas having known elevation values. Greater granularity of slope can be accomplished by having more areas with known elevation values. As an agricultural harvester travels across the terrain in known directions, the pitch and roll of the agricultural harvester can be determined based on the slope of the ground (i.e., areas of changing elevation). Topographic characteristics, when referred to below, can include, but are not limited to, the elevation, slope (e.g., including the machine orientation relative to the slope), and ground profile (e.g., roughness). The topographic map can be derived from sensor readings taken during a previous operation on the field of interest or from an aerial survey of the field (such as a plane, drone, or satellite equipped with lidar or other distance measuring devices). In some examples, the topographic map can be obtained from third parties. The topographic map can be generated in a variety of other ways.

[0028] In one example, the present description relates to obtaining an information map, such as a soil property map. A soil property map illustratively maps soil property values (which may be indicative of soil type, soil moisture, soil cover, soil structure, as well as various other soil properties) across different geographic locations in a field of interest. The soil property maps thus provide geo-referenced soil properties across a field of interest. Soil type can refer to taxonomic units in soil science, wherein each soil type includes defined sets of shared properties. Soil types can include, for example, sandy soil, clay soil, silt soil, peat soil, chalk soil, loam soil, and various other soil types. Soil moisture can refer to the amount of water that is held or otherwise contained in the soil. Soil moisture can also be referred to as soil wetness. Soil cover can refer to the amount of items or materials covering the soil, including, vegetation material, such as crop residue or cover crop, debris, as well as various other items or materials. Commonly, in agricultural terms, soil cover includes a measure of remaining crop residue, such as a remaining mass of plant stalks, as well as a measure of cover crop. Soil structure can refer to the arrangement of solid parts of the soil and the pore space located between the solid parts of the soil. Soil structure can include the way in which individual particles, such as individual particles of sand, silt, and clay, are assembled. Soil structure can be described in terms of grade (degree of aggregation), class (average size of aggregates), and form (types of aggregates), as well as a variety of other descriptions. These are merely examples. Various other characteristics and properties of the soil can be mapped as soil property values on a soil property map.

[0029] These soil property maps can be generated on the basis of data collected during another operation corresponding to the field of interest, for example, previous agricultural operations in the same season, such as planting operations or spraying operations, as well as previous agricultural operations performed in past seasons, such as a previous harvesting operation. The agricultural machines performing those agricultural operations can have on-board sensors that detect characteristics indicative of soil properties, for example, characteristics indicative of soil type, soil moisture, soil cover, soil structure, as well as various other characteristics indicative of various other soil properties. Additionally, operating characteristics, machine settings, or machine performance characteristics of the agricultural machines during previous operations along with other data can be used to generate a soil property map. For instance, header height data indicative of a height of an agricultural harvester’s header across different geographic locations in the field of interest during a previous harvesting operation along with weather data that indicates weather conditions such as precipitation data or wind data during an interim period (such as the period since the time of the previous harvesting operation and the generation of the soil property map) can be used to generate a soil moisture map. For example, by knowing the height of the header, the amount of remaining plant residue, such as crop stalks, can be known or estimated and, along with precipitation data, a level of soil moisture can be predicted. This is merely an example.

[0030] In other examples, surveys of the field of interest can be performed, either by various machines with sensors, such as imaging systems, or by humans. The data collected during these surveys can be used to generate a soil property map. For instance, aerial surveys of the field of interest can be performed in which imaging of the field is conducted, and, on the basis of the image data, a soil property map can be generated. In another example, a human can go into the field to collect various data or samples, with or without the assistance of devices such as sensors, and, on the basis of the data or samples, a soil property map of the field can be generated. For instance, a human can collect a core sample at various geographic locations across the field of interest. These core samples can be used to generate soil property maps of the field. In other examples, the soil property maps can be based on user or operator input, such as an input from a farm manager, which may provide various data collected or observed by the user or operator.

[0031] Additionally, the soil property map can be obtained from remote sources, such as third-party service providers or government agencies, for instance, the USDA Natural Resources Conservation Service (NRCS), the United States Geological Survey (USGS), as well as from various other remote sources.

[0032] In some examples, a soil property map may be derived from sensor readings of one or more bands of electromagnetic radiation reflected by the soil (or surface of the field). Without limitation, these bands may be in the microwave, infrared, visible or ultraviolet portions of the electromagnetic spectrum.

[0033] The soil property map can be generated in a variety of other ways.

[0034] In one example, the present description relates to obtaining an information map, such as a crop state map. Crop state may define whether the crop is down, standing, partially down, the orientation of downing (e.g., compass direction) as well as the magnitude of downing. A crop state map illustratively maps the crop state across different locations in a field of interest. The crop state map may be generated from aerial or other images of the field of interest, from images or other sensor readings taken during a prior operation in the field (e.g., a prior spraying operation) or in other ways prior to harvesting. The crop state map may generated in a variety of other ways. [0035] In one example, the present description relates to obtaining an information map, such as a biomass map. A biomass map illustratively maps biomass values across different geographic locations in a field of interest. The biomass map can be generated based on historical biomass values, based on sensor readings taken during an aerial survey of the field of interest or during another operation on the field of interest, such as during a spraying operation performed by a sprayer with a sensor that detects characteristic(s) of the plants indicative of biomass, from human scouting of the field, or derived from other values, such as vegetative index values. The biomass map can be generated in a variety of other ways.

[0036] In one example, the present description relates to obtaining an information map, such as a predictive yield map. A predictive yield map illustratively maps predictive yield values across different geographic locations in a field of interest. The predictive yield values may based on sensor readings taken during an aerial survey of the field of interest or during another operation on the field of interest, or derived from other values, such as vegetative index values. In one example, the predictive yield map may be generated during the agricultural harvesting operation using one or more information maps, in-situ data, and predictive modeling. The predictive yield map can be generated in a variety of other ways.

[0037] In one example, the present description relates to obtaining an information map, such as a historical yield map. A historical yield map illustratively maps historical yield values across different geographic locations of interest. The historical yield values may be derived from sensor readings from previous harvesting operations on the field of interest or another field, such as another field having had a similar crop or crop genotype. The historical yield values may be derived from post harvesting measurement, taken after the previous harvesting operation was completed. The historical yield map may be generated in a variety of other ways.

[0038] In one example, the present description relates to obtaining an information map, such as a seeding map. A seeding map illustratively maps values of seeding characteristics (e.g., seed location, seed spacing, seed population, seed genotype, etc.) across different geographic locations in a field of interest. The seeding map may be derived from control signals used by a planter when planting seeds or from sensors on the planter. The planters may also include geographic position sensors that geolocate the seed characteristics on the field. The seeding map can be generated in a variety of other ways.

[0039] In other examples, one or more other types of information maps can be obtained. The various other types of information maps illustratively map values of various other characteristics across different geographic locations in a field of interest.

[0040] The present discussion proceeds, in some examples, with respect to systems that obtain one or more information maps of a worksite (e.g., field) and also use an in-situ sensor to detect a characteristic. The systems generate a model that models a relationship between the values on the one or more obtained maps and the output values from the in-situ sensor. The model is used to generate a predictive map that predicts, for example, values of the characteristic detected by the in-situ sensor to different geographic locations in the worksite. The predictive map, generated during an operation, can be presented to an operator or other user or can be used in automatically controlling a mobile machine (e.g., agricultural harvester, receiving machines, etc.) or both, during an agricultural harvesting operation. [ 0041 ] The present discussion proceeds, in some examples, with respect to systems that obtain one or more maps, as well as a variety of other data, and generate logistics outputs for the control of harvesting operation logistics.

[ 0042 ] FIG. 1 is a partial pictorial, partial schematic, illustration of a self-propelled agricultural harvester 100. In the illustrated example, agricultural harvester 100 is a combine harvester. Further, although combine harvesters are provided as examples throughout the present disclosure, it will be appreciated that the present description is also applicable to other types of harvesters, such as cotton harvesters, sugarcane harvesters, self-propelled forage harvesters, windrowers, or other agricultural work machines. Consequently, the present disclosure is intended to encompass the various types of harvesters and is, thus, not limited to combine harvesters. Consequently, the present disclosure is intended to encompass these various types of harvesters and is thus not limited to combine harvesters.

[ 0043] As shown in FIG. 1, agricultural harvester 100 illustratively includes an operator compartment 101, which may have a variety of different operator interface mechanisms (e.g., 418 shown in FIG. 3) for controlling agricultural harvester 100. Agricultural harvester 100 includes a front-end subsystem that has front-end equipment, such as a header 102, and a cutter generally indicated at 104. Header 102 in FIG. 1 is illustrated as a reel-type header, but in other examples, other types of headers are contemplated, such as draper headers, corn headers, etc. Agricultural harvester 100 also includes a feeder house 106, a feed accelerator 108, and a thresher generally indicated at 110. The feeder house 106 and the feed accelerator 108 form part of a material handling subsystem 125. Header 102 is pivotally coupled to a frame 103 of agricultural harvester 100 along pivot axis 105. One or more actuators 107 drive movement of header 102 about axis 105 in the direction generally indicated by arrow 109. Thus, a vertical position of header 102 (the header height) above ground 111 over which the header 102 travels is controllable by actuating actuator 107. While not shown in FIG. 1, agricultural harvester 100 may also include one or more actuators that operate to apply a tilt angle, a roll angle, or both to the header 102 or portions of header 102. Tilt refers to an angle at which the cutter 104 engages the crop. The tilt angle is increased, for example, by controlling header 102 to point a distal edge 113 of cutter 104 more toward the ground. The tilt angle is decreased by controlling header 102 to point the distal edge 113 of cutter 104 more away from the ground. The roll angle refers to the orientation of header 102 about the front-to-back longitudinal axis of agricultural harvester 100 or about an axis parallel to the front-to-back longitudinal axis of agricultural harvester 100.

[0044] Thresher 110 illustratively includes a separation subsystem with a threshing rotor 112, a set of concaves 114, and a separator 116. Agricultural harvester 100 also includes a cleaning subsystem or cleaning shoe (collectively referred to as cleaning subsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve 124. The material handling subsystem 125 also includes discharge beater 126, tailings elevator 128, and clean grain elevator 130. The clean grain elevator 130 moves clean grain into clean grain tank 132. Agricultural harvester 100 also includes a material transfer subsystem that includes an unloading auger/blower 134, chute 134, and spout 136. Unloading auger/blower 134 coveys grain from grain tank 132 through chute 134 and spout 136 such that material can be offloaded from agricultural harvester 100. The material transfer subsystem is deploy able from a storage position (shown in FIG. 1) to a wide range of angular positions for operation. Agricultural harvester 100 also includes a residue subsystem 138 that can include chopper 140 and spreader 142. Agricultural harvester 100 also includes a propulsion subsystem that includes an engine (or other power plant) that drives ground engaging components 144, such as wheels or tracks. In some examples, an agricultural harvester 100 within the scope of the present disclosure may have more than one of any of the subsystems mentioned above. In some examples, agricultural harvester 100 may have left and right cleaning subsystems, separators, etc., which are not shown in FIG. 1.

[0045] In operation, and by way of overview, agricultural harvester 100 illustratively moves through a field in the direction indicated by arrow 147. As agricultural harvester 100 moves, header 102 (and the associated reel 164) engages the crop to be harvested and gathers the crop toward cutter 104. An operator of agricultural harvester 100 can be a local human operator, a remote human operator, or an automated system. An operator command is a command by an operator. The operator of agricultural harvester 100 may determine one or more of a height setting, a tilt angle setting, or a roll angle setting for header 102. For example, the operator inputs a setting or settings to a control system, that controls actuator 107. The control system may also receive a setting from the operator for establishing the tilt angle and roll angle of the header 102 and implement the inputted settings by controlling associated actuators, not shown, that operate to change the tilt angle and roll angle of the header 102. The actuator 107 maintains header 102 at a height above ground 111 based on a height setting and, where applicable, at desired tilt and roll angles. Each of the height, roll, and tilt settings may be implemented independently of the others. The control system responds to header error (e.g., the difference between the height setting and measured height of header 102 above ground 111 and, in some examples, tilt angle and roll angle errors) with a responsiveness that is determined based on a selected sensitivity level. If the sensitivity level is set at a greater level of sensitivity, the control system responds to smaller header position errors, and attempts to reduce the detected errors more quickly than when the sensitivity is at a lower level of sensitivity.

[0046] Returning to the description of the operation of agricultural harvester 100, after crops are cut by cutter 104, the severed crop material is moved through a conveyor in feeder house 106 toward feed accelerator 108, which accelerates the crop material into thresher 110. The crop material is threshed by rotor 112 rotating the crop against concaves 114. The threshed crop material is moved by a separator rotor in separator 116 where a portion of the residue is moved by discharge beater 126 toward the residue subsystem 138. The portion of residue transferred to the residue subsystem 138 is chopped by residue chopper 140 and spread on the field by spreader 142. In other configurations, the residue is released from the agricultural harvester 100 in a windrow. In other examples, the residue subsystem 138 can include weed seed eliminators (not shown) such as seed baggers or other seed collectors, or seed crushers or other seed destroyers.

[0047] Grain falls to cleaning subsystem 118. Chaffer 122 separates some larger pieces of material from the grain, and sieve 124 separates some of finer pieces of material from the clean grain. Clean grain falls to an auger that moves the grain to an inlet end of clean grain elevator 130, and the clean grain elevator 130 moves the clean grain upwards, depositing the clean grain in clean grain tank 132. Residue is removed from the cleaning subsystem 118 by airflow generated by cleaning fan 120. Cleaning fan 120 directs air along an airflow path upwardly through the sieves and chaffers. The airflow carries residue rearwardly in agricultural harvester 100 toward the residue handling subsystem 138.

[ 0048 ] Tailings elevator 128 returns tailings to thresher 110 where the tailings are rethreshed. Alternatively, the tailings also may be passed to a separate re-threshing mechanism by a tailings elevator or another transport device where the tailings are re-threshed as well.

[ 0049] FIG. 1 also shows that, in one example, agricultural harvester 100 includes ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward looking image capture mechanism 151, which may be in the form of a stereo or mono camera, one or more crop property sensors 200, 202, a geographic positioning system 203, and one or more loss sensors 152 provided in the cleaning subsystem 118.

[ 0050 ] Ground speed sensor 146 senses the travel speed of agricultural harvester 100 over the ground. Ground speed sensor 146 may sense the travel speed of the agricultural harvester 100 by sensing the speed of rotation of the ground engaging components (such as wheels or tracks), a drive shaft, an axle, or other components. In some instances, the travel speed may be sensed using geographic positioning system 203, which may be a global positioning system (GPS), a dead reckoning system, a long range navigation (LORAN) system, or a wide variety of other systems or sensors that provide an indication of a geographic positioning of agricultural harvester 100 in a global or local coordinate system. Detecting a change in position over time may provide an indication of travel speed.

[ 0051 ] Loss sensors 152 illustratively provide an output signal indicative of the quantity of grain loss occurring in both the right and left sides of the cleaning subsystem 118. In some examples, sensors 152 are strike sensors which count grain strikes per unit of time or per unit of distance traveled to provide an indication of the grain loss occurring at the cleaning subsystem 118. The strike sensors for the right and left sides of the cleaning subsystem 118 may provide individual signals or a combined or aggregated signal. In some examples, sensors 152 may include a single sensor as opposed to separate sensors provided for each cleaning subsystem 118.

[ 0052 ] Separator loss sensor 148 provides a signal indicative of grain loss in the left and right separators, not separately shown in FIG. 1. The separator loss sensors 148 may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregate signal. In some instances, sensing grain loss in the separators may also be performed using a wide variety of different types of sensors as well.

[0053] Agricultural harvester 100 may also include other sensors and measurement mechanisms. For instance, agricultural harvester 100 may include one or more of the following sensors: a header height sensor that senses a height of header 102 above ground 111; stability sensors that sense oscillation or bouncing motion (and amplitude) of agricultural harvester 100; a residue setting sensor that is configured to sense whether agricultural harvester 100 is configured to chop the residue, produce a windrow, etc.; a cleaning shoe fan speed sensor to sense the speed of cleaning fan 120; a concave clearance sensor that senses clearance between the rotor 112 and concaves 114; a threshing rotor speed sensor that senses a rotor speed of rotor 112; a chaffer clearance sensor that senses the size of openings in chaffer 122; a sieve clearance sensor that senses the size of openings in sieve 124; a material other than grain (MOG) moisture sensor, such as a capacitive moisture sensor, that senses a moisture level of the MOG passing through agricultural harvester 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvester 100; a machine orientation sensor (e.g., inertial measurement unit) that senses the orientation of agricultural harvester 100; mass sensors (e.g., pressure sensors, strain gauges, etc.) that sense a mass of material in grain tank 132; feed rate sensors that sense the feed rate of grain as the grain travels through the feeder house 106, clean grain elevator 130, or elsewhere in the agricultural harvester 100. In some implementations, the feed rate sensors sense the feed rate of biomass through feeder house 106, thresher 110, through the separator 116, or elsewhere in agricultural harvester 100. Further, in some instances, the feed rate sensors sense the feed rate as a mass flow rate of grain through elevator 130 or through other portions of the agricultural harvester 100 or provide other output signals indicative of other sensed variables. Various other sensors are contemplated herein, some of which are discussed in further detail below.

[0054] FIG. 2 is a partial plan view, partial pictorial illustration of an agricultural harvesting system 500 and shows an agricultural harvester 100 and one or more receiving machines 400 operating at a worksite (e.g., field) during a harvesting operation. Agricultural harvesting system 500, as illustrated in FIG. 2, includes agricultural harvester 100, one or more receiving machines 400, one or more remote computing systems 300. Agricultural harvester 100, receiving machines 400, and remote computing systems 300 can communicate over network 359 via respective communication systems. Network 359 and the communication systems will be discussed in more detail in FIG. 3.

[0055] FIG. 2 shows that a receiving machine 400 can be include a towing vehicle and towed implement, such as a tractor 160 and towed grain cart 162 (e.g., receiving machine 400-1) or a truck (e.g., semi-truck) 170 and trailer (e.g., semi-trailer) 172 (e.g., receiving machine 400-2). Various other forms of receiving machines 400 are contemplated herein. In the illustrated example, agricultural harvester 100 is traveling in the direction indicated by arrow 195 and is harvesting crop, while receiving machine 400-1 is traveling alongside agricultural harvester 100 and is receiving harvested material (e.g., grain) from grain tank 132 of agricultural harvester 100 via material transfer subsystem 254 of agricultural harvester 100, which is shown in a deployed position. In other examples, receiving machine 400-1 may travel behind agricultural harvester 100 and receive harvested material. In other examples, receiving machine 400-2 can receive harvested material from agricultural harvester 100, including receiving harvested material from agricultural harvester 100 while traveling in tandem with agricultural harvester 100.

[ 0056] Tractor 106, as illustrated, includes a power plant 163 (e.g., internal combustion engine, battery and electric motors, etc.), ground engaging elements 165 (e.g., wheels or tracks), and an operator compartment 167. Grain cart 162 is coupled to tractor by way of a connection assembly (e.g., one or more of hitch, electrical coupling, hydraulic coupling, pneumatic coupling, etc.) and, as illustrated, includes ground engaging elements 170, such as wheels or tracks, grain bin 172 which includes a volume 174 for receiving material, such as harvested crop material from agricultural harvester 100. Grain cart 162 also includes a material transfer subsystem 454-1 which includes a chute 171, a spout 173, and a conveying mechanism, such as an auger or blower (not shown), as well as various actuator(s) (not shown). Material transfer subsystem 454-1 is actuatable between a storage position (as shown) and a range of deployed positions. Material transfer subsystem 454-1 can be used to transfer material from grain bin 172 to another machine such as receiving machine 400-2, an elevator, a grinder, as well as various other machines or locations.

[ 0057 ] Truck 180, as illustrated, includes a power plant 183 (e.g., internal combustion engine, battery and electric motors, etc.), ground engaging elements 185 (e.g., wheels or tracks), and an operator compartment 187. Trailer 182 is coupled to track by way of a connection assembly (e.g., one or more of a hitch, electrical coupling, hydraulic coupling, pneumatic coupling, etc.) and, as illustrated, includes ground engaging elements 190, such as wheels or tracks, grain bin 192 which includes a volume 194 for receiving material, such as harvested crop material from agricultural harvester 100 or another receiving machine, such as receiving machine 400-1. Trailer 182 also includes a material transfer subsystem 454-2 which includes an actuatable door 191 disposed on the bottom side of trailer 182 as well as various actuator(s) (not shown). Actuatable door 191 is actuatable between an open position and a closed position, such that material in grain bin 192 can exit grain bin 192 via door 191. In one example, the interior walls of grain bin 192 taper towards door 191 such that material exits door 191 via gravity. Thus, material transfer subsystem 454-2 can be used to transfer material from grain bin 192 to another machine, such as an elevator, as well as various other machines or to a storage facility. [0058] It should be noted that other forms of material transfer subsystems 452 are contemplated herein and that the illustrated examples are not meant to limit the present disclosure. [0059] The operator compartments 101, 167, and 187 can include one or more operator interface mechanisms, which will be described below. Receiving machines 400 can include various other components as well, some of which will be described below.

[0060] FIG. 3 is a block diagram of agricultural system 500 in more detail. FIG. 3 shows that agricultural system 500 includes agricultural harvester 100, one or more receiving machines 400, one or more remote computing systems 300, one or more remote user interfaces 364, network 359, and one or more information maps 358. Agricultural harvester 100, itself, illustratively includes one or more processors or servers 202, data store 204, communication system 206, one or more in-situ sensors 208 that sense one or more characteristics at a worksite concurrent with an operation, control system 214, one or more controllable subsystems 216, one or more operator interface mechanisms 218, processing system 238 that processes the sensor data (e.g., signals, images, etc.) generated by in-situ sensors 208 to generate processed sensor data, and can include various other items and functionality 219 as well. In-situ sensors 208 can include feedrate controller output sensors 220, yield sensors 223, fill level sensors 124, heading/speed sensors 225, geographic position sensors 203, and can include various other sensors 228 as well, including, but not limited to those described above in FIG. 1. The in-situ sensors 208 generate values corresponding to sensed characteristics. The information generated by in-situ sensors 208 can be communicated to receiving machines 400 and/or to remote computing systems 300. The information generated by in-situ sensors 208 can be georeferenced to areas of the worksite based on geographic location data provided by geographic position sensors 203. Control system 214, itself, can include one or more controllers 235 for controlling various other items of agricultural harvester 100, and can include other items 237 as well. Controllable subsystems 216 can include propulsion subsystem 250, steering subsystem 252, material transfer subsystem 254, and can include various other subsystems 256 as well, including, but not limited to those discussed above. [0061] Receiving machines 400, themselves, illustratively include one or more processors or servers 402, data store 404, communication system 406, one or more in-situ sensors 408 that sense one or more characteristics at a worksite concurrent with an operation, control system 414, one or more controllable subsystems 416, one or more operator interface mechanisms 418, processing system 438 that processes the sensor data (e.g., signals, images, etc.) generated by in- situ sensors 408 to generate processed sensor data, and can include various other items and functionality 419 as well. In-situ sensors 408 can include fill level sensors 424, heading/speed sensors 425, geographic position sensors 403, and can include various other sensors 428 as well. The in-situ sensors 408 generate values corresponding to sensed characteristics. The information generated by in-situ sensors 408 can be communicated to other receiving vehicles 400, agricultural harvester 100, and/or to remote computing systems 300. The information generated by in-situ sensors 408 can be georeferenced to areas of the worksite based on geographic location data provided by geographic position sensor 403. Control system 414, itself, can include one or more controllers 435 for controlling various other items of a receiving machine 400, and can include other items 437 as well. Controllable subsystems 416 can include propulsion subsystem 450, steering subsystem 452, material transfer subsystem 454, and can include various other subsystems 456 as well.

[ 0062 ] Remote computing systems 300, as illustrated, include one or more processors or servers 301, data store 304, communication system 306, predictive model or relationship generator (collectively referred to herein as “predictive model generator 310”), predictive map generator 312, control zone generator 313, harvesting logistics module 315, processing system 338 which can process sensor data (e.g., signals, images, etc.) generated by in-situ sensors 208 or 408, or both, to generate processed sensor data, and can include various other items and functionality 319. [ 0063] Fill level sensors 224 sense a characteristic indicative of a fill level of grain tank 132. Fill level sensors 224 can be an imaging system, such as a stereo or mono camera, that observes clean grain tank 132 and detects a fill level of material within the grain tank 132. The images generated by the imaging system can be processed, such as by processing system 238 or processing system 338, using suitable imaging processing, to generate a value indicative of the fill level of the grain tank 132. The imaging system can be mounted to the exterior side of the roof of the operator compartment 101, to the grain tank 132, or to other suitable locations on agricultural harvester 100. Fill level sensors 224 can include one or more electromagnetic radiation (ER) sensors that transmit and/or receive electromagnetic radiation (ER) to detect presence of material. For instance, one or more ER sensors can be placed within grain tank 132 at a given distance from a perimeter of the grain tank 132 or mounted to observe the interior of grain tank 132 to detect when the grain pile in the grain tank 132 has reached a given height. Fill level sensors 224 can include one or more mass sensors (such as load cells, strain gauges, pressure sensors, etc.) disposed within grain tank 132 or between grain tank 132 and another component (e.g., an axle or frame) of agricultural harvester 100. The mass sensors sense a mass of the material within grain tank 132 which can be used to derive a fill level of the grain tank 132. Fill level sensors 224 can also include or a one or more feed rate (or mass flow) sensors that measure an amount of material entering grain tank 132. For instance, a feed rate sensor that senses a feed rate of grain through the clean grain elevator 130 of the agricultural harvester 100. Various other types of fill level sensors are also contemplated herein.

[ 0064 ] Heading/speed sensors 225 detect a heading and speed characteristics (e.g., travel speed, acceleration, deceleration, etc.) at which agricultural harvester 100 is traversing the worksite during the operation. This can include sensors that sense the movement (e.g., rotation) of ground-engaging elements (e.g., wheels or tracks 144), or movement of components coupled to the ground engaging elements, or can utilize signals received from other sources, such as geographic position sensors 203, thus, while heading/speed sensors 225 as described herein are shown as separate from geographic position sensors 203, in some examples, machine heading/speed is derived from signals received from geographic position sensors 203 and subsequent processing. In other examples, heading/speed sensors 225 are separate sensors and do not utilize signals received from other sources.

[ 0065] Geographic position sensors 203 illustratively sense or detect the geographic position or location of agricultural harvester 100. Geographic position sensor 203 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. Geographic position sensors 203 can also include a real-time kinematic (RTK) component that is configured to enhance the precision of position data derived from the GNSS signal. Geographic position sensors 203 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors.

[ 0066] Propulsion controller output sensors 220 illustratively sense or detect an output from a propulsion controller of agricultural harvester 100 (e.g., propulsion controller 630 shown in FIG. 7). The propulsion controller illustratively generates control outputs (e.g., control signals) that control propulsion subsystem 250 of agricultural harvester 100. Thus, propulsion controller output sensors 220 illustratively sense or detect a speed characteristic value commanded by the propulsion controller. [ 0067 ] Yield sensors 223 illustratively sense or detect levels of yield of crop material (e.g., grain) harvested by agricultural harvester 100. Yield sensors 223 can include an imaging system (e.g., mono or stereo camera, an optical sensor, ultrasonic sensor, one or more mass sensors that sense a mass of crop material in clean grain tank 132, feed rate (or mass flow) sensors that detect a feed rate of grain to grain tank 132, etc. In some examples, yield sensors 223 utilize data received from other sources, such as fill level sensors 224. Thus, while yield sensors 223 as described herein are shown as separate from fill level sensors 224, in some examples, yield is derived from sensor data received from fill level sensors 224.

[ 0068 ] In-situ sensors 208 can also include various other types of sensors 228. For example, agricultural harvester 100 can include, among various other sensors, machine dynamics sensors, such as inertial measurement units, accelerometers, gyroscopes, magnetometers, inclinometers, as well as various other sensors. The machine dynamics sensors detect dynamics of the agricultural harvester 100, such as the pitch, roll, and yaw of the agricultural harvester.

[ 0069] Processing system 238 or processing system 338 processes the sensor data generated by in-situ sensors 208 to generate processed sensor data indicative of one or more characteristics. For example, processing system 238 or 338 generates processed sensor data indicative of characteristic values based on the sensor data (e.g., signals, images, etc.) generated by in-situ sensors 208, such as: speed characteristic values (e.g., travel speed values, acceleration values, and/or deceleration values) based on sensor data generated by propulsion controller output sensors 220; yield values based on sensor data generated by yield sensors 223; fill level values based on sensor data generated by fill level sensors 224; machine speed characteristic values (e.g., travel speed values, acceleration values, and/or deceleration values) based on sensor data generated by heading/speed sensors 225; machine heading values based on sensor data generated by heading/speed sensors 225; geographic position values based on sensor data generated by geographic position sensors 203; and various other characteristic values based on sensors data generated by various other in-situ sensors 228.

[ 0070 ] It will be understood that processing system 238 or 338 can be implemented by one or more processers or servers, such as processors or servers 201 or processors or servers 301, respectively. Additionally, processing system 238 and processing system 338 can utilize various sensor signal filtering, noise filtering, sensor signal categorization, aggregation, normalization, as well as various other processing functionality. Similarly, processing system 238 and processing system 338 can utilize various image processing such as, sequential image comparison, RGB, edge detection, black/white analysis, machine learning, neural networks, pixel testing, pixel clustering, shape detection, as well any number of other suitable image processing and data extraction functionality.

[ 0071 ] Fill level sensors 424 sense a characteristic indicative of a fill level of a grain bin of the respective receiving machine 400 (e.g., grain bin 172 or 192). Fill level sensors 424 can be an imaging system, such as a stereo or mono camera, that observes the grain bin of the respective receiving vehicle and detects a fill level of material within the grain bin. The images generated by the imaging system can be processed, such as by processing system 438 or processing system 338, using suitable imaging processing, to generate a value indicative of the fill level of the respective grain bin. The imaging system can be mounted to the exterior side of the roof of the operator compartment of the respective receiving machine (e.g., exterior side of the roof of operator compartment 167 or operator compartment 187), to the respective grain bin, or to other suitable locations on the respective receiving machine. Fill level sensors 424 can include one or more electromagnetic radiation (ER) sensors that transmit and/or receive electromagnetic radiation (ER) to detect presence of material. For instance, one or more ER sensors can be placed within the respective grain bin at a given distance from a perimeter of the grain bin or mounted to observe the interior of the grain bin to detect when the grain pile in the grain bin has reached a given height. Fill level sensors 424 can include one or more mass sensors (such as load cells, strain gauges, pressure sensors, etc.) disposed within the grain bin, between the grain bin and another component (e.g., an axle, a frame, etc.) of the receiving machine 400, and/or in the hitch assembly of the receiving vehicle 400. The mass sensors sense a mass of the material within the grain bin which can be used to derive a fill level of the grain bin.

[ 0072 ] In some examples, the fill level of the grain bin of the receiving machine is derived from sensors disposed on the agricultural harvester 100 or disposed on another receiving machine (e.g., in the case where another receiving machine is transferring material to the receiving machine 400). For instance, an imaging system, such as a stereo or mono camera can be mounted on the agricultural harvester 100 (e.g., on the chute 134) or another receiving machine (e.g., on the chute 171) and can be disposed to view the grain bin of the receiving machine during a material transfer operation. In another example, the agricultural harvester 100 or another receiving machine, or both, can include a mass flow sensor that senses a mass flow of material through the chute 134 or 171, respectively, which can be used to derive a fill level of the grain bin of the receiving machine 400. In another example, the agricultural harvester 100 or other receiving machine, or both, can include a sensor that senses a speed of the auger or blower of the material transfer subsystem 254 or material transfer subsystem 454, respectively, to derive flow rate of material to derive a fill level of the grain bin of the receiving machine 400. The sensor data generated by the sensors on the agricultural harvester 100 (or the processed sensor data) can be communicated to the remote computing systems 300 or to the receiving machine 400, or both. The sensor data generated by the sensors on the other receiving machine (or the processed sensor data) can be communicated to the remote computing systems 300 or to the receiving machine 400, or both.

[ 0073] Heading/speed sensors 425 detect a heading and speed characteristics (e.g., travel speed, acceleration, deceleration, etc.) at which the respective receiving machine 200 is traversing the worksite during the operation. This can include sensors that sense the movement (e.g., rotation) of ground-engaging elements (e.g., wheels or tracks 165, 170, 185, and/or 190), or the movement of components coupled to the ground engaging elements, or can utilize data received from other sources, such as geographic position sensors 403, thus, while heading/speed sensors 425 as described herein are shown as separate from geographic position sensor 403, in some examples, machine heading/speed is derived from sensor data received from geographic position sensors 403 and subsequent processing. In other examples, heading/speed sensors 425 are separate sensors and do not utilize signals received from other sources.

[ 0074 ] Geographic position sensor 403 illustratively senses or detects the geographic position or location of the respective receiving machine 400. Geographic position sensor 403 can include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. Geographic position sensor 403 can also include a realtime kinematic (RTK) component that is configured to enhance the precision of position data derived from the GNSS signal. Geographic position sensor 403 can include a dead reckoning system, a cellular triangulation system, or any of a variety of other geographic position sensors.

[ 0075] In-situ sensors 408 can also include various other types of sensors 428. For example, receiving machines 400 can include, among various other sensors, machine dynamics sensors, such as inertial measurement units, accelerometers, gyroscopes, magnetometers, inclinometers, as well as various other sensors. The machine dynamics sensors detect dynamics of the agricultural harvester 100, such as the pitch, roll, and yaw of the agricultural harvester. [ 0076] Processing system 438 or processing system 338 processes the sensor data generated by in-situ sensors 408 to generate processed sensor data indicative of one or more characteristics. For example, processing system 438 or 338 generates processed sensor data indicative of characteristic values based on the sensor data (e.g., signals, images, etc.) generated by in-situ sensors 408, such as: fill level values based on sensor data generated by fill level sensors 424, machine speed characteristic values (e.g., travel speed values, acceleration values, and/or deceleration values) based on sensor data generated by heading/speed sensors 425, machine heading values based on sensor data generated by heading/speed sensors 425, geographic position values based on sensor data generated by geographic position sensors 403; and various other characteristic values based on sensors data generated by various other in-situ sensors 428.

[ 0077 ] It will be understood that processing system 438 can be implemented by one or more processers or servers, such as processors or servers 401. Additionally, processing system 438 can utilize various sensor signal filtering, noise filtering, sensor signal categorization, aggregation, normalization, as well as various other processing functionality. Similarly, processing system 438 can utilize various image processing such as, sequential image comparison, RGB, edge detection, black/white analysis, machine learning, neural networks, pixel testing, pixel clustering, shape detection, as well any number of other suitable image processing and data extraction functionality. [ 0078 ] Control system 214 can include a variety of controllers 235, such as a communication system controller to control communication system 206, such as to send information to obtain information from various other items of system 500, a propulsion controller to control propulsion subsystem 250 to control a travel speed, acceleration, and/or deceleration of agricultural harvester 100, a path planning controller to control steering subsystem 252 to control the heading of agricultural harvester 100, and a material transfer controller to control material transfer subsystem 254, to initiate or end a material transfer operation, to control the position of chute 134 and/or spout 136, and/or to control the actuation (speed) of the auger or blower 133. Controllers 235 can also include an operator interface controller to control operator interface mechanisms 218 to provide indications, such as displays, alerts, notifications, as well as various other outputs. Some examples of the different types of controllers 235 will be shown in FIG. 6.

[ 0079] Control system 414 can include a variety of controllers 435, such as a communication system controller to control communication system 206, such as to send information to obtain information from various other items of system 500, a propulsion controller to control propulsion subsystem 450 to control a travel speed, acceleration, and/or deceleration of the respective receiving vehicle 400, a path planning controller to control steering subsystem 452 to control the heading of the respective receiving vehicle 400, and a material transfer controller to control material transfer subsystem 454, to initiate or end a material transfer operation, to control the actuation of door 191 or to control the position of chute 171 and/or spout 173, and/or to control the actuation (speed) of the auger or blower. Controllers 435 can also include an operator interface controller to control operator interface mechanisms 418 to provide indications, such as displays, alerts, notifications, as well as various other outputs. Some examples of the different types of controllers 435 will be shown in FIG. 6.

[ 0080 ] Communication system 206 is used to communicate between components of agricultural harvester 100 or with other items of agricultural system 500, such as remote computing systems 300 and/or receiving machines 400. Communication system 206 can include one or more of wired communication circuitry and wireless communication circuitry, as well as wired and wireless communication components. In some examples, communication system 206 can be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a controller area network (CAN), such as a CAN bus, a system for communication over a near field communication network, or a communication system configured to communicate over any of a variety of other networks. Communication system 206 can also include a system that facilitates downloads or transfers of information to and from a secure digital (SD) card or a universal serial bus (USB) card, or both. Communication system can utilize network 359. Network 359 can be any of a wide variety of different types of networks such as the Internet, a cellular network, a wide area network (WAN), a local area network (LAN), a controller area network (CAN), a near-field communication network, or any of a wide variety of other networks or communication systems.

[0081] Communication system 406 is used to communicate between components of the respective receiving machine 400 or with other items of agricultural system 500, such as remote computing systems 300, other receiving machines 400, and/or agricultural harvester 100. Communication system 406 can include one or more of wired communication circuitry and wireless communication circuitry, as well as wired and wireless communication components. In some examples, communication system 406 can be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a controller area network (CAN), such as a CAN bus, a system for communication over a near field communication network, or a communication system configured to communicate over any of a variety of other networks. Communication system 406 can also include a system that facilitates downloads or transfers of information to and from a secure digital (SD) card or a universal serial bus (USB) card, or both. Communication system 406 can utilize network 359.

[ 0082 ] Communication system 306 is used to communicate between components of the remote computing system 300 or with other items of agricultural system 500, such as remote receiving machines 400 and/or agricultural harvester 100. Communication system 306 can include one or more of wired communication circuitry and wireless communication circuitry, as well as wired and wireless communication components. In some examples, communication system 306 can be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a controller area network (CAN), such as a CAN bus, a system for communication over a near field communication network, or a communication system configured to communicate over any of a variety of other networks. Communication system 306 can also include a system that facilitates downloads or transfers of information to and from a secure digital (SD) card or a universal serial bus (USB) card, or both. In communicating with other items of agricultural system 500, communication system can utilize network 359.

[ 0083] FIG. 3 also shows remote users 366 interacting with agricultural harvester 100, receiving machines 400, and/or remote computing systems 300 through user interfaces mechanisms 364 over network 359. In some examples, user interface mechanisms 364 may include joysticks, levers, a steering wheel, linkages, pedals, buttons, wireless devices (e.g., mobile computing devices, etc.), dials, keypads, user actuatable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (where speech recognition and speech synthesis are provided), among a wide variety of other types of control devices. Where a touch sensitive display system is provided, the users 366 may interact with user interface mechanisms 364 using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Consequently, other types of user interface mechanisms 364 may be used and are within the scope of the present disclosure.

[ 0084 ] FIG. 3 also shows that one or more operators 360 may operate agricultural harvester 100 and receiving machines 400. The operators 360 interact with operator interface mechanisms 218 and 418. In some examples, operator interface mechanisms 218 and 418 may include joysticks, levers, a steering wheel, linkages, pedals, buttons, wireless devices (e.g., mobile computing devices, etc.), dials, keypads, user actuatable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (where speech recognition and speech synthesis are provided), among a wide variety of other types of control devices. Where a touch sensitive display system is provided, the operators 360 may interact with operator interface mechanisms 218 and 418 using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Consequently, other types of operator interface mechanisms 218 and 418 may be used and are within the scope of the present disclosure.

[0085] Remote computing systems 300 can be a wide variety of different types of systems, or combinations thereof. For example, remote computing systems 300 can be in a remote server environment. Further, remote computing systems 300 can be remote computing systems, such as mobile devices, a remote network, a farm manager system, a vendor system, or a wide variety of other remote systems. In one example, agricultural harvester 100 or receiving machines 400, or both, can be controlled remotely by remote computing systems 300 or by remote users 366, or both. As will be described below, in some examples, one or more of the components shown in FIG. 3 as being disposed on agricultural harvester 100 or on receiving machines 400 can be located elsewhere, such as at remote computing systems 300. Similarly, in some examples, one or more of the components shown in FIG. 3 as being disposed on remote computing systems 300 can be located elsewhere, such as on agricultural harvester 100 or receiving machines 400, or both.

[0086] FIG. 3 also shows that agricultural harvesting system 500 can obtain one or more information maps 358. As described herein, the information maps 358 include, for example, one or more of a vegetative index map, a historical yield map, a predictive yield map, a biomass map, a crop state map, a topographic map, a soil property map, and a seeding map. However, information maps 358 may also encompass other types of data, such as other types of data that were obtained prior to a harvesting operation or a map from a prior operation. In other examples, information maps 358 can be generated during a current operation, such a map generated by predictive map generator 312 based on a predictive model 311 generated by predictive model generator 310. [ 0087 ] Information maps 358 may be downloaded over network 359 and stored in a data store, such as data store 302, using a communication system, such as communication system 306, or in other ways.

[ 0088 ] Predictive model generator 310 generates a predictive model or relationship (collectively referred to hereinafter as “predictive model 311”) that is indicative of a relationship between the values sensed by the in-situ sensors 208 or derived from sensor data generated by in- situ sensors 208 and values mapped to the worksite by the information maps 358. As an illustrative example, if the information map 358 maps a vegetative index value to different locations in the worksite, and the in-situ sensor 208 is sensing a value indicative of yield, then model generator 310 generates a predictive yield model that models the relationship between vegetative index values and yield values. As another illustrative example, if the information map 358 maps a biomass value to different locations in the worksite, and the in-situ sensor 208 is sensing a value indicative of a speed characteristic, then model generator 310 generates a predictive speed model that models the relationship between biomass values and speed characteristic values.

[ 0089] In some examples, the predictive map generator 312 uses the predictive models 311 generated by predictive model generator 310 to generate functional predictive maps 263 that predict the value of a characteristic sensed by the in-situ sensors 208 at different locations in the worksite based upon one or more of the information maps 358. Keeping with the previous example, where the predictive model 311 is a predictive yield model that models a relationship between yield values sensed by in-situ sensors 208 and vegetative index values from a vegetative index map, then predictive map generator 312 generates a functional predictive yield map that predicts yield values at different locations at the field based on the mapped vegetative index values at those locations and the predictive yield model. Keeping with the previous example, where the predictive model 311 is a predictive speed model that models a relationship between speed characteristic values sensed by in-situ sensors 208 and biomass values from a biomass map, then predictive map generator 312 generates a functional predictive speed map that predicts speed characteristic values at different locations at the field based on the mapped biomass values at those locations and the predictive speed model.

[ 0090 ] In some examples, the type of values in the functional predictive map 263 may be the same as the in-situ data type sensed by the in-situ sensors 208. In some instances, the type of values in the functional predictive map 263 may have different units from the data sensed by the in-situ sensors 208. In some examples, the type of values in the functional predictive map 263 may be different from the data type sensed by the in-situ sensors 208 but have a relationship to the type of data type sensed by the in-situ sensors 208. For example, in some examples, the data type sensed by the in-situ sensors 208 may be indicative of the type of values in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 may be different than the data type in the information maps 358. In some instances, the type of data in the functional predictive map 263 may have different units from the data in the information maps 358. In some examples, the type of data in the functional predictive map 263 may be different from the data type in the information map 358 but has a relationship to the data type in the information map 358. For example, in some examples, the data type in the information maps 358 may be indicative of the type of data in the functional predictive map 263. In some examples, the type of data in the functional predictive map 263 is different than one of, or both of, the in-situ data type sensed by the in-situ sensors 208 and the data type in the information maps 358. In some examples, the type of data in the functional predictive map 263 is the same as one of, or both of, of the in-situ data type sensed by the in-situ sensors 208 and the data type in information maps 358. In some examples, the type of data in the functional predictive map 263 is the same as one of the in-situ data type sensed by the in-situ sensors 208 or the data type in the information maps 358, and different than the other.

[ 0091 ] Continuing with the preceding example, in which information map 358 is a vegetative index map and in-situ sensor 208 senses a value indicative of a yield value, predictive map generator 312 can use the vegetative index values in information map 358, and the predictive yield model 311 generated by predictive model generator 310, to generate a functional predictive map 263 that predicts the yield value at different locations in the worksite. Predictive map generator 312 thus outputs predictive map 264. Continuing with the preceding example, in which information map 358 is a biomass map and in-situ sensor 208 senses a value indicative of a speed characteristic, predictive map generator 312 can use the biomass values in information map 358, and the predictive speed model 311 generated by predictive model generator 310, to generate a functional predictive map 363 that predicts the speed characteristic value at different locations in the worksite.

[ 0092 ] As shown in FIG. 3, predictive map 264 predicts the value of a sensed characteristic (sensed by in-situ sensors 208), or a characteristic related to the sensed characteristic, at various locations across the worksite based upon one or more information values in one or more information maps 358 at those locations and using the predictive model(s) 311. For example, if predictive model generator 310 has generated a predictive model 311 indicative of a relationship between crop state values and speed characteristic values, then, given the crop state value (from a crop state map) at different locations across the worksite, predictive map generator 312 generates a predictive map 264 that predicts speed characteristic values at different locations across the worksite. The crop state value, obtained from the crop state map, at those locations and the relationship between crop state values and speed characteristic values, obtained from the predictive model 311, are used to generate the predictive map 264. This is merely one example.

[ 0093] Some variations in the data types that are mapped in the information maps 358, the data types sensed by in-situ sensors 208, and the data types predicted on the predictive map 264 will now be described.

[ 0094 ] In some examples, the data type in one or more information maps 358 is different from the data type sensed by in-situ sensors 208, yet the data type in the predictive map 264 is the same as the data type sensed by the in-situ sensors 208. For instance, the information map 358 may be a topographic map, and the variable sensed by the in-situ sensors 308 may be speed characteristic values. The predictive map 264 may then be a predictive speed map that maps predicted speed values to different geographic locations in the in the worksite.

[ 0095] Also, in some examples, the data type in the information map 358 is different from the data type sensed by in-situ sensors 208, and the data type in the predictive map 264 is different from both the data type in the prior information map 358 and the data type sensed by the in-situ sensors 108.

[ 0096] In some examples, the information map 358 is from a prior pass through the field during a prior operation and the data type is different from the data type sensed by in-situ sensors 208, yet the data type in the predictive map 264 is the same as the data type sensed by the in-situ sensors 208. For instance, the information map 358 may be a seeding map generated based on information from a previous planting operation on the worksite, and the variable sensed by the in- situ sensors 208 may be speed characteristic values. The predictive map 264 may then be a predictive speed map that maps predicted speed characteristic values to different geographic locations in the worksite. [ 0097 ] In some examples, the information map 358 is from a prior pass through the field during a prior operation and the data type is the same as the data type sensed by in-situ sensors 208, and the data type in the predictive map 264 is also the same as the data type sensed by the in- situ sensors 208. For instance, the information map 358 may be a historical yield map generated during a previous year, and the variable sensed by the in-situ sensors 208 may yield values. The predictive map 264 may then be a predictive yield map that maps predicted yield values to different geographic locations in the field. In such an example, the relative yield value differences in the georeferenced information map 358 from the prior year can be used by predictive model generator 310 to generate a predictive model that models a relationship between the relative yield value differences on the information map 358 and the yield values sensed by in-situ sensors 208 during the current operation. The predictive model is then used by predictive map generator 312 to generate a predictive yield map.

[ 0098 ] In another example, the prior information map 258 may be a map generated during a prior operation in the same year and the data type is different from the data type sensed by the in-situ sensors 208, and the data type in the predictive map 264 is also the same as the data type sensed by the in-situ sensors 208. For instance, the information map 358 may be a crop state map generated on the basis of sensor data generated during a spraying operation earlier in the same year, and the variable sensed by the in-situ sensors 208 during the current harvesting operation may be speed characteristic values. The predictive map 264 may then be a predictive speed map that maps predictive speed characteristic values to different geographic locations in the worksite. In such an example, the crop state values at time of the prior spraying operation are geo-referenced, recorded, and provided to remote computing systems 300 as an information map 358 of crop state values. In-situ sensors 208 during a current harvesting operation can detect speed characteristic values at geographic locations in the worksite and predictive model generator 310 may then build a predictive model that models a relationship between speed characteristic values at time of the current harvesting operation and crop state values at the time of the prior spraying operation. This is because the crop state values at the time of the prior spraying operation in the same year are likely to be the same as at the time of the current harvesting operation or otherwise may be more accurate than the crop state values for the worksite provided in other ways.

[ 0099] In some examples, predictive map 264 can be provided to the control zone generator 313. Control zone generator 313 groups adjacent portions of an area into one or more control zones based on data values of predictive map 264 that are associated with those adjacent portions. A control zone may include two or more contiguous portions of a worksite, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time to alter a setting of controllable subsystems may be inadequate to satisfactorily respond to changes in values contained in a map, such as predictive map 264. In that case, control zone generator 313 parses the map and identifies control zones that are of a defined size to accommodate the response time of the controllable subsystems. In another example, control zones may be sized to reduce wear from excessive actuator movement resulting from continuous adjustment. In some examples, there may be a different set of control zones for each controllable subsystem or for groups of controllable subsystems. The control zones may be added to the predictive map 264 to obtain predictive control zone map 265. Predictive control zone map 265 can thus be similar to predictive map 264 except that predictive control zone map 265 includes control zone information defining the control zones. Thus, a functional predictive map 263, as described herein, may or may not include control zones. Both predictive map 264 and predictive control zone map 265 are functional predictive maps 263. In one example, a functional predictive map 263 does not include control zones, such as predictive map 264. In another example, a functional predictive map 263 does include control zones, such as predictive control zone map 265. In some examples, multiple crops may be simultaneously present in a field if an intercrop production system is implemented. In that case, predictive map generator 312 and control zone generator 313 are able to identify the location and characteristics of the two or more crops and then generate predictive map 264 and predictive map with control zones 265 accordingly.

[00100 ] It will also be appreciated that control zone generator 313 can cluster values to generate control zones and the control zones can be added to predictive control zone map 265, or a separate map, showing only the control zones that are generated. In some examples, the control zones may be used for controlling or calibrating agricultural harvester 100 or receiving machines 400, or both. In other examples, the control zones may be presented to operator(s) 360 and used to control or calibrate agricultural harvester 100 or receiving machines 400, or both, and, in other examples, the control zones may be presented to an operator 360 or another user, such as a remote user 366, or stored for later use. [ 00101 ] Predictive map 264 or predictive control zone map 265 or both are provided to control system 214, which generates control signals based upon the predictive map 264 or predictive control zone map 265 or both to control agricultural harvester 100. Predictive map 264 or predictive control zone map 265 or both are provided to control system 414, which generates control signals based upon the predictive map 264 or predictive control zone map 265 or both to control the respective receiving machine 400.

[ 00102 ] While the illustrated example of FIG. 3 shows that various components of agricultural harvesting system 500 are located at specific locations, it will be understood that in other examples one or more of the components illustrated as being located at one location in FIG. 3 can be located at other locations. For example, one or more of predictive model generator 310, predictive model 311, predictive map generator 312, functional predictive maps 263 (e.g., 264 and 265), and control zone generator 313 can be located on agricultural harvester 100 or receiving machines 400, or both, but can communicate with other items of agricultural system 500 over network 359. Thus, the predictive models 311 and functional predictive maps 263 may be generated locally at agricultural harvester 100 or receiving machines 400 and communicated to other items in agricultural system 500. In other examples, agricultural harvester 100 or receiving machines 400 may access the predictive models 311 and functional predictive maps 263 at the remote locations without downloading the predictive models 311 and functional predictive maps 263. In other examples, one or more of control system 214 and control system 414, or components thereof, can be located at remote computing systems 300. In another example, remote computing systems 300 can include a control system or a control value generator that communicates control commands to one or more of agricultural harvester 100 and receiving machines 400 which are then used by the local control system of the agricultural harvester 100 and/or the receiving machines 400. These are merely some examples of the ways in which the agricultural system 500 can be distributed. Thus, it will be understood that the items in agricultural system 500 can be distributed in various ways, including ways that differ from the example shown in FIG. 3.

[ 00103 ] FIG. 4 is a block diagram of a portion of the agricultural harvesting system architecture 500 shown in FIG. 3. Particularly, FIG. 4 shows, among other things, examples of the predictive model generator 310 and the predictive map generator 312 in more detail. FIG. 4 also illustrates information flow among the various components shown. The predictive model generator 310 receives one or more information map(s) 358. In the example illustrated in FIG. 4, information maps 358 include one or more of a vegetative index map 330, a historical yield map 332, or any of a wide variety of other maps 347. Predictive model generator 310 also receives geographic location data 334, such as an indication of a geographic location, from geographic position sensor 203. In-situ sensors 208 illustratively include yield sensors 223 as well as a processing system 238. Processing system 238 processes sensor data generated from yield sensors 223 to generate processed sensor data 340 indicative of yield values. In some examples, other processing systems, such as processing system 338 or processing system 438 can process sensor data generated from in-situ sensors 208. Additionally, while the example shown in FIG. 4 illustrates the processing system 238 (or 338 or 438) as part of in-situ sensors 208, in other examples, processing system 238 (or 338 or 438) is separate from in-situ sensors 208 but in communication with in-situ sensors 208, such as the example shown in FIG. 3.

[ 00104 ] It will be understood that geographic location data 334 illustratively represents geographic locations on a field to which the values indicated by sensors 208 correspond. For example, where the in-situ sensor 208 detects a characteristic value, geographic location data 334 indicates the location of the field where that detected characteristic value corresponds. It will be understood that the geographic location of the agricultural harvester 100 at the time the characteristic value is detected by the in-situ sensor 208 may not be the location on the field to which the characteristic value corresponds. For instance, in the example of yield values detected by a yield sensor 223, the geographic location on the field to which the yield value corresponds may be behind the agricultural harvester 100 at the time the yield is detected. This is because an amount of time passes between when the crop (to which the yield corresponds) is encountered by the agricultural harvester 100 and when the crop is detected by the yield sensor 223. This latency can be taken into account when georeferencing the yield values detected by the yield sensor 223. [ 00105 ] Thus, the geographic location data 334, indicative of the geographic location on the field to which the characteristic value detected by the in-situ sensor 208 corresponds, can be derived from sensor data from geographic position sensor 203 along with heading data, travel speed data, machine latency data, as well as positional data of the sensor relative to the geographic position sensor 203 (or relative to another part of the agricultural harvester 100, such as the front of the header 102). This is merely one example. In any case, it will be understood that geographic location data 334 represents the geographic location on the field to which the characteristic values (e.g., yield values) correspond. [ 00106 ] As shown in FIG. 4, the example predictive model generator 310 includes one or more of a vegetative index value-to-yield value model generator 340, a historical yield value-to- yield value model generator 342, and an other characteristic value-to-yield value model generator 344. In other examples, the predictive model generator 310 may include additional, fewer, or different components than those shown in the example of FIG. 4. Consequently, in some examples, the predictive model generator 310 may include other items 345 as well, which may include other types of predictive model generators to generate other types of predictive models.

[00107 ] Vegetative index value-to-yield value model generator 340 identifies a relationship between yield value(s) detected in processed sensor data 340, at geographic location(s) to which the detected yield value(s) correspond, and vegetative index (VI) value(s) from the VI map 330 corresponding to the same location(s) in the worksite where the yield value(s) correspond. Based on this relationship established by vegetative index value-to-yield value model generator 1470, vegetative index value-to-yield value model generator 1470 generates a predictive yield model. The predictive yield model is used by yield map generator 363 to predict yield values at different locations in the worksite based upon the georeferenced VI value contained in the vegetative index map 330 at the different locations in the worksite. Thus, for a given location in the worksite, a yield value can be predicted at the given location based on the predictive yield model and the VI value, from the vegetative index map 330, at that given location.

[ 00108 ] Historical yield value-to-yield value model generator 342 identifies a relationship between yield value(s) detected in processed sensor data 340, at geographic location(s) to which the detected yield value(s) correspond, and historical yield value(s) from the historical yield map 332 corresponding to the same location(s) in the worksite where the yield value(s) correspond. Based on this relationship established by historical yield value-to-yield value model generator 342, historical yield value-to-yield value model generator 342 generates a predictive yield model. The predictive yield model is used by yield map generator 363 to predict yield values at different locations in the worksite based upon the georeferenced historical yield value contained in the historical yield map 332 at the different locations in the worksite. Thus, for a given location in the worksite, a yield value can be predicted at the given location based on the predictive yield model and the historical yield value, from the historical yield map 332, at that given location.

[ 00109 ] Other characteristic value-to-yield value model generator 344 identifies a relationship between yield value(s) detected in processed sensor data 340, at geographic location(s) to which the detected yield value(s) correspond, and other characteristic value(s) from an other map 347 corresponding to the same location(s) in the worksite where the yield value(s) correspond. Based on this relationship established by other characteristic value-to-yield value model generator 344, other characteristic value-to-yield value model generator 344 generates a predictive yield model. The predictive yield model is used by yield map generator 363 to predict yield values at different locations in the worksite based upon the georeferenced other characteristic value contained in the other map 347 at the different locations in the worksite. Thus, for a given location in the worksite, a yield value can be predicted at the given location based on the predictive yield model and the other characteristic value, from the other map 347, at that given location.

[ 00110 ] In light of the above, the predictive model generator 310 is operable to produce a plurality of predictive yield models, such as one or more of the predictive yield models generated by model generators 340, 342, 344, and 345. In another example, two or more of the predictive yield models described above may be combined into a single predictive yield model, such as a predictive yield model that predicts yield values based upon two or more of the vegetative index value, the historical yield value, and the other characteristic value at those different locations in the field. Any of these predictive yield models, or combinations thereof, are represented collectively by predictive yield model 353 in FIG. 4.

[00111 ] The predictive yield model 353 is provided to predictive map generator 312. In the example of FIG. 4, predictive map generator 312 includes a predictive yield map generator 363. In other examples, predictive map generator 312 may include additional or different map generators. Thus, in some examples, predictive map generator 312 may include other items 364 which may include other types of map generators to generate other types of maps.

[ 00112 ] Predictive yield map generator 363 receives one or more of the vegetative index map 330, the historical yield map 332, and other maps 347 along with the predictive yield model 353 which predicts yield values based upon one or more of vegetative index values, historical yield values, and other characteristic values and generates a functional predictive yield map 373 that predicts yield values at different locations in the worksite.

[ 00113 ] The functional predictive yield map 373 is a predictive map 264. The functional predictive yield map 373 predicts yield values at different locations in a worksite. The functional predictive yield map 373 may be provided to control zone generator 313, control system 214, and/or control system 414. Control zone generator 313 generates control zones and incorporates those control zones into the functional predictive yield map 373 to produce a predictive control zone map 265, that is, a functional predictive yield control zone map 383. One or both of functional predictive yield map 373 and functional predictive yield control zone map 383 may be provided to control system 214, which generates control signals to control one or more of the controllable subsystems 216 based upon the functional predictive yield map 373, the functional predictive yield control zone map 383, or both. One or both of functional predictive yield map 373 and functional predictive yield control zone map 383 may be provided to control system 414, which generates control signals to control one or more of the controllable subsystems 416 based upon the functional predictive yield map 373, the functional predictive yield control zone map 383, or both. One or both of functional predictive yield map 373 and functional predictive yield control zone map 383 may be presented to an operator 360, such as on an operator interface mechanism 218 or 418, or to a remote user 366, such as on a remote user interface 364, or both.

[00114 ] FIG. 5 is a block diagram of a portion of the agricultural system architecture 400 shown in FIG. 3. Particularly, FIG. 5 shows, among other things, examples of the predictive model generator 310 and the predictive map generator 312 in more detail. FIG. 5 also illustrates information flow among the various components shown. The predictive model generator 310 receives one or more information map(s) 358. In the example illustrated in FIG. 5, information maps 358 include one or more of a vegetative index map 330, a predictive yield map 333, a biomass map 335, a crop state map 337, a topographic map 339, a soil property map 341, a seeding map 343 or any of a wide variety of other maps 348. Predictive model generator 310 also receives a geographic location 1334, or an indication of a geographic location, from geographic position sensor 203. In-situ sensors 208 illustratively include machine heading/speed sensors 225 or a propulsion controller output sensor 220 that sense an output from a propulsion controller of agricultural harvester 100, or both, as well as a processing system 238. Processing system 238 processes sensor data generated from header/speed sensor 125 or from propulsion controller output sensor 220, or both, to generate processed sensor data 1340 indicative of machine speed characteristic values (e.g., travel speed values, acceleration values, deceleration values, etc.). In some examples, other processing systems, such as processing system 338 or processing system 438 can process sensor data generated from header/speed sensor 125 or from propulsion controller output sensor 220, or both. Additionally, while the example shown in FIG. 5 illustrates the processing system 238 (or 338 or 438) as part of in-situ sensors 208, in other examples, processing system 238 (or 338 or 438) is separate from in-situ sensors 208 but in communication with in-situ sensors 208, such as the example shown in FIG. 3.

[00115] It will be understood that geographic location 1334 illustratively represents geographic locations on a field to which the values indicated by sensors 208 correspond. For example, where the in-situ sensor 208 detects a speed characteristic value, geographic location 1334 indicates the location of the field where that detected speed characteristic value corresponds. As an illustrative example, the sensor data generated by sensors 208 can be timestamped and geographic position sensor data generated by geographic position sensor 203 can be timestamped. In this way, the geographic position detected at the same time as the speed characteristic can be correlated. This is merely one example.

[00116] As shown in FIG. 5, the example predictive model generator 310 includes one or more of a Vegetative Index (VI) value-to-speed characteristic value model generator 1342, a biomass value-to-speed characteristic value model generator 1344, a topographic value-to-speed characteristic value model generator 1345, a seeding characteristic value-to-speed characteristic value model generator 1346, a yield value-to-speed characteristic value model generator 1347, a crop state value-to-speed characteristic value model generator 1348, a soil property value-to-speed characteristic model generator 1349, and an other characteristic value-to-speed characteristic value model generator 1351. In other examples, the predictive model generator 310 may include additional, fewer, or different components than those shown in the example of FIG. 5. Consequently, in some examples, the predictive model generator 310 may include other items 1351 as well, which may include other types of predictive model generators to generate other types of predictive models.

[00117 ] VI value-to-speed characteristic value model generator 1342 identifies a relationship between machine speed characteristic value(s) detected in processed in-situ sensor data 1340, at geographic location(s) to which the detected speed characteristic value(s) correspond, and vegetative index (VI) value(s) from the VI map 330 corresponding to the same location(s) in the worksite where the machine speed characteristic value(s) correspond. Based on this relationship established by VI value-to-speed characteristic value model generator 1342, VI value- to-speed characteristic value model generator 1342 generates a predictive speed model. The predictive speed model is used by speed map generator 1352 to predict machine speed characteristic value(s) at different locations in the worksite based upon the georeferenced VI value contained in the VI map 432 at the different locations in the worksite. Thus, for a given location in the worksite, values of machine speed characteristics can be predicted at the given location based on the predictive speed model and the VI value, from the VI map 432, at that given location. [ 00118 ] Biomass value-to-speed characteristic value model generator 1344 identifies a relationship between machine speed characteristic value(s) detected in processed in-situ sensor data 1340, at geographic location(s) to which the detected speed characteristic value(s) correspond, and biomass value(s) from the biomass map 335 corresponding to the same location(s) in the worksite where the machine speed characteristic value(s) correspond. Based on this relationship established by biomass value-to-speed characteristic value model generator 1344, biomass value- to-speed characteristic value model generator 1344 generates a predictive speed model. The predictive speed model is used by speed map generator 1352 to predict machine speed characteristic value(s) at different locations in the worksite based upon the georeferenced biomass value contained in the biomass map 1335 at the different locations in the worksite. Thus, for a given location in the worksite, values of machine speed characteristics can be predicted at the given location based on the predictive speed model and the biomass value, from the biomass map 335, at that given location.

[ 00119 ] Topographic value-to-speed characteristic value model generator 1345 identifies a relationship between machine speed characteristic value(s) detected in processed in-situ sensor data 1340, at geographic location(s) to which the detected speed characteristic value(s) correspond, and topographic value(s) from the topographic map 339 corresponding to the same location(s) in the worksite where the machine speed characteristic value(s) correspond. Based on this relationship established by topographic value-to-speed characteristic value model generator 1345, topographic value-to-speed characteristic value model generator 1345 generates a predictive speed model. The predictive speed model is used by speed map generator 1352 to predict machine speed characteristic value(s) at different locations in the worksite based upon the georeferenced topographic value contained in the topographic map 339 at the different locations in the worksite. Thus, for a given location in the worksite, values of machine speed characteristics can be predicted at the given location based on the predictive speed model and the topographic value, from the topographic map 339, at that given location.

[ 00120 ] Seeding characteristic value-to-speed characteristic value model generator 1346 identifies a relationship between machine speed characteristic value(s) detected in processed in- situ sensor data 1340, at geographic location(s) to which the detected speed characteristic value(s) correspond, and seeding characteristic value(s) from the seeding map 443 corresponding to the same location(s) in the worksite where the machine speed characteristic value(s) correspond. Based on this relationship established by seeding characteristic value-to-speed characteristic value model generator 1346, seeding characteristic value-to-speed characteristic value model generator 1346 generates a predictive speed model. The predictive speed model is used by speed map generator 1352 to predict machine speed characteristic value(s) at different locations in the worksite based upon the georeferenced seeding characteristic value contained in the seeding map 343 at the different locations in the worksite. Thus, for a given location in the worksite, values of machine speed characteristics can be predicted at the given location based on the predictive speed model and the seeding characteristic value, from the seeding map 343, at that given location.

[ 00121 ] Yield value-to-speed characteristic value model generator 1347 identifies a relationship between machine speed characteristic value(s) detected in processed in-situ sensor data 1340, at geographic location(s) to which the detected speed characteristic value(s) correspond, and yield value(s) from the predictive yield map 333 corresponding to the same location(s) in the worksite where the machine speed characteristic value(s) correspond. Based on this relationship established by yield value-to-speed characteristic value model generator 1347, yield value-to- speed characteristic value model generator 1347 generates a predictive speed model. The predictive speed model is used by speed map generator 452 to predict machine speed characteristic value(s) at different locations in the worksite based upon the georeferenced yield value contained in the predictive yield map 333 at the different locations in the worksite. Thus, for a given location in the worksite, values of machine speed characteristics can be predicted at the given location based on the predictive speed model and the yield value, from the predictive yield map 333, at that given location.

[ 00122 ] Crop state value-to-speed characteristic value model generator 1348 identifies a relationship between machine speed characteristic value(s) detected in processed in-situ sensor data 1340, at geographic location(s) to which the detected speed characteristic value(s) correspond, and crop state value(s) from the crop state map 337 corresponding to the same location(s) in the worksite where the machine speed characteristic value(s) correspond. Based on this relationship established by crop state value-to-speed characteristic value model generator 1348, crop state value-to-speed characteristic value model generator 1348 generates a predictive speed model. The predictive speed model is used by speed map generator 1352 to predict machine speed characteristic value(s) at different locations in the worksite based upon the georeferenced crop state value contained in the crop state map 437 at the different locations in the worksite. Thus, for a given location in the worksite, values of machine speed characteristics can be predicted at the given location based on the predictive speed model and the crop state value, from the crop state map 337, at that given location.

[ 00123 ] Soil property value-to-speed characteristic value model generator 1349 identifies a relationship between machine speed characteristic value(s) detected in processed in-situ sensor data 1340, at geographic location(s) to which the detected speed characteristic value(s) correspond, and soil property value(s) from the soil property map 341 corresponding to the same location(s) in the worksite where the machine speed characteristic value(s) correspond. Based on this relationship established by soil property value-to-speed characteristic value model generator 1349, soil property value-to-speed characteristic value model generator 1349 generates a predictive speed model. The predictive speed model is used by speed map generator 1352 to predict machine speed characteristic value(s) at different locations in the worksite based upon the georeferenced soil property value contained in the soil property map 341 at the different locations in the worksite. Thus, for a given location in the worksite, values of machine speed characteristics can be predicted at the given location based on the predictive speed model and the soil property value, from the soil property map 341, at that given location.

[ 00124 ] Other characteristic value-to-speed characteristic value model generator 1351 identifies a relationship between machine speed characteristic value(s) detected in processed in- situ sensor data 1340, at geographic location(s) to which the detected speed characteristic value(s) correspond, and other characteristic value(s) from an other map 348 corresponding to the same location(s) in the worksite where the machine speed characteristic value(s) correspond. Based on this relationship established by other characteristic value-to-speed characteristic value model generator 1351, other characteristic value-to-speed characteristic value model generator 1351 generates a predictive speed model. The predictive speed model is used by speed map generator 1352 to predict machine speed characteristic value(s) at different locations in the worksite based upon the georeferenced other characteristic value contained in the other map 348 at the different locations in the worksite. Thus, for a given location in the worksite, values of machine speed characteristics can be predicted at the given location based on the predictive speed model and the other characteristic value, from the other map 348, at that given location.

[ 00125 ] In light of the above, the predictive model generator 310 is operable to produce a plurality of predictive speed models, such as one or more of the predictive speed models generated by model generators 1342, 1344, 1345, 1346, 1347, 1348, 1349, 1351, and 1353. In another example, two or more of the predictive models described above may be combined into a single predictive speed model, such as a predictive speed model that predicts machine speed characteristic values based upon two or more of the VI values, the biomass values, the topographic values, the seeding characteristic values, the yield values, the crop state values, the soil property values, and the other map characteristic values at those different locations in the field. Any of these speed models, or combinations thereof, are represented collectively by predictive speed model 1350 in FIG. 5.

[ 00126 ] The predictive speed model 1350 is provided to predictive map generator 312. In the example of FIG. 5, predictive map generator 312 includes a predictive speed map generator 1352. In other examples, predictive map generator 312 may include additional or different map generators. Thus, in some examples, predictive map generator 312 may include other items 1354 which may include other types of map generators to generate other types of maps.

[ 00127 ] Predictive speed map generator 1352 receives one or more of the VI map 330, the biomass map 335, the topographic map 339, the seeding map 343, the yield map 333, the crop state map 337, the soil property map 341, and other maps 348 along with the predictive speed model 1350 which predicts machine speed characteristic values based upon one or more VI values, biomass values, topographic values, seeding characteristic values, yield values, crop state values, soil property values, and other characteristic values and generates a functional predictive speed map 1360 that predicts machine speed characteristic values at different locations in the worksite. [ 00128 ] The functional predictive speed map 1360 is a predictive map 264. The functional predictive speed map 1360 predicts machine speed characteristic values at different locations in a worksite. The functional predictive speed map 1360 may be provided to control zone generator 313, control system 214, and/or control system 414. Control zone generator 313 generates control zones and incorporates those control zones into the functional predictive speed map 1360 to produce a predictive control zone map 265, that is, a functional predictive speed control zone map 1361. One or both of functional predictive speed map 1360 and functional predictive speed control zone map 1361 may be provided to control system 214, which generates control signals to control one or more of the controllable subsystems 216 based upon the functional predictive speed map 1360, the functional predictive speed control zone map 1361, or both. One or both of functional predictive speed map 1360 and functional predictive speed control zone map 1361 may be provided to control system 414, which generates control signals to control one or more of the controllable subsystems 416 based upon the functional predictive speed map 1360, the functional predictive speed control zone map 1361, or both. One or both of functional predictive speed map 1360 and functional predictive speed control zone map 1361 may be presented to an operator 360, such as on an operator interface mechanism 218 or 418, or to a remote user 366, such as on a remote user interface 364, or both.

[00129] As can be seen from FIGS. 4-5, predictive map generator 310 is operable to produce a plurality of predictive models 311, such as predictive yield model 353 or predictive speed model 1350, or both. Additionally, predictive map generator 312 is operable to produce a plurality of functional predictive maps 263, such as one or more functional predictive yield map 373, functional predictive yield control zone map 383, functional predictive speed map 1360, and functional predictive speed control zone map 1361. It will be understood that one or more of functional predictive yield map 373, functional predictive yield control zone map 383, functional predictive speed map 1360, and functional predictive speed control zone map 1361, can be provided to control system 214 or control system 414, or both.

[00130 ] FIGS. 6A-6B (collectively referred to herein as FIG. 6) show a flow diagram illustrating one example of the operation of agricultural harvesting system architecture 500 in generating a predictive model and a predictive map.

[00131 ] At block 502, agricultural system 500 receives one or more information maps 358. Examples of information maps 358 or receiving information maps 358 are discussed with respect to blocks 504, 505, 507, and 508. As discussed above, information maps 358 map values of a variable, corresponding to a characteristic, to different locations in the worksite, as indicated at block 505. As indicated at block 504, receiving the information maps 358 may involve selecting one or more of a plurality of possible information maps 358 that are available. For instance, one information map 358 may be a VI map, such as VI map 330. Another information map 358 may be a topographic map, such as topographic map 331. Another information map 358 may be a historical yield map, such as historical yield map 332. Another information map 358 may be a predictive yield map, such as predictive yield map 333. Another information map 358 may be a biomass map, such as biomass map 335. Another information map 358 may be a crop state map, such as crop state map 337. Another information map 358 may be a topographic map, such as topographic map 339. Another information map 358 may be a soil property map, such as soil property map 341. Another information map 358 may be a seeding map, such as seeding map 343. Information maps 358 may include various other types of characteristic maps, such as other maps 347 or other maps 348, or both. The process by which one or more information maps 358 are selected can be manual, semi-automated, or automated. The information maps 358 can be based on data collected prior to a current operation, as indicated by block 506. For instance, the data may be collected based on aerial images taken during a previous year, or earlier in the current season, or at other times. The data may be based on data detected in ways other than using aerial images. For instance, the data may be collected during a previous operation on the worksite, such an operation during a previous year, or a previous operation earlier in the current season, or at other times. The machines performing those previous operations may be outfitted with one or more sensors that generate sensor data indicative of one or more characteristics. For example, the sensed yield during a previous harvesting operation at the worksite may be used as data to generate a historical yield map. In other examples, and as described above, the information maps 358 may be predictive maps having predictive values, such as a predictive yield map having predictive yield values, such as predictive yield map 333. The predictive information map 358 can be generated during a current operation by predictive map generator 312 based on a model generated by predictive model generator 310, as indicated by block 506. For instance, in one example, predictive yield map 333 can be functional predictive yield map 373 or functional predictive yield control zone map 383, or the predictive yield map 333 can be generated in a similar way as functional predictive yield map 373 or functional predictive yield control zone map 383. The predictive information map 358 can be predicted in other ways (before or during the current operation), such as based on other measured values (e.g., predictive yield or predictive biomass based on measured vegetative index values). The data for the information maps 358 can be obtained by predictive model generator 310 and predictive map generator 312 using communication system 306 and stored in data store 304. The data for the information maps 358 can be obtained by harvesting system 500 using a communication system in other ways as well, and this is indicated by block 507 in the flow diagram of FIG. 6. [ 00132 ] As agricultural harvester 100 is operating, in-situ sensors 208 generate sensor data indicative of one or more in-situ data values indicative of one or more characteristics, as indicated by block 508. For example, yield sensors 223 generate sensor data indicative of one or more in- situ yield values as indicated by block 509. Heading/speed sensors 225 generate sensor data indicative of one or more in-situ speed characteristic values as indicated by block 510. Propulsion controller output sensors 220 generate sensor data indicative of one or more in-sit speed characteristic values as indicated by block 511. In some examples, data from in-situ sensors 208 is georeferenced using position data from geographic position sensor 203 as well as, in some examples, one or more of heading data, travel speed data, machine latency data, and positional information of the in-situ sensors 208.

[ 00133 ] At block 513, predictive model generator 310 controls one or more of model generators to generate one or more models that model the relationship between mapped values and in-situ characteristic values sensed by in-situ sensors 208. For instance, predictive model generator 310 controls one or more of the model generators 344, 342, 344, and 345 to generate a predictive yield model that models the relationship between the mapped values, such as one or more of the VI values, the historical yield values and the other characteristic values contained in the respective information map and the in-situ yield values sensed by yield sensors 223. Predictive model generator 310 thus generates a predictive yield model 353 as indicated by block 514. Additionally, or alternatively, predictive model generator 310 controls one or more of the model generators 1342, 1344, 1345, 1346, 1347, 1348, 1349, 1351, and 1353 to generate a predictive speed model that models the relationship between the mapped values, such as one or more the VI values, the predictive yield values, the biomass values, the crop state values, the topographic characteristic values, the soil property values, the seeding characteristic values, and the other characteristic values contained in the respective information map and the in-situ speed characteristic values sensed by heading/speed sensors 225 or propulsion controller output sensors 220, or both. Predictive model generator 310 thus generates a predictive speed model 1350 as indicated by block 515.

[ 00134 ] The relationship(s) or model(s) generated by predictive model generator 310 are provided to predictive map generator 312.

[ 00135 ] Predictive map generator 312, at block 518, predictive map generator 312 controls one or more predictive map generators to generate one or more functional predictive maps. For instance, predictive map generator 312 controls predictive yield map generator 363 to generate a functional predictive yield map 373 that predicts yield values (or sensor value(s) indictive of yield values) at different geographic locations in a worksite at which agricultural harvester 100 is operating using the predictive yield model 353 and one or more of the VI map 330, the historical yield map 332, and one or more other maps 347. Generating a functional predictive yield map 373 is indicated by block 519.

[ 00136 ] Additionally, or alternatively, at block 518, predictive map generator 312 controls predictive speed map generator 1352 to generate a functional predictive speed map 1360 that predicts speed characteristic values (or sensor value(s) indictive of speed characteristic values) at different geographic locations in a worksite at which agricultural harvester 100 is operating using the predictive speed model 1350 and one or more of the VI map 330, the predictive yield map 333, the biomass map 335, the crop state map 337, the topographic map 339, the soil property map 341, the seeding map 343, and one or more other maps 348. Generating a functional predictive speed map 1360 is indicated by block 520.

[ 00137 ] It should be noted that, in some examples, the functional predictive yield map 373 may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive yield map 373 that provides two or more of a map layer that provides predictive yield values based on VI values from VI map 330, a map layer that provides predictive yield values based on historical yield values from historical yield map 332, and a map layer that provides predictive yield values based on other characteristic values from an other map 347. In some examples, the functional predictive yield map 373 may include a map layer that provides predictive yield values based on two or more of VI values from VI map 330, historical yield values from historical yield map 332, and other characteristic values from an other map 347. [ 00138 ] It should be noted that, in some examples, the functional predictive speed map 1360 may include two or more different map layers. Each map layer may represent a different data type, for instance, a functional predictive speed map 1360 that provides two or more of a map layer that provides predictive speed characteristic values based on VI values from VI map 330, a map layer that provides predictive speed characteristic values based on predictive yield values from predictive yield map 333, a map layer that provides predictive speed characteristic values based on biomass values from biomass map 335, a map layer that provides predictive speed characteristic values based on crop state values from crop state map 337, a map layer that provides predictive speed characteristic values based on topographic characteristic values from topographic map 339, a map layer that provides predictive speed characteristic values based on soil property values from soil property map 341, a map layer that provides predictive speed characteristic values based on seeding characteristic values from seeding map 343, and a map layer that provides predictive speed characteristic values based on other characteristic values from an other map 348. In some examples, the functional predictive speed map 1360 may include a map layer that provides predictive speed characteristic values based on two or more of VI values from VI map 330, predictive yield values from predictive yield map 333, biomass values from biomass map 335, crop state values from crop state map 337, topographic characteristic values from topographic map 339, soil property values from soil property map 341, seeding characteristic values from seeding map 343, and other characteristic values from an other map 348.

[ 00139 ] At block 523, predictive map generator 312 configures the functional predictive map(s) (e.g., one or more of 373 and 1360) so that the functional predictive map(s) are actionable (or consumable) by control system 214 or 414, or both. Predictive map generator 312 can provide one or more of the functional maps 373 and 1360 to the control system 214, to the control system 414, and/or to control zone generator 313. Some examples of the different ways in which the functional predictive map(s) 373 and 1360 can be configured or output are described with respect to blocks 523, 524, 525, and 526. For instance, predictive map generator 312 configures one or more of the functional predictive maps 373 and 1360 so that the one or more functional predictive maps 373 and 1360 include values that can be read by control system 214 or 414, or both, and used as the basis for generating control signals for one or more of the different controllable subsystems 216 of agricultural harvester 100 or controllable subsystems 416 of a respective receiving machine 400, as indicated by block 523.

[ 00140 ] Control zone generator 313 , at block 524, can divide the functional predictive yield map 373 into control zones based on the values on the functional predictive yield map 373 to generate functional predictive yield control zone map 383. Additionally, or alternatively, control zone generator 313, at block 524, can divide the functional predictive speed map 1360 into control zones based on the values on the functional predictive speed map 1360 to generate functional predictive speed control zone map 1361.

[ 00141 ] Contiguously-geolocated values that are within a threshold value of one another can be grouped into a control zone. The threshold value can be a default threshold value, or the threshold value can be set based on an operator input, based on an input from an automated system, or based on other criteria. A size of the zones may be based on a responsiveness of the control system, the controllable subsystems, based on wear considerations, or on other criteria.

[ 00142 ] At block 525, predictive map generator 312 configures one or more of the functional predictive maps 373 and 1360 for presentation to an operator or other user. Alternatively, or additionally, at block 525, control zone generator 313 can configure one or more of the functional predictive control zone maps 383 and 1361 for presentation to an operator or other user. When presented to an operator or other user, the presentation of the one or more functional predictive map(s) 373 and 1360 or of the one or more functional predictive control zone map(s) 383 and 1361, or both, may contain one or more of the predictive values on the functional predictive map(s) correlated to geographic location, the control zones of functional predictive control zone map(s) correlated to geographic location, and settings values or control parameters that are used based on the predicted values on functional predictive map(s) or control zones on functional predictive control zone map(s). The presentation can, in another example, include more abstracted information or more detailed information. The presentation can also include a confidence level that indicates an accuracy with which the predictive values on the one or more functional predictive map(s) 373 and 1360 or the control zones on the one or more predictive control zone map(s) 383 and 1361, or both, conform to measured values that may be measured by sensors on agricultural harvester 100 as agricultural harvester 100 operates at the worksite. Further where information is presented to more than one location, an authentication and authorization system can be provided to implement authentication and authorization processes. For instance, there may be a hierarchy of individuals that are authorized to view and change maps and other presented information. By way of example, an on-board display device may show the maps in near real time locally on the machine, or the maps may also be generated at one or more remote locations, or both. In some examples, each physical display device at each location may be associated with a person or a user permission level. The user permission level may be used to determine which display elements are visible on the physical display device and which values the corresponding person may change. As an example, a local operator of agricultural harvester 100 or a receiving machine 400, or both, may be unable to see the information corresponding to the one or more functional predictive maps 373 and 1360 or make any changes to machine operation. A supervisor, such as a supervisor at a remote location, however, may be able to see the one or more functional predictive maps 373 and 1360 on the display but be prevented from making any changes. A manager, who may be at a separate remote location, may be able to see all of the elements on the one or more functional predictive map(s) 373 and 1360 and also be able to change the functional predictive map(s). In some instances, the one or more functional predictive maps 373 and 1360 accessible and changeable by a manager located remotely may be used in machine control. This is one example of an authorization hierarchy that may be implemented. The one or more functional predictive maps 373 and 1360 or the one or more functional predictive control zone maps 383 and 1361, or both, can be configured in other ways as well, as indicated by block 526.

[ 00143 ] At block 527, when agricultural harvester 100 is being controlled, input from geographic position sensor 203 and other in-situ sensors 208 are received by the control system 214. Particularly, at block 528, control system 214 detects an input from the geographic position sensor 203 identifying a geographic location of agricultural harvester 100. Block 529 represents receipt by the control system 214 of sensor inputs indicative of trajectory or heading of agricultural harvester 100, and block 530 represents receipt by the control system 214 of a speed of agricultural harvester 100. Block 531 represents receipt by the control system 214 of other information from various in-situ sensors 208.

[ 00144 ] At block 527, when a receiving machine 400 is being controlled, input from geographic position sensor 403 and other in-situ sensors 408 are received by the control system 414. Particularly, at block 528, control system 414 detects an input from the geographic position sensor 403 identifying a geographic location of receiving machine 400. Block 529 represents receipt by the control system 414 of sensor inputs indicative of trajectory or heading of receiving machine 400, and block 530 represents receipt by the control system 414 of a speed of receiving machine 200. Block 531 represents receipt by the control system 414 of other information from various in-situ sensors 408.

[ 00145 ] At block 532, where agricultural harvester 100 is being controlled, control system 214 generates control signals to control the controllable subsystems 216 based on the one or more functional predictive maps 373 and 1360 or the one or more functional predictive control zone maps 383 and 1361, or both, and the input from the geographic position sensor 208 and any other in-situ sensors 208. At block 534, control system 214 applies the control signals to the controllable subsystems 216. It will be appreciated that the particular control signals that are generated, and the particular controllable subsystems 216 that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and the controllable subsystems 216 that are controlled may be based on the type of the one or more functional predictive maps 373 and 1360 or the one or more functional predictive control zone maps 383 and 1361, or both, that is being used. Similarly, the control signals that are generated and the controllable subsystems 216 that are controlled and the timing of the control signals can be based on various latencies of agricultural harvester 100 and the responsiveness of the controllable subsystems 216.

[ 00146 ] At block 532, where a receiving machine 400 is being controlled, control system 414 generates control signals to control the controllable subsystems 416 based on the one or more functional predictive maps 373 and 1360 or the one or more functional predictive control zone maps 383 and 1361, or both, and the input from the geographic position sensor 403 and any other in-situ sensors 408. At block 534, control system 414 applies the control signals to the controllable subsystems 416. It will be appreciated that the particular control signals that are generated, and the particular controllable subsystems 416 that are controlled, may vary based upon one or more different things. For example, the control signals that are generated and the controllable subsystems 416 that are controlled may be based on the type of the one or more functional predictive maps 373 and 1360 or the one or more functional predictive control zone maps 383 and 1361, or both, that is being used. Similarly, the control signals that are generated and the controllable subsystems 416 that are controlled and the timing of the control signals can be based on various latencies of the receiving machine 400 and the responsiveness of the controllable subsystems 416.

[ 00147 ] At block 536, a determination is made as to whether the operation has been completed. If the operation is not completed, the processing advances to block 538 where in-situ sensor data from geographic position sensor 203 and in-situ sensors 208 (and perhaps other sensors) and from geographic position sensor 403 and in-situ sensors 408 (and perhaps other sensors) continue to be read.

[ 00148 ] In some examples, at block 540, agricultural harvesting system 500 can also detect learning trigger criteria to perform machine learning on one or more of the one or more functional predictive maps 373 and 1360, the one or more functional predictive control zone maps 383 and 1361, the one or more predictive models 353 and 1350, the one or more zones generated by control zone generator 313, the one or more control algorithms implemented by the controllers in the control system 214 or the controllers in the control system 414, or both, and other triggered learning.

[ 00149 ] The learning trigger criteria can include any of a wide variety of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks 542, 544, 546, 548, and 549. For instance, in some examples, triggered learning can involve recreation of a relationship used to generate a predictive model when a threshold amount of in-situ sensor data are obtained from in-situ sensors 208. In such examples, receipt of an amount of in-situ sensor data from the in-situ sensors 208 that exceeds a threshold trigger or causes the predictive model generator 310 to generate a new predictive model that is used by predictive map generator 312. Thus, as agricultural harvester 100 continues an operation, receipt of the threshold amount of in- situ sensor data from the in-situ sensors 208 triggers the creation of a new relationship represented by one or more new predictive models 353 and 1350 generated by predictive model generator 310. Further, one or more new functional predictive maps 373 and 1360, one or more new functional predictive control zone maps 383 and 1361, or both, can be generated using the respective one or more new predictive modes 353 and 1350. Block 542 represents detecting a threshold amount of in-situ sensor data used to trigger creation of one or more new predictive models.

[ 00150 ] In other examples, the learning trigger criteria may be based on how much the in- situ sensor data from the in-situ sensors 208 are changing, such as over time or compared to previous values. For example, if variations within the in-situ sensor data (or the relationship between the in-situ sensor data and the information in the one or more information maps 358) are within a selected range or is less than a defined amount, or below a threshold value, then one or more new predictive models are not generated by the predictive model generator 310. As a result, the predictive map generator 312 does not generate one or more new functional predictive maps, one or more new functional predictive control zone maps, or both. However, if variations within the in-situ sensor data are outside of the selected range, are greater than the defined amount, or are above the threshold value, for example, then the predictive model generator 310 generates one or more new predictive models 353 and 1350 using all or a portion of the newly received in-situ sensor data that the predictive map generator 312 uses to generate one or more new functional predictive maps 373 and 1360 which can be provided to control zone generator 313 for the creation of one or more new functional predictive control zone maps 383 and 1361. At block 544, variations in the in-situ sensor data, such as a magnitude of an amount by which the data exceeds the selected range or a magnitude of the variation of the relationship between the in-situ sensor data and the information in the one or more information maps, can be used as a trigger to cause generation of one or more of one or more new predictive models 353 and 1350, one or more new functional predictive maps 373 and 1360, and one or more new functional predictive control zone maps 383 and 1361. Keeping with the examples described above, the threshold, the range, and the defined amount can be set to default values; set by an operator or user interaction through a user interface; set by an automated system; or set in other ways.

[00151 ] Other learning trigger criteria can also be used. For instance, if predictive model generator 310 switches to a different information map (different from the originally selected information map), then switching to the different information map may trigger re-learning by predictive model generator 310, predictive map generator 312, control zone generator 313, control system 214, control system 414, or other items. In another example, transitioning of agricultural harvester 100 to a different topography, a different control zone, a different region of the worksite, a different area with different grouped characteristics (such as a different crop genotype area) may be used as learning trigger criteria as well.

[00152 ] In some instances, an operator 360 or user 366 can also edit the functional predictive map(s) or functional predictive control zone map(s), or both. The edits can change a value on the functional predictive map(s), change a size, shape, position, or existence of a control zone on functional predictive control zone map(s), or both. Block 546 shows that edited information can be used as learning trigger criteria.

[00153] In some instances, it may also be that an operator 360 or user 366 observes that automated control of a controllable subsystem, is not what the operator or user desires. In such instances, the operator 360 or user 366 may provide a manual adjustment to the controllable subsystem reflecting that the operator 360 or user 366 desires the controllable subsystem to operate in a different way than is being commanded by control system. Thus, manual alteration of a setting by the operator or user can cause one or more of predictive model generator 310 to relearn one or more predictive models, predictive map generator 312 to generate one or more new functional predictive maps, control zone generator 313 to generate one or more new control zones on one or more functional predictive maps, and a control system to relearn a control algorithm or to perform machine learning on one or more of the controllers in the control system based upon the adjustment by the operator or user, as shown in block 548. Block 549 represents the use of other triggered learning criteria.

[ 00154 ] In other examples, relearning may be performed periodically or intermittently based, for example, upon a selected time interval such as a discrete time interval or a variable time interval, as indicated by block 550.

[ 00155 ] If relearning is triggered, whether based upon learning trigger criteria or based upon passage of a time interval, as indicated by block 550, then one or more of the predictive model generator 310, predictive map generator 312, control zone generator 313, control system 214 and control system 414 performs machine learning to generate new predictive model(s), new functional predictive map(s), new control zone(s), and new control algorithm(s), respectively, based upon the learning trigger criteria. The new predictive model(s), the new functional predictive map(s), the new control zone(s), and the new control algorithm(s) are generated using any additional data that has been collected since the last learning operation was performed. Performing relearning is indicated by block 552.

[ 00156 ] If the operation has been completed, operation moves from block 552 to block 554 where one or more of the functional predictive map(s), functional predictive control zone map(s), the predictive model(s), the control zone(s), and the control algorithm(s) are stored. The functional predictive map(s), the functional predictive control zone map(s), the predictive model(s), the control zone(s), and the control algorithm(s) may be stored locally on a data store of a machine (e.g., data store 204 of agricultural harvester 100 or data store 404 of a receiving machine 400) or stored remotely (e.g., stored at data store 304 of remote computing systems 300), for later use.

[ 00157 ] If the operation has not been completed, operation returns to block 518 such that the new functional predictive map(s), the new functional predictive control zone map(s), the new control zone(s), and/or the new control algorithm(s) can be used to control the agricultural harvester 100 or the receiving machine(s) 400, or both.

[ 00158 ] FIG. 7 is a block diagram of a portion of agricultural harvesting system 500 shown in FIG. 3, in more detail. Particularly, FIG. 7 shows examples of the harvesting logistics module 315 in more detail. FIG. 7 also illustrates information flow among the various components shown. [ 00159 ] As illustrated in FIG. 7, harvesting logistics module 315 receives one or more predictive maps 601, one or more information maps 358, agricultural harvester sensor data 604, agricultural harvester data 606, material transfer subsystem data 607, receiving machine sensor data 608, receiving machine data 610, harvester route data 612, operator data 614, and various other data 616. Predictive maps 601 can include functional predictive maps 263 and other maps 602. Functional predictive maps 263 can include one or more predictive maps 264, such as one or more of functional predictive yield map 373 and functional predictive speed map 1360. Functional predictive maps 263 can include one or more predictive maps with control zones 265, such as one or more of functional predictive yield control zone map 383 and functional predictive speed control zone map 1361. Other maps 602 can include various other maps 602, such as other predictive maps that are generated differently than functional predictive control maps 263, such as other types of predictive yield maps or other types of predictive speed maps, or both.

[ 00160 ] Agricultural harvester sensor data 604 includes data generated by or derived from in-situ sensors 208 of agricultural harvester 100. Agricultural harvester data 606 includes various data relative to the particular agricultural harvester 100. For example, agricultural harvester data 606 can include dimensional information of the agricultural harvester 100, such as the length and width of the agricultural harvester 100, the width (or number of row units) of header 102, the dimensions (or fill capacity) of grain tank 132, the empty weight of the agricultural harvester 100, and dimensional information with regard to the material transfer subsystem 254. Agricultural harvester data 606 can include data relative to the capabilities of the agricultural harvester 100, such as the power output, the operating parameter ranges, the power requirements of various systems, as well as various other data.

[ 00161 ] Receiving machine sensor data 608 includes data generated by or derived from in- situ sensors 408 of receiving machine(s) 400. Receiving machine data 610 includes various data relative to the particular receiving machines 400. For example, receiving machine data 610 can include dimensional information of the particular receiving machines 400, such as the length and width of the receiving machines 400, the dimensions (or fill capacity) of grain bins 172/192, the empty weight of the receiving machines 400, and dimensional information with regard to the material transfer subsystems 454. Receiving machine data 610 can include data relative to the capabilities of the receiving machines 400, such as the power output, the operating parameter ranges, the power requirements of various systems, as well as various other data.

[ 00162 ] Material transfer subsystem data 607 includes operation information with regard to the material transfer subsystem(s) 254 and/or 454, such as a rate (or range of rates) at which material transfer subsystem(s) 254 and/or 454, can convey material. In some examples, the rate at which the material transfer subsystem(s) 254 and/or 454 can also be derived from sensors on agricultural harvester 100 or receiving machines 400 such as from a sensor that senses a speed of rotation of the respective auger or blower, a flow sensor that senses a flow of harvested material through the material transfer subsystem, or from fill level sensors 224 and/or 424 which can indicate the rate at which the respective machine is being filled.

[ 00163 ] Harvester route data 612 includes data indicative of a route (such as a prescribed or commanded route) being traveled by agricultural harvester 100 at the worksite. In some examples, route data 612 can include or be derived from heading data generated by heading/speed sensors 225.

[ 00164 ] Operator data 614 includes data relative to the operators 360 that are operating the various machines in the operation. For example, operator data 614 can include operator identifying information, as well as operator status data indicative of one or more operator status characteristics, such as operator skill, operator, experience, as well as various other operator status characteristics. It should be noted that the operator data 614 can also include, as operator fatigue data, data that indicates the amount of time that the operator has been on shift, how much time the operator has been active, time since the last break, etc. Additionally, it should be noted that the operator data 614 can also include, as operator skill or operator experience data, data that indicates the amount of operations performed by the operator, the amount of operating time of the operator, historical operator performance data, operator training information, operator years of service, as well as various other data.

[ 00165 ] Other data 616 can include any of a wide variety of other data, including various other data provided by operator or user input or obtained from various other sources.

[ 00166 ] As illustrated in FIG. 7, harvesting logistics module 315 includes data capture logic 622, material transfer point identifier logic 624, arrival time logic 626, compaction constraints logic 628, field constraints logic 640, operator constraints logic 642, other constraints logic 644, fill strategy logic 646, location to complete logic 648, route planning logic 650, speed logic 652, display element integration component 659, map generator 660, control signal generator 635, and can include other items 668 as well. Data capture logic 622, itself, includes sensor accessing logic 662, data store accessing logic 624, and can include other items 630 as well. Data capture logic 622 captures or obtains data that can be used by other items of harvesting logistics module 315. Sensor accessing logic 662 can be used by harvesting logistics module 315 to obtain or otherwise access sensor data (or values indicative of the sensed variables/characteristics) provided from in- situ sensors 208 and 408. Additionally, data store accessing logic 664 can be used by harvesting logistics module 315 to obtain or access data stored on data stores 204, 304, and/or 404. Upon obtaining the various data, harvesting logistics module 315 generates logistics outputs 668 which can be used in the control of agricultural harvester 100 and/or receiving machine(s) 400.

[ 00167 ] Compaction constraints logic 628 illustratively identifies operational constraints relative to compaction (e.g., compaction-based operational constraints) at the field of interest. Compaction constraints logic 628 can identify, as a compaction-based operational constraint, limiting the amount of area of the field that a receiving machine 400 travels on during a harvesting operation. For example, there may be compaction-based operational constrain that seeks to limit the total area of the field that a receiving machine 400 travels during a harvesting operation in order to reduce overall compaction at the field. Compaction constraints logic 628 can identify compaction characteristics (e.g., compaction susceptibility, soil properties, etc.) at the field of interest and identify compaction-based operational constraints due to compaction characteristics at the field of interest. Compaction constraints logic 628 identifies compaction susceptibility at different areas of the field. For instance, compaction constraints logic 628 may identify areas (or zones) of the field based on their compaction susceptibility level (e.g., high, medium, low, various other values, etc.). For instance, based on properties of the soil at the field, such as soil moisture or soil type, or both, as well as various other characteristics, compaction constraints logic 628 may identify the compaction susceptibility of different areas of the field. This data can be derived from obtained maps, such as information maps 358 (e.g., soil property map 341). In other examples, the data can be derived from other sources. Compaction constraints logic 628 may then assign operational constraints (or operational limits) to the different areas of the field. For instance, for areas that are identified as susceptible to compaction, compaction constraints logic 628 may assign operational limits such as mass (or weight) limits, traffic limits (e.g., if or how much traffic is allowed, which types of machines are allowed to travel in given locations, etc.), speed limits, as well as various other limits. For some areas of the field, such as areas of the field that have low compaction susceptibility, it may be that there are no compaction-based operational constraints.

[ 00168 ] It will be understood that the compaction-based operational constraints may be predefined (e.g., operator or user preferences, manufacturer recommendations, expert knowledge, historical data, etc.) or may be determined or learned by compaction constraints logic 628. [ 00169 ] Field constraints logic 640 illustratively identifies field characteristics (e.g., topographic characteristics, field features, etc.) at the field of interest and identifies operational constraints due to field characteristics at the field of interest (field-based operational constraints). For instance, field constraints logic 640 may identify areas (or zones) of the field based on fieldbased operational constraints of those areas. For example, field constraints logic 640 may identify areas based on their terrain characteristics (such as terrain characteristics that make material spill more likely). For instance, based on the topographic characteristics of the field, such as the slope or elevation, or both, as well as various other characteristics, field constraints logic 640 may identify the different areas. In another example, field constraints logic 640 may identify areas based on their features, such as field drain tiles, bridges, culverts, proximity to ditches, waterways, ruts or machine pathways, end/cross rows, as well as various other features, which may make material spill more likely. This data can be derived from obtained maps, such as information maps 358 (e.g., information maps 358, such as a field characteristics map that shows field features (e.g., a field feature map that maps field feature characteristics), a field characteristic map that shows terrain characteristics such as topographic map 339, a field characteristic map, such as an optical map that maps optical characteristics, etc.). In other examples, the data can be derived from other sources. Field constraints logic 640 may then assign operational constraints (or operational limits) to the different areas of the field. For instance, for areas having certain terrain characteristics, field constraints logic 640 may assign operational limits such as material fill level limits, speed limits, or material transfer limits (e.g., if a material transfer operation is allowed at those areas). For some areas of the field, such as areas of the field that have relatively flat terrain, it may be that there are no field-based operational constraints. In other examples, for areas of the field having certain features, field constraints logic 640 may assign operational limits such as material fill level limits (e.g., fill volume or fill weight, or both), speed limits, or material transfer limits (e.g., if a material transfer operation is allowed at those areas). The location and presence of various field features may be derived from the obtained data, such as information maps 358 (e.g., a field feature map), or could be derived in various other ways.

[ 00170 ] It will be understood that the field-based operational constraints may be predefined (e.g., operator or user preferences, manufacturer recommendations, expert knowledge, historical data, thresholds, etc.) or may be determined or learned by field constraints logic 640. [00171 ] Operator constraints logic 642 illustratively identifies operator characteristics for the current operation at the field of interest and identifies operational constraints due to operator characteristics for the operators taking part in the current operation (operator-based operational constraints) at the field of interest. For instance, operator constraints logic 642 may identify operator status (e.g., operator skill, operator experience level, and/or operator fatigue, etc.) as operator factors, and identify operator-based operational constraints based thereon. For instance, based on operator data 614, or other data, or both, operator constraints logic 642 may identify areas (or zones) of the field having operator-based operational constraints (operational limits), such as mass limits, traffic limits, speed limits, fill level limits, or material transfer limits. For some areas of the field, or for particular operators, it may be that there are no operator-based operational constraints.

[ 00172 ] It will be understood that the operator-based operational constraints may be predefined (e.g., operator or user preferences, manufacturer recommendations, expert knowledge, historical data, thresholds, etc.) or may be determined or learned by operator constraints logic 642. [ 00173 ] Other constraints logic 644 illustratively identifies other factors for the current operation at the field of interest and identifies other operational constraints due to the other factors. For example, but not by limitation, other constraints logic 644 may identify, as an operational constraint, a distance travelled-based operational constraint. For instance, the distance travelledbased operational constraint may seek to limit the distance that a receiving machine 400 must travel during the harvesting operation.

[ 00174 ] Material transfer point identifier logic 624 illustratively identifies harvester fill levels for given areas at the field of interest along the route of the harvester 100. For example, material transfer point identifier logic 624 can identify predictive fill levels of the harvester 100 at different locations along its planned route, based on the current fill level of the harvester (e.g., data from fill level sensors 224), the harvester route as indicated by harvester route data 612 or agricultural harvester heading data (e.g., data from heading/speed sensors 225), dimensional data of the agricultural harvester 100 (e.g., dimensions or capacity of on-board grain tank), as indicated by agricultural harvester data 100, and data from obtained maps, such as obtained yield maps (e.g., 373, 383, 333, or 602). It will be understood that material transfer point identifier logic 624 can aggregate yield values along the route of agricultural harvester 100 to identify the fill level of the agricultural harvester 100 at different locations along the route. [00175] Material transfer point identifier logic 624 also identifies geographic locations at the field at which a material transfer operation is to be initiated (material transfer start locations). This can include separate locations for both the harvester 100 (harvester material transfer start location) and the receiving machine 400 (receiving machine material transfer start location). As the harvester 100 and receiving machine 400 will be offset by some distance (e.g., lateral distance, fore-to-aft distance, or both), their locations at the field during the initiation of a material transfer operation will also be offset. Material transfer point identifier logic 624 may identify material transfer start locations based on the location at which the harvester will become full or full to a fill level threshold. Material transfer point identifier logic 624 can identify the location at which the agricultural harvester will be full, to capacity or to fill level threshold, (harvester full location) based on obtained maps, the harvester route, as well as the current fill level of the harvester 100. For instance, based on a predictive yield map (e.g., 373, 383, 333, or 602), the harvester route as indicated by harvester route data 612 or agricultural harvester sensor data 604 (e.g., data from heading data heading/speed sensors 225), and current fill data as indicated by agricultural harvester sensor data 604 (e.g., data from fill level sensors 224), material transfer point identifier logic can identify a location on the field, along the route of agricultural harvester 100, at which agricultural harvester will become full, to capacity or to a desired fill level (harvester full location). In some instances, material transfer point identifier logic 626 will identify the harvester full location or a location along the route just prior to the harvester full location as a material transfer start location, that is, a location where a material transfer operation should begin such that a receiving machine 400 can travel to a location on the field, relative to the material transfer location, to begin receiving harvested material.

[ 00176 ] In other examples, material transfer point identifier logic 624 may identify, as material transfer start locations, other locations at the field. For example, in some instances, it may be that the material transfer point identifier logic 624 identifies other locations as material transfer start locations due to other constraints, such as compaction-based operational constraints, fieldbased operational constraints, operator-based operational constraints, as well as various other operational constraints (e.g., distance travelled-based operational constraint).

[ 00177 ] For instance, it may be that it is desirable to begin emptying harvester 100 (transferring material) prior to the location the harvester will become full due to compaction-based operational constraints of the field ahead of the harvester 100 along its route. Thus, material transfer point identifier logic 624 may identify a location earlier in the route, prior to agricultural harvester becoming full and prior to traveling in the area with higher compaction susceptibility, to reduce the weight of the harvester 100. In another example, it may be that an area of the field proximate the harvester’ s route, on which the receiving machine 400 would have to travel during a material transfer operation initiated at the harvester full location (or proximate the harvester full location), is susceptible to compaction. Thus, material transfer point identifier logic 624 may identify a location earlier in the route prior to the agricultural harvester becoming full to begin transferring material to the receiving machine 400 to prevent receiving machine 400 from traveling in compaction-susceptible area of the field.

[00178 ] In another example, it may be that is desirable to begin emptying harvester 100 prior to the harvester full location due to field-based operational constraints, such as terrain factors (e.g., slope and elevation) as well as other field-based operational constraints such as field features (e.g., wet spots, culverts, waterways, obstacles, etc.). For instance, it may be that it is undesirable to transfer material while the harvester 100 is traveling uphill due to the extra power requirements to propel the harvester 100 uphill. In another example, the upcoming terrain may result in machine dynamics (e.g., pitch and roll) that make material spill more likely when the harvester 100 is full to a certain level, thus it may be desirable to empty the harvester 100 (at least by a select amount) prior the harvester 100 entering the area. In another example, it may be that field features that are to be traversed require the harvester 100 to have less weight. In any case, material transfer point identifier logic 624 may identify a location earlier in the route, prior to agricultural harvester becoming full and prior to traveling in the area with the given field-based operational constraints, to conduct a material transfer operation. In another example, it may be that an area of the field proximate the harvester’ s route, on which the receiving machine 400 would have to travel during a material transfer operation initiated at the harvester full location (or proximate the harvester full location), has various field-based operational constraints, such as slope or elevation that affects the power distribution or speed capabilities of the receiving machine, terrain features that may cause machine dynamics (e.g., pitch and roll) that makes material spill more likely, as well as field features that are undesirable to drive over, particularly when the receiving machine 400 has additional load due to received material. Thus, material transfer point identifier logic 624 may identify a location earlier in the route prior to the agricultural harvester becoming full to begin transferring material to the receiving machine 400 to prevent receiving machine 400 from traveling in areas of the field with field-based operational constraints.

[ 00179 ] In another example, it may be that is desirable to begin emptying harvester 100 prior to the harvester full location due to operator-based operational constraints, such as machine operation constraints due to operator status (e.g., operator skill, operator experience, operator fatigue, etc.). For instance, it may be that there are operator-based operational constraints at the field, such as speed limits, travel limits (where the operator is allowed to drive), fill level limits, etc., that make starting the material transfer operation at or closer to the harvester full location untenable or undesirable. In any case, material transfer point identifier logic 624 may identify a location earlier in the route, prior to agricultural harvester becoming full and prior to traveling in the area with the given field-based operational constraints, to conduct a material transfer operation. In another example, it may be that an area of the field proximate the harvester’s route, on which the receiving machine 400 would have to travel during a material transfer operation initiated at the harvester full location (or proximate the harvester full location), has various field-based operational constraints, such as slope or elevation that affects the power distribution or speed capabilities of the receiving machine, terrain features that may cause machine dynamics (e.g., pitch and roll) that makes material spill more likely, as well as field features that are undesirable to drive over, particularly when the receiving machine 400 has additional load due to received material. Thus, material transfer point identifier logic 624 may identify a location earlier in the route prior to the agricultural harvester becoming full to begin transferring material to the receiving machine 400 to prevent receiving machine 400 from traveling in areas of the field with field-based operational constraints.

[ 00180 ] In other examples, material transfer point identifier logic 624 can identify a cluster of material transfer locations across the field for multiple passes of the harvester. For example, to limit travel of the receiving machine 400 (e.g., travel distance) and to limit travel of the receiving machine 400 on the field (e.g., to reduce compaction of the field), material transfer point identifier logic 624 may identify material transfer locations for a plurality of planned passes of the harvester 100 at the field. These locations may be selected to limit the travel of the receiving machine 400 (e.g., distance travelled and/or area of the field travelled upon) to the greatest extent possible given constraints of the operation. To calculate these locations, material transfer point identifier logic 624 can consider the various data obtained by harvesting logistics module 315, for example, but not by limitation, the fill capacity and fill level of harvester 100, the location of one or more receiving machines 400, the harvester route data, the speed of harvester 100, predictive yield of crop along the route of the harvester, constraint areas (zones) of the field, the location of material delivery locations, as well as various other data. For example, material transfer point identifier logic can identify a predictive aggregated yield value indicative of a predicted amount of crop that will be collected by harvester along the route based on yield data from a yield map, such as from one of predictive maps 601 or another type of yield map. Then, based on the capacity of the harvester 100, the current fill level of harvester 100, as well as any applicable thresholds (e.g., threshold fill level, etc.), material transfer point identifier logic 624 can identify, a cluster of material transfer start locations and end locations on a plurality of passes of the harvester at the field. These material transfer start locations may be located prior to a location at which the harvester 100 will be full, even to a threshold level and the material transfer end locations may be located prior to a location at which the harvester 100 is empty or the receiving vehicle 400 is full. The start and end locations may represent a cluster of locations relative to successive passes of the harvester 100 that limit the travel of receiving machine 400 (e.g., distance travelled and/or area of field travelled) while also ensuring that harvester 100 can keep harvesting in between the locations without becoming overfull. It will also be understood that identifying the cluster of locations can be further constrained based on one or more other criterion, such as compaction-based operational constraints, field-based operational constraints, operator-based operational constraints, as well as various other operational constraints. The cluster of locations can be output and integrated into a map, such as a harvesting logistics map 661, for display to an operator or user, or both. Further, the cluster of locations can be output and utilized by route planning logic 650 to plan routes of a receiving machine 400, which can then be used to control a receiving machine 400 (e.g., to control propulsion subsystem 450 and steering subsystem 452). Further, the cluster of locations can be output and utilized to control the operation of the material transfer subsystem 254 of the harvester 100.

[00181 ] Arrival time logic 626 illustratively identifies a time at which agricultural harvester 100 will reach a location along its route, such as a material transfer location or a harvester full location identified by material transfer point identifier logic 624. For instance, based on the current geographic location of the agricultural harvester 100 (as indicated by data from geographic position sensor 203), the route or heading of agricultural harvester 100, the geographic location identified by material transfer point identifier logic 624, or another location, current speed characteristic data, as indicated by heading/speed sensors 225, as well as predictive speed characteristic values, as provided by a speed map (e.g., 358, a prescriptive speed map, functional predictive speed map 1360 or functional predictive speed control zone map 1361), along the route of agricultural harvester 100, arrival time logic 626 can determine a time at which agricultural harvester 100 will arrive at a location.

[ 00182 ] Arrival time logic 626 illustratively identifies a time at which receiving machine(s) 400 can or will arrive at a location, such as location to begin initiating a material transfer operation (receiving machine material transfer location). For instance, based on a current location of a receiving machine 400 (e.g., data from geographic position sensor 403), route or heading of the receiving machine (e.g., data from heading/speed sensors 425, route commanded route planning logic, etc.), the location identified by material transfer point identifier logic 624, or another location, current speed characteristic data, as provided by heading/speed sensors 425, as well as any speed limits along the route of the receiving machine 400, arrival time logic 626 can determine a time at which a receiving machine 400 will arrive at a location.

[ 00183 ] Fill strategy logic 646 illustratively identifies a filling strategy for filling a receiving machine 400 during a material transfer operation. The filling strategy can include the amount of material transferred or the way in which the material is transferred (front-to-back, back-to-front, etc.). For example, it may be that fill strategy logic 646 determines an amount of material to transfer based on the capacity (or dimensions) of receiving machine 400, as indicated by receiving machine data 610, as well as a current fill level of the receiving machine 400 (e.g., as indicated by data received from fill level sensors 424). For instance, it may be desirable to fill the receiving machine 400 to capacity or to a threshold fill level. In other examples, the amount of material transferred to the receiving machine 400 may be constrained based upon various operational constraints at the field (such as along a route of the receiving machine 400), such as compactionbased operational constraints (e.g., mass limits), field-based operational constraints (e.g., fill level limits, mass limits, etc.), operator-based operational constraints (e.g., mass limits, fill level limits, etc.), as well as various other operational constraints.

[00184 ] Additionally, or alternatively, it may be desirable to fill the receiving machine 400 in a particular fill location order (e.g., front-to-back, back-to-front, middle to one end and then another end, etc.) given certain operational constraints. For instance, because of compaction-based operational constraints, it may be desirable to begin filling the receiving machine 400 in the middle of the material receptacle (e.g., 172 or 192) to more evenly distribute the force applied to the field. In another example, it may be desirable to begin filling the receiving machine 400 in the middle of the material receptacle due to operator status, such as operator fatigue, operator skill, or operator experience, or a combination thereof. For example, a fatigued, less skilled, or inexperienced operator may be more likely to control the receiving machine 400 in such a way as to cause material shift. Thus, if the material is in the middle of the material receptacle, it will be more likely to remain in the material receptacle, in the case of material shift, as opposed to spilling out of the material receptacle. In other examples, it may be desirable to begin filling the receiving vehicle from the front (as opposed to the back) of the material receptacle or from back (as opposed to the front) of the material receptacle. For instance, due to the upcoming terrain along the route of the receiving machine, which may cause machine dynamics (e.g., pitch or roll, or both) which may increase the likelihood of material shift or material spill. For example, due to upcoming increases(s) in the elevation of the field (e.g., the machine will be traveling uphill or will encounter bumps, or both), it may be desirable to execute a front-to-back filling strategy, such that the material, being in the front of the material receptacle as the machine encounters the increases(s), will be less likely to spill. Additionally, a higher load in the back of the material receptacle while traveling uphill may cause traction issues. In another example, due to upcoming decrease(s) in elevation of the field (e.g., the machine will be traveling downhill or will encounter dips, or both), it may be desirable to execute a back-to-front filling strategy, such that the material, being in the back of the material receptacle as the machine encounters the decrease(s), will be less likely to spill.

[ 00185 ] Location to complete logic 648 identifies geographic locations at the worksite at which a material transfer operation will end or is to be ended (material transfer completion locations). This can include separate locations for both the harvester 100 (harvester material transfer completion locations) and a receiving machine 400 (receiving machine material transfer completion locations). As the harvester 100 and the receiving machine 400 will be offset by some distance, their locations at the field as the material transfer operation ends will also be offset. Location to complete logic 648 may identify material transfer completion locations based on the material transfer start locations identified by material transfer point identifier logic 626 along with various other data, such as various operational constraints (e.g., compaction-based operational constraints, field-based operational constraints, operator-based operational constraints, other operational constraints), the amount of material to be transferred (e.g., as output by fill strategy logic 646), the speed or rate at which the material transfer subsystem can or will transfer material (e.g., as derived from material transfer subsystem data 607), the speed of the agricultural harvester 100 as derived from a speed map, such as a prescriptive speed map or a functional predictive map 263 (e.g., 1360 or 1361) or from harvester speed data (e.g., data from heading/speed sensors 225), as well as the speed of receiving machine 400 as derived from receiving machine speed data (e.g., data from heading/speed sensors 225) or as output by speed logic 652 (e.g., the receiving machine speed may match the speed of the harvester 100).

[ 00186 ] In some examples, location to complete logic 648 may identify an initial material transfer completion location but may adjust the initial location due to various operational constraints. For instance, it may be undesirable to perform a material transfer operation at certain locations in the field based on operational constraints at those locations, such as compaction-based operational constraints, field-based operational constraints, operator-based operational constraints, or various other operational constraints. Adjusting the material transfer completion location, may cause material transfer point identifier logic 624 to identify a new material transfer start location. In other examples, adjusting the material transfer completion location, may cause fill strategy logic 646 to adjust a parameter of the fill strategy, such as the amount of material to be transferred. For instance, if an initial material transfer completion location would cause the receiving machine 400 to travel in a compaction susceptible area or to travel in a compaction susceptible area with too much mass, location to complete logic 648 may identify a new material transfer completion location to prevent the receiving machine 400 from doing so. In another example, if an initial material transfer completion location would cause the receiving machine 400 to travel in an area of the field having certain characteristics, such as topographic characteristics or field features, location to complete logic 648 may identify a new material transfer completion location to prevent the receiving machine 400 from doing so. In another example, if an initial material transfer completion location would cause the harvester 100 to conduct a material transfer operation in an area of the field having certain characteristics, such as topographic characteristics (e.g., when going uphill), location to complete logic 648 may identify a new material transfer completion location to prevent the harvester from doing so. In another example, if an initial material transfer completion location would cause an operator 360 to operate beyond operator-based operational constraints, location to complete logic 648 may identify a new material transfer completion location to prevent the operator 360 from doing so.

[ 00187 ] In some examples, it may be that due to the location of the initial material transfer completion location, the receiving machine 400 would be required to travel over areas having certain operational constraints, in order to exit the field or to travel to the location where the material is to be transferred from the receiving machine 400, location to complete logic 648 may identify a new material transfer completion location to allow the use of a route that could avoid such areas.

[ 00188 ] Speed logic 652 illustratively determines speed characteristics for agricultural harvester 100 or receiving machine(s) 400 (or both). For instance, where arrival time logic 626 indicates that the agricultural harvester 100 will arrive at the material transfer location before a receiving machine 400, speed logic 652 may generate an output to reduce the speed of agricultural harvester 100 such that agricultural harvester 100 will arrive at the material transfer location closer in time (or at the same time) as a receiving machine 400. Reducing the speed of the agricultural harvester 100 in this way can reduce downtime, reduce wear, save fuel, improve ride quality, as well as improve performance of the agricultural harvester 100, such as by reducing grain loss. In the same example, where arrival time logic 626 indicates that the agricultural harvester 100 will arrive at the material transfer location before a receiving machine 400, speed logic 651 may generate an output to increase the speed of the receiving machine 400 such that receiving machine 400 will arrive at the material transfer location closer in time, such as ahead of or at the same time, as agricultural harvester 100. In this way, the operation of the agricultural harvester 100 is not interrupted. In some examples, speed logic 652 may provide an output to adjust the speed of both the agricultural harvester 100 and the receiving machine 400, such that they arrive at the material transfer location closer in time (or at the same time). For instance, there may be speed characteristic limits (operational constraints) on the agricultural harvester 100 and the receiving machine 400 such that both can only be incrementally changed. In another example, other parameters of the operation may dictate adjustment of both the agricultural harvester 100 and a receiving machine 400, such as a preferred performance metrics or other operational constraints.

[ 00189 ] In other examples, where arrival time logic 626 indicates that the receiving machine 400 will arrive at the material transfer start location before the agricultural harvester 100, speed logic 652 may generate an output to reduce the speed of the receiving machine 400 such that the receiving machine 400 will arrive at the material transfer start location closer in time (or at the same time) as agricultural harvester 100. Reducing the speed of the receiving machine 400 in this way can reduce downtime, improve ride quality, save fuel, reduce wear, as well as various other benefits. In the same example, where arrival time logic 626 indicates that the receiving machine 400 will arrive at the material transfer start location before agricultural harvester 100, speed logic 652 may generate an output to increase the speed of agricultural harvester 100 such that agricultural harvester 100 will arrive at the material transfer location closer in time (or at the same time) as the receiving machine 400. Increasing the speed of agricultural harvester 100 in this way can reduce down time, improve time to complete, as well as various other benefits. In some examples, speed logic 652 may provide an output to adjust the speed of both the agricultural harvester 100 and the receiving machine 400, such that they arrive at the material transfer start location closer in time (or at the same time). For instance, there may be speed characteristic limits on the agricultural harvester 100 and the receiving machine 400 such that both can only be incrementally changed. In another example, other parameters of the operation may dictate adjustment of both the agricultural harvester 100 and a receiving machine 400, such as preferred harvesting performance metrics or other operational constraints.

[00190 ] Speed logic 652 illustratively identifies speeds at which receiving machine 400 or harvester 100 are to operate at based on various factors, such as operational constraints at the field. For instance, it may be desirable to limit the speed of a harvester 100 or receiving machine 400 based on various operational constraints at the field, such as compaction-based operational constraints, field-based operational constraints, operator-based operational constraints, as well as various other operational constraints. For example, the speed of a machine traveling in a compaction-susceptible area may need to be reduced as compared to an area of the field less susceptible to compaction. In another example, the speed of a machine may need to be reduced based on field characteristics, such as bumps or dips which may cause instability and machine dynamics that may increase the likelihood of material shift or spill if the speed is not reduced or limited, as well as field features which may require the machine to travel over those features more slowly to prevent damage to the feature or the machine. In another example, the speed of a machine may need to be reduced (or reduced in certain areas) based on operator-based operational constraints. For example, it may be desirable to limit the speed of a fatigued, less skilled, or an inexperienced operator generally, or at particular locations of the field. [ 00191 ] Speed logic 652 illustratively identifies speeds at which receiving machine 400 or harvester 100 are to operate based on fill strategy as identified by fill strategy logic 654. For instance, it may be that the speed of the receiving machine 400 is controlled to match the speed of the harvester 100 as indicated by speed data from heading speed sensors 225 or from a speed map, such as a prescriptive speed map or a predictive speed map (e.g., 1360 or 1361, or both). In order to adjust the fill location, speed logic 652 may identify a temporary acceleration or deceleration for the receiving machine 400 to adjust the position of the receiving machine 400 relative to the agricultural harvester 100. In other examples, in order to adjust the fill location, speed logic 652 may identify a temporary acceleration or deceleration for the harvester 100 to adjust the position of the harvester 100 relative to the receiving machine 400.

[ 00192 ] It will be understood that, in other examples, after the receiving machine 400 is initially positioned, the speed of the harvester 100 and receiving machine 400 may be matched, and the material transfer subsystem 254 may be controllably positioned to adjust the fill location. [ 00193 ] Route planning logic 650 illustratively identifies routes for a receiving machine 400 to a material transfer start location. In some examples, route planning logic 658 may identify the fastest or shortest route to the material transfer start location based on the location of the receiving machine 400 (e.g., as indicated by data from geographic position sensor 403) and the location of the material transfer start location (as indicated by material transfer point identifier logic 624). In other examples, route planning logic 650 may identify a route for a receiving machine 400 to a material transfer location based further on various operational constraints, such as compactionbased operational constraints, field-based operational constraints, operator-based operational constraints, or various other operational constraints. For example, route planning logic 650 may identify the fastest or shortest route given the various operational constraints, for example speed limits or traffic limits. For instance, the most direct route may not be the fastest route when the most direct route travels through an area with a speed limit. In another example, it may be that no receiving machine traffic is allowed in a given area or that traffic is to be limited in a given area.

[00194 ] In some examples, it may be that the fastest or shortest route is not desirable given other factors. For example, where arrival time logic 626 indicates that the receiving machine 400 will arrive at the material transfer start location ahead of the harvester 100 if traveling the identified fastest or shortest route, route planning logic 650 may identify a different route that will cause the arrival time of the receiving machine 400 and harvester 100 to be closer. For instance, it may be preferable to alter the route of the receiving machine 400 rather than to adjust a speed of the harvester or adjust a speed of the receiving machine 400. In some examples, where there is sufficient time, route planning logic 650 may choose a longer route that avoids operational constraint areas. For instance, it may be that traffic is allowed in a given area, but is to be limited. Where there is sufficient additional time, route planning logic 650 may avoid that area in a given iteration such that it can be traveled on in a future instance where there is less additional time.

[ 00195 ] Of course, in some examples, it may be that the fastest route or shortest route is maintained, and where there is additional time, the receiving machine 400 may be controlled to only initiate traveling along the route based on the timing.

[00196] Route planning logic 650 illustratively identifies routes for a receiving machine 400 from a material transfer completion location to another location, such as a location off of the field or the location to which the receiving machine 400 is to deliver material. In some examples, route planning logic 658 may identify the fastest or shortest route based on the location of the material transfer completion location (as indicated by material transfer point identifier logic 624) and the location off the field or the location to which the receiving machine 400 is to travel to deliver its material. In other examples, route planning logic 650 may identify a route for a receiving machine 400 to a location off the field or to the location to which the receiving machine 400 is to travel to deliver its material based further on various operational constraints, such as compaction-based operational constraints, field-based operational constraints, operator-based operational constraints, or various other operational constraints. For example, route planning logic 650 may identify the fastest or shortest route given the various operational constraints, for example speed limits, mass or fill level limits, or traffic limits. For instance, the most direct route may not be the fastest route when the most direct route travels through an area with a speed limit. In another example, it may be that no receiving machine traffic is allowed in a given area, that traffic is to be limited in a given area, or that no traffic is allowed in the area given the fill level or weight of the receiving machine 400, or due to the likelihood of material shift or material spill, or due to the operator status (e.g., skill, experience, fatigue, etc.) 360.

[ 00197 ] Map generator 660 illustratively generates one or more harvesting logistics maps 661. Harvesting logistics maps 661 illustratively map the operational area (which may include one or more of one or more worksites [fields], roads, storage locations, and purchasing locations) in which the harvesting operation is being performed. Harvesting logistics maps 661 may include a variety of display elements (discussed below) and can be used in the control of an agricultural harvester 100 or receiving machine 400, or both. In some examples, map generator 660 may generate separate harvesting logistics maps 661 (having different display elements) for each different machine.

[ 00198 ] Display element integration component 659 illustratively generates one or more display elements, such as material transfer start location display elements, material transfer completion location display elements, operational constraint area (zone) display elements, route display elements, receiving machine display elements, agricultural harvester display elements, as well as various other display elements. Display element integration component 659 can integrate the one or more display elements into one or more maps, such as one or more of functional predictive maps 263, information maps 358, or a separate harvesting logistics map 661 generated by map generator 660.

[ 00199 ] Thus, it can be seen that harvesting logistics module 315 can generate a variety of different harvesting logistics outputs 669 which can be provided to control system 214 of a harvester 100 or a control system 414 of a receiving machine 400. For example, logistics outputs 669 can be in the form of material transfer start locations, predictive harvester fill levels, predictive harvester full locations, as well as various other identifications generated by material transfer point identifier logic 624, Logistics outputs 669 can be in the form of compaction-based operational constraints, compaction-based operational constraint areas (or zones), as well as various other identifications generated by compaction constraints logic 628. Logistics outputs 669 can be in the form of field-based operational constraints, field-based operational constraint areas (or zones), as well as various other identifications generated by field constraints logic 640. Logistics outputs 669 can be in the form of operator-based operational constraints, operator-based operational constraint areas (or zones), as well as various other identifications generated by operator constraints logic 642. Logistics outputs 669 can be in the form of other operational constraints (e.g., distance travelled-based operational constraint), other operational constraint areas (or zones), as well as various other identifications generated by other constraints logic 644. Logistics outputs 669 can be in the form of a fill strategy identified by fill strategy logic 646, such as a fill amount (e.g., weight or volume, or both) or a fill location order (e.g., front-to-back, back-to-front, middle to one end and then to other end, etc.), or both, as well as various other identifications generated by fill strategy logic 646. Logistics outputs 669 can be in the form of material transfer completion locations, as well as various other identifications generated by location to complete logic 648. Logistics outputs 669 can be in the form of routes, as well as various other identifications generated by route planning logic 650. Logistics outputs can be in the form of speed characteristics (e.g., travel speed, acceleration, deceleration), as well as various other identifications generated by speed logic 652. Logistics outputs 669 can be in the form of display elements generated by display element integration component 659. Logistics outputs 669 can be in the form of display a map generated by map generator 660, such as harvesting logistics map 660.

[ 00200 ] Logistics outputs 669 may be in the form of control outputs that can be used to control a harvester 100 or a receiving machine 400. For examples, as illustrated in FIG. 7, harvesting logistics module 315 includes a control signal generator 635 which may generate control outputs which are provided as logistics outputs 669. Thus, when logistics module 315 is located remotely from the machines, it may function as a remote control system. Such control outputs may be in the form of commanded travel speeds, commanded routes, commanded material transfer operation initiation or termination, or both, as well as various other control outputs.

[ 00201 ] It will be noted that at the one or more functional predictive maps 263 are updated or otherwise made new (as described above in FIG. 6), the logistics outputs 669 generated by harvesting logistics module 315 can also be updated or otherwise made new according to the updated (or new) functional predictive maps 263.

[ 00202 ] The logistic outputs 669 can be provided to control system 214 to control agricultural harvester 100. As illustrated in FIG. 7, controllers 235 of control system 214 include propulsion controller 630, route controller 632, communication system controller 634, interface controller 636, material transfer controller 638, and can include various other controllers 639.

[ 00203 ] Propulsion controller 630 generates control signals to control propulsion subsystem 250, such as to control the acceleration, deceleration, or travel speed of agricultural harvester 100. For example, propulsion controller 630 may control propulsion subsystem 250 based on logistics outputs 669.

[ 00204 ] Route controller 632 generates control signals to control steering subsystem 252, such as to control the heading of agricultural harvester 100 according to a route.

[ 00205 ] Communication system controller 634 controls communication system 206 to send or obtain information, or both. [ 00206 ] Interface controller 636 generates control signals to control operator interface mechanism(s) 218 such as to provide displays, alerts, notifications, recommendations, or various other indications. For example, interface controller 636 may generate control signals to generate displays of maps, such as the display of one or more functional predictive maps 263 (with or without integrated display elements generated by component 659) or harvesting logistics maps 661. In another example, interface controller 636 may generate control signals to generate displays or other indications (e.g., visual or audible alerts, notifications, recommendations, etc.) such as to adjust the operation of agricultural harvester 100.

[ 00207 ] Material transfer controller 638 generates control signals to control material transfer subsystem 254 such as to initiate or end a material transfer operation, to control the flow rate of material through the chute 134 and spout 136 such as by controlling the operational speed of the auger or blower 133, as well as to control the position (e.g., rotational position) of material transfer subsystem 254.

[ 00208 ] The logistic outputs 669 can be provided to control system 414 to control a receiving machine 400. As illustrated in FIG. 7, controllers 435 of control system 414 include propulsion controller 670, route controller 672, communication system controller 674, interface controller 676, material transfer controller 678, and can include various other controllers 639.

[ 00209 ] Propulsion controller 670 generates control signals to control propulsion subsystem 250, such as to control the acceleration, deceleration, or travel speed of receiving machine 400. For example, propulsion controller 670 may control propulsion subsystem 450 based on logistics outputs 669.

[ 00210 ] Route controller 672 generates control signals to control steering subsystem 452, such as to control the heading of receiving machine 400 according to a route, such as a route generated by route planning logic 658 (which may be indicated in a map).

[ 00211 ] Communication system controller 674 controls communication system 406 to send or obtain information, or both.

[ 00212 ] Interface controller 676 generates control signals to control operator interface mechanism(s) 418 such as to provide displays, alerts, notifications, recommendations, or various other indications. For example, interface controller 676 may generate control signals to generate displays of maps, such as the display of one or more functional predictive maps 263 (with or without integrated display elements generated by component 659) or harvesting logistics maps 661. In another example, interface controller 676 may generate control signals to generate displays or other indications (e.g., visual or audible alerts, notifications, recommendations, etc.) such to adjust the speed of a receiving machine 400 or to adjust the heading (or route) of receiving machine 400.

[ 00213 ] Material transfer controller 678 generates control signals to control material transfer subsystem 454 such as to initiate or end a material transfer operation, to control the flow rate of material through the chute 171 and spout 173 such as by controlling the operational speed of the auger or blower, to control the position (e.g., rotational position) of material transfer subsystem 454, or to actuate (e.g., open or close) door 191.

[ 00214 ] FIG. 8A is a pictorial illustration showing one example of harvesting logistics module 315 in controlling a harvesting operation. As illustrated, FIG. 8 A, a harvester 100 is operating at a field 111 which is proximate to a road 802. Access to the field 111 from the road 802 is provided by entrance 804. Additionally, as shown in FIG. 8A, one or more receiving machines 400 are also present, such as a receiving machine 400- 1 (illustrated as a tractor and grain cart) and a receiving machine 400-2 (illustrated as a tractor and trailer).

[ 00215 ] Agricultural harvester 100 will travel along route 806. Harvesting logistics module 315 has identified operational constraint areas (or zones) 850 and 851 at the field 111. In the illustrated example, constraint area 850 is an area with no operational constraints or with less restrictive operational constraints as compared to constraint areas 851. For example, constraint area 850 may be an area with less or no compaction-based operational constraints (e.g., a low or medium compaction susceptibility area), less or no field-based operational constraints (e.g., a relatively flat area or an area without field features), or less or no operator-based operational constraints. Constraint areas 851, on the other hand, may be areas with more operational constraints, such as more compaction-based operational constraints (e.g., high compaction susceptibility areas), more field-based operational constraints (e.g., areas with uneven terrain or terrain that is likely to cause undesirable machine dynamics, or both, or areas with field features), or areas with more operator-based operational constraints.

[ 00216 ] As illustrated, harvesting logistics module 315 has identified a harvester material transfer start location 822, which, in the illustrated example, is the location at which (or a location closely proximate to the location at which) the harvester 100 will be full or full to a threshold level. Harvesting logistics module 315 has also identified a receiving machine material transfer start location 832 based on the location of the harvester material transfer start location 822, as well as other factors.

[ 00217 ] Harvesting logistics module 315 may identify a theoretical shortest or fastest route 870. However, as can be seen, route 870 passes through constraint area 851-1, which may have operational constraints that cause route 870 to be slower, or such that it is undesirable to cause receiving machine 400 to travel through. Thus, harvesting logistics module 315 identifies a route 860 based on the operational constraints of the field 111. As can be seen route 860 avoids travel through constraint area 851-1, instead traveling through area 850 which may have no operational constraints or less restrictive operational constraints. In the example illustrated in FIG. 8A it will be understood that the area of the field over which the receiving machine 400-1 is to travel has already been harvested.

[ 00218 ] Harvesting logistics module 315 identifies a harvester material transfer completion location 826 and a receiving machine material transfer completion location 836 based on the respective material transfer start locations, 822 and 832. As can be seen, the identified completion locations 826 and 836 would result in receiving machine 400 operating in a constraint area 851-2 and harvesting machine 100 performing a material transfer operation in a constraint area 851-2. For example, it may be that constraint area 851 -2 has constraints that make it undesirable to operate receiving machine 400 in area 851-2, such as compaction-based operational constraints, fieldbased operational constraints, or operator-based operational constraints. Additionally, the exit route 892 from completion location 836 may also be undesirable. Additionally, it may be undesirable for agricultural harvester 100 to perform a material transfer operation in area 851-2, for example, area 851-2 may be uphill (relative to the direction of travel of harvester 100) and thus performing a material transfer operation may take necessary power away from other subsystems. [ 00219 ] Thus, harvesting logistics module 315 may identify a new harvester material transfer start location 820 and thus a new receiving machine material transfer start location 830. Harvesting logistics module 315 also identifies a route 864 for receiving machine to travel to start location 830. Harvesting logistics module 315 identifies a harvester material transfer completion location 824 and a receiving machine transfer completion location 834 based on the respective material transfer start locations, 820 and 830. As can be seen, the adjusted material transfer operation does not cause operation in a constraint zone 851. In other examples, it may be that the start location need not be adjusted, rather the amount of material transferred can be adjusted or the speed at which it is transferred can be adjusted, or both. For instance the material transfer operation may still start at locations 822 and 832, however, it may be terminated earlier than locations 826 and 836, for example, at locations 824 and 834, or at other locations outside of constraint zone 851-2.

[ 00220 ] Harvesting logistics module 315 may identify a theoretical shortest or fastest exit route 890 to the delivery location (e.g., material receiving machine 400-2). However, as can be seen, exit route 890 passes through constraint area 851-3, which may have operational constraints that cause route 890 to be slower, or such that it is undesirable to cause receiving machine 400 to travel through. Thus, harvesting logistics module 315 identifies a route 880 based on the operational constraints of the field 111. As can be seen route 880 avoids travel through constraint area 851-3, instead traveling through area 850 which may have no operational constraints or less restrictive operational constraints.

[ 00221 ] FIG. 8B is a pictorial illustration showing one example of harvesting logistics module 315 in controlling a harvesting operation. FIG. 8B is similar to FIG. 8 A and thus similar items are numbered similarly. FIG. 8B illustrates an example of harvesting logistics module 315 in identifying a cluster of material transfer locations on a plurality of passes (illustratively shown as seven passes) of an agricultural harvester 100 during its planned route 1806. The cluster of material transfer locations includes a plurality of harvester material transfer start locations 1822 (illustratively 1822-1 through 1822-6), a plurality of harvester material transfer end locations 1826 (illustratively 1826-1 through 1826-6), a plurality of receiving machine material transfer start locations 1832 (illustratively 1832-1 through 1832-6), and a plurality of receiving machine material transfer end locations 1836 (illustratively 1836-1 through 1836-6). While the example in FIG. 8B illustrates the operation of harvesting logistics module 315 occurring at the beginning of a harvesting operation, it will be understood that harvesting logistics modules 315 can identify a cluster of material transfer locations at any time during an operation or before an operation begins. FIG. 8B illustrates the operation of harvesting logistics module 315 in controlling a harvesting operation based on operational constraints, such as a compaction-based operational constraint that seeks to limit the amount of area of the field that a receiving machine must travel as well as a distance-travelled constraint that seeks to limit the distance that a receiving machine 400 travels during the harvesting operation. [ 00222 ] As shown, harvesting logistics module 315 has identified a harvester material transfer start location 1822-1, which, as the initial location may be the location at which (or location closely proximate to the location at which) the harvester 100 will be full or full to a threshold level. Though, in other examples, this need not be the case. In other examples, even the initial material transfer start location can be at a location much earlier along the route of the harvester 100 and prior to the harvester becoming full or full to a threshold level. Harvesting logistics module 315 has also identified a receiving machine material transfer start location 1832- 1 based on the harvester material transfer start location 1822- 1. Harvesting logistics module 315 has also identified a harvester material transfer completion location 1826-1 and a receiving machine material transfer completion location 1836-1 based on the respective material transfer start locations, 1822-1 and 1832-1 as well as based on other data. For example, the completion locations 1826 and 1836 may be the theoretical latest locations up to which material transfer can occur while allowing for other constraints, such as allowing time for receiving machine 400-1 to travel to receiving machine 400-2 (or to another material delivery location) and to transfer material and to return to the field to receive more material from the harvester 100 to limit the travel of the receiving machine 400-1 on the field and thus the compaction of the field. For example, harvesting logistics module 315 may calculate the material transfer completion locations 1826 and 1836 on successive passes of the harvester 100 to limit the distance travelled by the receiving machine 400- 1 as well as to limit the area of the field on which the receiving machine 400-1 will travel.

[ 00223 ] Thus, the cluster of material transfer locations are identified by harvesting logistics module 315 to limit the distance that the receiving machine 400-1 must travel and to limit the amount of field that the receiving machine 400-1 must travel on during the operation, to the greatest extent possible, or within a threshold of the greatest extent possible, taking into account various factors such as a combination of the capacity of the harvester 100, the current fill level of the harvester 100, the yield at the field, the route of the harvester 100, the travel speed of the harvester 100, the location of material delivery locations (e.g., the location of receiving machine 400-2), the time for the receiving machine 400-1 to complete delivery of material, the location of the field entrance 804, as well as various other factors.

[ 00224 ] Thus, the cluster of material transfer locations shown in FIG. 8B represent a plurality of locations on passes of the harvester 100 that limit the travel of the receiving machine 400-1 in both distance as well as in area of the field 111 while still allowing harvester 100 to continuously harvest without becoming overfull.

[ 00225 ] Thus, the successive harvester material transfer start locations 1822-2 through 1822-6 and the harvester material transfer completion locations 1832-2 through 1832-6 and the successive receiving machine material transfer start locations 1832-2 through 1832-6 and the receiving machine material transfer completion locations 1836-2 through 1836-6 are identified, by harvesting logistics module 315 to limit the travel distance of receiving machine 400-1 and to limit the area of field 111 on which receiving machine 400- 1 must travel while still allowing harvester 100 to continuously harvest at field 111 without becoming overfull, given various factors, such as those described above.

[ 00226 ] Additionally, harvesting logistics module 315 can identify routes 1860 (illustratively shown as 1860-1 through 1860-6) for receiving machine 400-1 to travel based on the material transfer locations (1832 and 1836) as well as based on other factors. The routes may represent the shortest possible route given other criteria. In identifying the routes, harvesting logistics module 315 can account for the distance between the material receiving machine 400-2 and the material transfer locations (e.g., receiving machine material transfer start locations 1836), the route and heading of harvester 100, as well as areas of the field that have been harvested and have not yet been harvested. For example, it may be that receiving machine 400- 1 will have to turn around on the field 111 in order to match its heading with the heading of harvester 100 during a material transfer operation, as illustrated by routes 1860-1, 1860-3, and 1860-5, or to turn around on the field to return to a material delivery location (e.g., receiving machine 400-2), as illustrated by routes 1860-2, 1806-4, and 1860-6.

[ 00227 ] As illustrated, harvesting logistics module 315 also identifies constraint area(s) (or zone(s)) 1850 and 1851 (illustratively shown as 1851-1 and 1851-2) at the field 111. In the illustrated example, constraint area 1850 may be similar to constraint area 850 of FIG. 8A and constraint areas 1851 may be similar to constraint areas 851 of FIG. 8A.

[ 00228 ] In addition to the various other factors described above, harvesting logistics module 315 may take into account identified constraint areas when identifying the cluster of material transfer locations as well as the routes for receiving machine 400-1. For example, the theoretical shortest route for receiving machine 400-1 to travel to receiving machine material transfer start location 1832-4 is represented by theoretical route line 1870-4. However, given the location of constraint area 1851-1, harvesting logistics module 315 identifies route 1860-4 as the route for receiving machine to travel. Additionally, it may be that material transfer could have been initiated sooner on the fourth pass of harvester 100, thus, harvesting logistics module 315 may have identified theoretical harvester material transfer start location 1820-4 with corresponding harvester material transfer completion location 1824-4 and theoretical receiving machine material transfer start location 1830-4 with corresponding theoretical receiving machine material transfer completion location 1834-4. However, given the location of constraint area 1851-1, harvesting logistics module 315 identifies start locations 1822-4 and 1832-4 and corresponding end locations 1826-4 and 1836-4. Additionally, there may be a constraint area 1851-2 and it may be preferable to prevent or limit material transfer at constraint area 1851-2 or to prevent travel of receiving machine 400- 1 at constraint area 1851-2 or to limit travel of receiving machine 400- 1 at constraint area 1851-2 to the greatest extent possible. Thus, harvesting logistics module 315 has identified a cluster of material transfer locations that prevents material transfer within constraint area 1851-2 and prevents travel of receiving machine 400-1 in constraint area 1851-2

[ 00229 ] It will be understood that as the operation commences and data at the field is collected and other data is revised or updated (e.g., maps 601, 358, 661 are updated), the cluster of locations can also be dynamically updated. As a simplified example, where the on-field yield is greater or less than the predicted yield, it may be that the start and completion locations need to be adjusted.

[ 00230 ] FIG. 9 is a flow diagram showing one example operation of agricultural harvesting system 500 in controlling an agricultural harvester 100 or a receiving machine 400, or both, in performing a harvesting operation.

[ 00231 ] At block 702 one or more maps are obtained by harvesting logistics module 315. As indicated by block 704, the maps may be one or more predictive maps, such as one or more predictive yield maps or predictive speed maps, or both, as well as various other predictive maps. The predictive yield maps and predictive speed maps may be functional predictive maps 263. As indicated by block 706, the one or more maps may include information maps, such as information maps 358. As indicated by block 709, the one or more maps may include various other maps.

[ 00232 ] At block 710 various other data are obtained by harvesting logistics module 315. For example, harvesting logistics module 315 can obtain one or more of the other data items illustrated in FIG. 7. As indicated by block 712, harvesting logistics module 315 can obtain agricultural harvester sensor data 604. As indicated by block 713, harvesting logistics module 315 can obtain agricultural harvester data 606. As indicated by block 714, harvesting logistics module 315 can obtain material transfer subsystem data 607. As indicated by block 715, harvesting logistics module 315 can obtain receiving machine sensor data 608. As indicated by block 716, harvesting logistics module 315 can obtain receiving machine data 610. As indicated by block 717, harvesting logistics module 315 can obtain harvester route data 612. As indicated by block 718, harvesting logistics module 315 can obtain operator data 614. As indicated by block 719, harvesting logistics module 315 can obtain various other data 616.

[ 00233 ] At block 720 harvesting logistics module 315 generates one or more logistics outputs 669. As indicated by block 722, material transfer point identifier logic 652 can generate, as a logistics output 669, one or more material transfer start locations, material transfer completion locations, a cluster of material transfer locations, predictive harvester fill levels, predictive harvester full locations, as well as various other outputs. As indicated by block 723, compaction constraints logic 628 can generate, as a logistics output 669, one or more compaction-based constraints, compaction constraints areas (or zones), as well as various other outputs. As indicated by block 724, field constraints logic 640 can generate, as a logistics output 669, one or more fieldbased constraints, field constraints areas (or zones), as well as various other outputs. As indicated by block 726, operator constraints logic 642 can generate, as a logistics output 669, one or more operator-based constraints, operator constraints areas (or zones), as well as various other outputs. As indicated by block 728, fill strategy logic 644 can generate, as a logistics output 669, a fill strategy, such an amount of material to transfer or a transfer location order (e.g., front-to-back, back-to-front, etc.), or both, as well as various other outputs. As indicated by block 730, location to complete logic 648 can generate, as a logistics output 669, one or more material transfer completion locations, as well as various other outputs. As indicated by block 732, route planning logic 650 can generate, as a logistics output 669, one or more routes, as well as various other outputs. As indicated by block 734, speed logic 652 can generate, as a logistics output 669, one or more speed characteristics values (e.g., travel speeds, accelerations, deceleration), as well as various other outputs. As indicated by block 736, display integration component 659 can generate, as a logistics output 669, one or more display elements or a map with integrated display elements. As indicated by block 738, map generator 660 can generate, as a logistics output 669, one or more maps, such as one or more harvesting logistics maps 661. As indicated by block 740, harvesting logistics module 315 can generate various other logistics outputs 669, for instance, other constraints logic 644 can generate other constraints or other constrain areas (or zones). Additionally, the logistics outputs 669 can be control outputs generated by control signal generator 635.

[ 00234 ] At block 742, control system 214 and/or control system 414 generate control signals based on the one or more logistics outputs 669. For example, as indicated by block 744, control system 214 can generate control signals to control one or more controllable subsystems 216 based on the one or more logistics outputs 669. Additionally, or alternatively, as indicated by block 744, control system 414 can generate control signals to control one or more controllable subsystems 416 based on the one or more logistics outputs 669. As indicated by block 746, control system 214 can generate control signals to control one or more interface mechanisms (e.g., 218 or 364) to generate displays, alerts, notifications, recommendations, as well as various other indications based on the one or more logistics outputs 669. Alternatively, or additionally, as indicated by block 746, control system 414 can generate control signals to control one or more interface mechanisms (e.g., 418 or 364) to generate displays, alerts, notifications, recommendations, as well as various other indications based on the one or more logistics outputs 669. As indicated by block 748, control system 214 and/or control system 414 can generate various other control signals based on the logistics outputs 669.

[ 00235 ] At block 750 it is determined if the harvesting operation is complete. If the harvesting operation has not been completed, operation returns to block 702. If the harvesting operation has been completed, then the operation ends.

[ 00236 ] The examples herein describe the generation of a predictive model and, in some examples, the generation of a functional predictive map based on the predictive model. The examples described herein are distinguished from other approaches by the use of a model which is at least one of multi-variate or site-specific (i.e., georeferenced, such as map-based). Furthermore, the model is revised as the work machine is performing an operation and while additional in-situ sensor data is collected. The model may also be applied in the future beyond the current worksite. For example, the model may form a baseline (e.g., starting point) for a subsequent operation at a different worksite or the same worksite at a future time.

[ 00237 ] The revision of the model in response to new data may employ machine learning methods. Without limitation, machine learning methods may include memory networks, Bayes systems, decisions trees, Eigenvectors, Eigenvalues and Machine Learning, Evolutionary and Genetic Algorithms, Cluster Analysis, Expert Systems/Rules, Support Vector Machines, Engines/Symbolic Reasoning, Generative Adversarial Networks (GANs), Graph Analytics and ML, Linear Regression, Logistic Regression, LSTMs and Recurrent Neural Networks (RNNSs), Convolutional Neural Networks (CNNs), MCMC, Random Forests, Reinforcement Learning or Reward-based machine learning. Learning may be supervised or unsupervised.

[00238 ] Model implementations may be mathematical, making use of mathematical equations, empirical correlations, statistics, tables, matrices, and the like. Other model implementations may rely more on symbols, knowledge bases, and logic such as rule-based systems. Some implementations are hybrid, utilizing both mathematics and logic. Some models may incorporate random, non-deterministic, or unpredictable elements. Some model implementations may make uses of networks of data values such as neural networks. These are just some examples of models.

[00239] The predictive paradigm examples described herein differ from non-predictive approaches where an actuator or other machine parameter is fixed at the time the machine, system, or component is designed, set once before the machine enters the worksite,, is reactively adjusted manually based on operator perception, or is reactively adjusted based on a sensor value.

[00240 ] The functional predictive map examples described herein also differ from other map-based approaches. In some examples of these other approaches, an a priori control map is used without any modification based on in-situ sensor data or else a difference determined between data from an in-situ sensor and a predictive map are used to calibrate the in-situ sensor. In some examples of the other approaches, sensor data may be mathematically combined with a priori data to generate control signals, but in a location-agnostic way; that is, an adjustment to an a priori, georeferenced predictive setting is applied independent of the location of the work machine at the worksite. The continued use or end of use of the adjustment, in the other approaches, is not dependent on the work machine being in a particular defined location or region within the worksite. [00241 ] In examples described herein, the functional predictive maps and predictive actuator control rely on obtained maps and in-situ data that are used to generate predictive models. The predictive models are then revised during the operation to generate revised functional predictive maps and revised actuator control. In some examples, the actuator control is provided based on functional predictive control zone maps which are also revised during the operation at the worksite. In some examples, the revisions (e.g., adjustments, calibrations, etc.) are tied to regions or zones of the worksite rather than to the whole worksite or some non-georeferenced condition. For example, the adjustments are applied to one or more areas of a worksite to which an adjustment is determined to be relevant (e.g., such as by satisfying one or more conditions which may result in application of an adjustment to one or more locations while not applying the adjustment to one or more other locations), as opposed to applying a change in a blanket way to every location in a non-selective way.

[00242 ] In some examples described herein, the models determine and apply those adjustments to selective portions or zones of the worksite based on a set of a priori data, which, in some instances, is multivariate in nature. For example, adjustments may, without limitation, be tied to defined portions of the worksite based on site-specific factors such as topography, soil type, crop variety, soil moisture, as well as various other factors, alone or in combination. Consequently, the adjustments are applied to the portions of the field in which the site-specific factors satisfy one or more criteria and not to other portions of the field where those site-specific factors do not satisfy the one or more criteria. Thus, in some examples described herein, the model generates a revised functional predictive map for at least the current location or zone, the unworked part of the worksite, or the whole worksite.

[ 00243 ] As an example, in which the adjustment is applied only to certain areas of the field, consider the following. The system may determine that a detected in-situ characteristic value varies from a predictive value of the characteristic such as by a threshold amount. This deviation may only be detected in areas of the field where the elevation of the worksite is above a certain level. Thus, the revision to the predictive value is only applied to other areas of the worksite having elevation above the certain level. In this simpler example, the predictive characteristic value and elevation at the point the deviation occurred and the detected characteristic value and elevation at the point the deviation crossed the threshold are used to generate a linear equation. The linear equation is used to adjust the predictive characteristic value in areas of the worksite not yet operated at (during the current operation) in the functional predictive map as a function of elevation and the predicted characteristic value. This results in a revised functional predictive map in which some values are adjusted while others remain unchanged based on selected criteria, e.g., elevation as well as threshold deviation. The revised functional map is then used to generate a revised functional control zone map for controlling the machine. [ 00244 ] As an example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows.

[00245] One or more maps of the field are obtained, such as one or more of a vegetative index map, a historical yield map, and another type of map.

[00246] In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ yield values.

[00247 ] A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive yield model.

[00248 ] A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive yield map that maps predictive yield values to one or more locations on the worksite based on a predictive yield model and the one or more obtained maps.

[00249] Control zones, which include machine settings values, can be incorporated into the functional predictive yield map to generate a functional predictive yield control zone map.

[00250 ] As another example, without limitation, consider an instance of the paradigm described herein which is parameterized as follows.

[00251 ] One or more maps of the field are obtained, such as one or more of a vegetative index map, a predictive yield map, a biomass map, a crop state map, a topographic map, a soil property map, a seeding map, and another type of map.

[00252 ] In-situ sensors generate sensor data indicative of in-situ characteristic values, such as in-situ speed characteristic values.

[00253] A predictive model generator generates one or more predictive models based on the one or more obtained maps and the in-situ sensor data, such as a predictive speed model.

[00254 ] A predictive map generator generates one or more functional predictive maps based on a model generated by the predictive model generator and the one or more obtained maps. For example, the predictive map generator may generate a functional predictive speed map that maps predictive speed characteristic values to one or more locations on the worksite based on a predictive speed model and the one or more obtained maps.

[ 00255 ] Control zones, which include machine settings values, can be incorporated into the functional predictive speed map to generate a functional predictive speed control zone map. [00256] As the mobile machine continues to operate at the worksite, additional in-situ sensor data is collected. A learning trigger criteria can be detected, such as threshold amount of additional in-situ sensor data being collected, a magnitude of change in a relationship (e.g., the in- situ characteristic values varies to a certain [e.g., threshold] degree from a predictive value of the characteristic), and operator or user makes edits to the predictive map(s) or to a control algorithm, or both, a certain (e.g., threshold) amount of time elapses, as well as various other learning trigger criteria. The predictive model(s) are then revised based on the additional in-situ sensor data and the values from the obtained maps. The functional predictive maps or the functional predictive control zone maps, or both, are then revised based on the revised model(s) and the values in the obtained maps.

[00257 ] The present discussion has mentioned processors and servers. In some examples, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. They are functional parts of the systems or devices to which they belong and are activated by and facilitate the functionality of the other components or items in those systems.

[00258 ] Also, a number of user interface displays have been discussed. The displays can take a wide variety of different forms and can have a wide variety of different user actuatable operator interface mechanisms disposed thereon. For instance, user actuatable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. The user actuatable operator interface mechanisms can also be actuated in a wide variety of different ways. For instance, they can be actuated using operator interface mechanisms such as a point and click device, such as a track ball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc., a virtual keyboard or other virtual actuators. In addition, where the screen on which the user actuatable operator interface mechanisms are displayed is a touch sensitive screen, the user actuatable operator interface mechanisms can be actuated using touch gestures. Also, user actuatable operator interface mechanisms can be actuated using speech commands using speech recognition functionality. Speech recognition may be implemented using a speech detection device, such as a microphone, and software that functions to recognize detected speech and execute commands based on the received speech.

[00259] A number of data stores have also been discussed. It will be noted the data stores can each be broken into multiple data stores. In some examples, one or more of the data stores may be local to the systems accessing the data stores, one or more of the data stores may all be located remote form a system utilizing the data store, or one or more data stores may be local while others are remote. All of these configurations are contemplated by the present disclosure.

[ 00260 ] Also, the figures show a number of blocks with functionality ascribed to each block.

It will be noted that fewer blocks can be used to illustrate that the functionality ascribed to multiple different blocks is performed by fewer components. Also, more blocks can be used illustrating that the functionality may be distributed among more components. In different examples, some functionality may be added, and some may be removed.

[ 00261 ] It will be noted that the above discussion has described a variety of different systems, components, logic, modules, generators, and interactions. It will be appreciated that any or all of such systems, components, logic, modules, generators, and interactions may be implemented by hardware items, such as one or more processors, one or more processors executing computer executable instructions stored in memory, memory, or other processing components, some of which are described below, that perform the functions associated with those systems, components, logic, modules, generators, or interactions. In addition, any or all of the systems, components, logic, modules, generators, and interactions may be implemented by software that is loaded into a memory and is subsequently executed by one or more processors or one or more servers or other computing component(s), as described below. Any or all of the systems, components, logic, modules, generators, and interactions may also be implemented by different combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that may be used to implement any or all of the systems, components, logic, modules, generators, and interactions described above. Other structures may be used as well.

[00262 ] FIG. 10 is a block diagram of agricultural harvester 1000, which may be similar to agricultural harvester 100 shown in FIG. 3, receiving machine 4000, which may be similar to receiving machine 400 shown in FIG. 3, and remote computing systems 3000, which may be similar to remote computing systems 300 shown in FIG. 3. The agricultural harvester 1000, receiving machine 4000, and remote computing system 3000 communicates with elements in a remote server architecture 900. In some examples, remote server architecture 900 provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various examples, remote servers may deliver the services over a wide area network, such as the internet, using appropriate protocols. For instance, remote servers may deliver applications over a wide area network and may be accessible through a web browser or any other computing component. Software or components shown in FIG. 3 as well as data associated therewith, may be stored on servers at a remote location. The computing resources in a remote server environment may be consolidated at a remote data center location, or the computing resources may be dispersed to a plurality of remote data centers. Remote server infrastructures may deliver services through shared data centers, even though the services appear as a single point of access for the user. Thus, the components and functions described herein may be provided from a remote server at a remote location using a remote server architecture. Alternatively, the components and functions may be provided from a server, or the components and functions can be installed on client devices directly, or in other ways.

[ 00263 ] In the example shown in FIG. 10, some items are similar to those shown in FIG. 3 and those items are similarly numbered. FIG. 10 specifically shows that predictive model generator 310, predictive map generator 312, and harvesting logistics module 315 may be located at a server location 902 that is remote from the agricultural harvester 1000, the receiving machine 2000, and the remote computing systems 3000. Therefore, in the example shown in FIG. 10, agricultural harvester 1000, receiving machine 4000, and remote computing systems 3000 accesses systems through remote server location 902. In other examples, various other items may also be located at server location 902, such as predictive model 311, functional predictive maps 263 (including predictive maps 264 and predictive control zone maps 265), control zone generator 313, and processing system 338.

[00264 ] FIG. 10 also depicts another example of a remote server architecture. FIG. 10 shows that some elements of FIG. 3 may be disposed at a remote server location 902 while others may be located elsewhere. By way of example, one or more of data store(s) 204, 304, and 404 may be disposed at a location separate from location 902 and accessed via the remote server at location 902. Regardless of where the elements are located, the elements can be accessed directly by agricultural harvester 1000, receiving machine 4000, and remote computing systems 3000 through a network such as a wide area network or a local area network; the elements can be hosted at a remote site by a service; or the elements can be provided as a service or accessed by a connection service that resides in a remote location. Also, data may be stored in any location, and the stored data may be accessed by, or forwarded to, operators, users or systems. For instance, physical carriers may be used instead of, or in addition to, electromagnetic wave carriers. In some examples, where wireless telecommunication service coverage is poor or nonexistent, another machine, such as a fuel truck or other mobile machine or vehicle, may have an automated, semi-automated or manual information collection system. As the agricultural harvester 1000 or receiving machine 4000, or both, comes close to the machine containing the information collection system, such as a fuel truck prior to fueling, the information collection system collects the information from the agricultural harvester 1000 or the receiving machine 4000, or both, using any type of ad-hoc wireless connection. The collected information may then be forwarded to another network when the machine containing the received information reaches a location where wireless telecommunication service coverage or other wireless coverage- is available. For instance, a fuel truck may enter an area having wireless communication coverage when traveling to a location to fuel other machines or when at a main fuel storage location. All of these architectures are contemplated herein. Further, the information may be stored on the agricultural harvester 1000 or the receiving machine 4000, or both, until the agricultural harvester 1000 or the receiving machine 4000, or both, enters an area having wireless communication coverage. The agricultural harvester 1000, itself, may send the information to another network. The receiving machine 4000, itself, may send the information to another network.

[00265] It will also be noted that the elements of FIG. 3, or portions thereof, may be disposed on a wide variety of different devices. One or more of those devices may include an onboard computer, an electronic control unit, a display unit, a server, a desktop computer, a laptop computer, a tablet computer, or other mobile device, such as a palm top computer, a cell phone, a smart phone, a multimedia player, a personal digital assistant, etc.

[ 00266 ] In some examples, remote server architecture 902 may include cybersecurity measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of people or processes accessing data, as well as the use of ledgers for recording metadata, data, data transfers, data accesses, and data transformations. In some examples, the ledgers may be distributed and immutable (e.g., implemented as blockchain).

[ 00267 ] FIG. 11 is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a user’s or client’s handheld device 16, in which the present system (or parts of it) can be deployed. For instance, a mobile device can be deployed in the operator compartment of agricultural harvester 100 or receiving machine 400, or both, for use in generating, processing, or displaying the maps discussed above. FIGS. 12-13 are examples of handheld or mobile devices.

[00268 ] FIG. 11 provides a general block diagram of the components of a client device 16 that can run some components shown in FIG. 3, that interacts with them, or both. In the device 16, a communications link 13 is provided that allows the handheld device to communicate with other computing devices and under some examples provides a channel for receiving information automatically, such as by scanning. Examples of communications link 13 include allowing communication though one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks.

[ 00269 ] In other examples, applications can be received on a removable Secure Digital (SD) card that is connected to an interface 15. Interface 15 and communication links 13 communicate with a processor 17 (which can also embody processors or servers from other FIGS.) along a bus 19 that is also connected to memory 21 and input/output (I/O) components 23, as well as clock 25 and location system 27.

[ 00270 ] I/O components 23, in one example, are provided to facilitate input and output operations. I/O components 23 for various examples of the device 16 can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I/O components 23 can be used as well.

[ 00271 ] Clock 25 illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor 17.

[ 00272 ] Location system 27 illustratively includes a component that outputs a current geographical location of device 16. This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. Location system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions. [ 00273 ] Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data store 37, communication drivers 39, and communication configuration settings 41. Memory 21 can include all types of tangible volatile and non-volatile computer-readable memory devices. Memory 21 may also include computer storage media (described below). Memory 21 stores computer readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions according to the instructions. Processor 17 may be activated by other components to facilitate their functionality as well.

[ 00274 ] FIG. 12 shows one example in which device 16 is a tablet computer 1100. In FIG. 12, computer 1100 is shown with user interface display screen 1102. Screen 1102 can be a touch screen or a pen-enabled interface that receives inputs from a pen or stylus. Tablet computer 1100 may also use an on-screen virtual keyboard. Of course, computer 1100 might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance. Computer 1100 may also illustratively receive voice inputs as well. [ 00275 ] FIG. 13 is similar to FIG. 12 except that the device is a smart phone 71. Smart phone 71 has a touch sensitive display 73 that displays icons or tiles or other user input mechanisms 75. Mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, smart phone 71 is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone.

[ 00276 ] Note that other forms of the devices 16 are possible.

[ 00277 ] FIG. 14 is one example of a computing environment in which elements of previous figures described herein can be deployed. With reference to FIG. 14, an example system for implementing some embodiments includes a computing device in the form of a computer 1210 programmed to operate as discussed above. Components of computer 1210 may include, but are not limited to, a processing unit 1220 (which can comprise processors or servers from previous FIGS.), a system memory 1230, and a system bus 1221 that couples various system components including the system memory to the processing unit 1220. The system bus 1221 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Memory and programs described with respect to previous figures described herein can be deployed in corresponding portions of FIG. 14. [ 00278 ] Computer 1210 typically includes a variety of computer readable media. Computer readable media may be any available media that can be accessed by computer 1210 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. Computer readable media includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 1210. Communication media may embody computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[ 00279 ] The system memory 1230 includes computer storage media in the form of volatile and/or nonvolatile memory or both such as read only memory (ROM) 1231 and random access memory (RAM) 1232. A basic input/output system 1233 (BIOS), containing the basic routines that help to transfer information between elements within computer 1210, such as during start-up, is typically stored in ROM 1231. RAM 1232 typically contains data or program modules or both that are immediately accessible to and/or presently being operated on by processing unit 1220. By way of example, and not limitation, FIG. 14 illustrates operating system 1234, application programs 1235, other program modules 1236, and program data 1237.

[ 00280 ] The computer 1210 may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only, FIG. 14 illustrates a hard disk drive 1241 that reads from or writes to non-removable, nonvolatile magnetic media, an optical disk drive 1255, and nonvolatile optical disk 1256. The hard disk drive 1241 is typically connected to the system bus 1221 through a non -removable memory interface such as interface 1240, and optical disk drive 1255 are typically connected to the system bus 1221 by a removable memory interface, such as interface 1250.

[ 00281 ] Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (e.g., ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[ 00282 ] The drives and their associated computer storage media discussed above and illustrated in FIG. 14, provide storage of computer readable instructions, data structures, program modules and other data for the computer 1210. In FIG. 14, for example, hard disk drive 1241 is illustrated as storing operating system 1244, application programs 1245, other program modules 1246, and program data 1247. Note that these components can either be the same as or different from operating system 1234, application programs 1235, other program modules 1236, and program data 1237.

[ 00283 ] A user may enter commands and information into the computer 1210 through input devices such as a keyboard 1262, a microphone 1263, and a pointing device 1261, such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 1220 through a user input interface 1260 that is coupled to the system bus, but may be connected by other interface and bus structures. A visual display 1291 or other type of display device is also connected to the system bus 1221 via an interface, such as a video interface 1290. In addition to the monitor, computers may also include other peripheral output devices such as speakers 1297 and printer 1296, which may be connected through an output peripheral interface 1295.

[ 00284 ] The computer 1210 is operated in a networked environment using logical connections (such as a controller area network - CAN, local area network - LAN, or wide area network WAN) to one or more remote computers, such as a remote computer 1280.

[00285] When used in a LAN networking environment, the computer 1210 is connected to the LAN 1271 through a network interface or adapter 1270. When used in a WAN networking environment, the computer 1210 typically includes a modem 1272 or other means for establishing communications over the WAN 1273, such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device. FIG. 14 illustrates, for example, that remote application programs 1285 can reside on remote computer 1280. [00286] It should also be noted that the different examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is contemplated herein. [00287 ] Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of the claims.