CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. provisional application number 62/236,762, filed October 2, 2015 entitled "AGRICULTURE TELEMETRY UNITS AND SYSTEMS AND METHODS FOR PROVIDING A PLATFORM FOR MANAGING AND CONTROLLING THE OPERATION OF THE UNITS AND AGRICULTURE SYSTEMS" and U.S. provisional application number 62/246,034, filed October 24, 2015, entitled "SYSTEMS AND METHODS FOR PROVIDING A PLATFORM FOR MANAGING AND CONTROLLING THE OPERATION OF AGRICULTURE TELEMETRY UNITS AND AGRICULTURE SYSTEMS" which are both incorporated by reference herein.

FIELD OF INVENTION

[0002] The present invention relates to a system and method for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems.

BACKGROUND OF INVENTION

[0003] Over the years, the agricultural industry has gradually embraced technology in order to improve overall farming operation and reduce inputs to be more efficient. Remote data monitoring is an example of such technology that is used to increase farming productivity and reduce costs. Currently, remote

monitoring and control solutions fall into two categories. The first category is a system in which a single specific sensor is used to monitor agricultural and other conditions on a farming property. Because of the limited functionality, this system does not provide sufficient data to enable a farmer to make decisions to improve on the overall operation of his/her farm.

[0004] The second category is a system in which a variety of sensors are used to enable a farmer to monitor large amounts of data. This system, however, has significant drawbacks. For one thing, the system requires a skilled technician to properly install, program and configure the agricultural telemetry units (ATU) and other components. This comes at substantial cost to the farmer. In addition, this process is quite time consuming for the following reason. The technician must install the sensors and ATU, test the connections for accuracy, program (write code) each ATU to (1 ) designate sensor power and sensor signal interpretation and (2) identify the farmer's account, frequency of and location for reporting. Once installed, the technician or farmer must configure the software to record and associate all monitored data to one or more of the farmer's properties, sensors and graphs. Now, if an ATU has an issue in the field, a technician must make an in-field visit to diagnose and repair the ATU as needed. Not only is the service call quite costly to the farmer (e.g., $300), a non-functioning ATU may delay operations or lead to poor and improper farming decisions. This could easily jeopardize the agriculture product over an extended period of time.

[0005] Now, once the system is ready for use, the farmer may decide to use data in the form of graphs. The current systems, however, simply plot the data collected in a line graph for each sensor. For example, if a farmer sets one station (of many on a section of a property) with a soil moisture/soil temperature sensor, water pressure sensor and air temperature, the system would populate fourteen lines of graph or fourteen individual graphs. If a farmer has ten stations on his/her 1000 acre property, the farmer must review and analyze 120 graphs just to make basic farming decisions on his/her property. This requires a farmer or service provider to review multiple graphs to identify relationships between line graphs, different sensors and physical locations of these sensors as well as correlations and trends of the data. This process is both frustrating and time consuming to the farmer.

[0006] In short, these processes described above involve substantial specialized expertise, expense and time thereby leading to a reduced return on investment (ROI) or even negative return.

SUMMARY OF THE INVENTION

[0007] Embodiments of a system and method for a system and method for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems are disclosed.

[0008] In accordance with an embodiment of this disclosure, an a system is disclosed for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems, wherein the system including agricultural telemetry units (ATUs) and agriculture systems or devices on a user's property, wherein the ATUs are configured as nodes that communicate with a central system over a network, the system comprising: a data storage area to store: a sensor readings database, wherein sensor data retrieved from a plurality of sensors, coupled to one or more agriculture telemetry units (ATUs), are stored; an indicia database, wherein indicia indicating states of the sensor data are stored; and one or more servers coupled to the data storage area, wherein the one or more servers are programmed to execute computer program modules, the computer program modules comprising: a sensor data processing engine for processing sensor data with respect to an indicia from an indicia database; and a layering engine for layering the indicia over a map of the user's property.

[0009] In accordance with yet another embodiment of the disclosure, a method is disclosed of efficient installation, configuration and management of agricultural telemetry units (ATUs) and agricultural systems on a user's property, wherein the ATUs are configured as nodes to communicate with a central system over a network, the method comprising: connecting a first end of an adapter cable to a sensor and second of the adapter cable to an agricultural telemetry unit (ATU); retrieving, by the ATU, a sensor ID associated with the sensor from the adapter cable; searching for a matching ID in a sensor ID database that matches the sensor ID associated with the sensor; searching for a driver from a driver database that is associated with the sensor ID, if the sensor ID matches an ID stored in the sensor ID database; and retrieving a driver from the driver database associated with the sensor ID.

[0010] In accordance with yet another embodiment of the disclosure, an adapter cable for use in a system for rapid installation, configuration and management of agricultural telemetry units (ATUs) on a user's property that are configured to wirelessly communicate with a central system over a network, the adapter cable comprising: a first connector configured to connect a first end of the adapter cable to a sensor and a second connector configured to connect a second end of the adapter cable to an agricultural telemetry unit; and memory for storing a sensor identification (IDS) that is associated with a sensor sensing data parameters relating to one or more agricultural conditions.

[0011] In accordance with yet another embodiment of the disclosure, a system efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems, wherein the system including agricultural telemetry units (ATUs) on a user's property, wherein the ATUs are configured as nodes in a network that communicate with a central system over the network, the central system including one or more servers and a display, the one or more servers comprising a memory for storing instructions and a processor for executing the instructions to: retrieve a map of the user's property; receive, from a plurality of sensors over the network, first data relating to one or more agricultural conditions that may affect agriculture on the user's property; receive a user selection of indicia representing the first data; process the first data with respect to the indicia; receive a user request to display indicia relating to the first data over the map; display the map of user's property on the display; and overlay, on the display, the indicia relating to the first data as a layer over the map.

[0012] In yet another embodiment of the disclosure, a system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems, wherein the system including agricultural telemetry units (ATUs) and agriculture systems or devices on a user's property, wherein the ATUs are configured as nodes that communicate with a central system over a network, the system comprising: a data storage area to store: a sensor readings database, wherein sensor data retrieved from a plurality of sensors, coupled to one or more agriculture telemetry units (ATUs), are stored; an indicia database, wherein indicia indicating states of the sensor data are stored; and one or more servers coupled to the data storage area, wherein the one or more servers are programmed to execute computer program modules, the computer program modules comprising: a sensor data processing engine for processing sensor data with respect to an indicia from an indicia database; and a layering engine for layering the indicia over a map of the user's property.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Embodiments of the present disclosure are described herein with reference to the drawing figures.

[0014] Fig. 1 depicts a perspective view of an example agriculture telemetry unit (ATU) used in a system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems.

[0015] Fig. 2 depicts a perspective view of an example environment in which an example system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems operates. [0016] Fig. 3 depicts an example wireless system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems wherein several agricultural telemetry units are shown along with clients and a central system.

[0017] Fig. 4 depicts an example agriculture telemetry unit in an exploded configuration illustrating the components therein.

[0018] Figs. 5A-5B depict hardware and software block diagrams of an example control unit of the agriculture telemetry unit of Fig. 4, respectively.

[0019] Fig. 6 depicts two example adapter cables connecting two example sensors with the control unit in Figs. 5A and 5B.

[0020] Fig. 7 depicts two example sensors connected directly with the control unit in Fig. 5A and 5B.

[0021] Fig. 8 depicts example method steps for setting up a network of agriculture telemetry units (by sensor ID) along with sensors for subsequent data collection, transmission and subsequent control of such units and agriculture systems.

[0022] Fig. 9 depicts another example method steps for setting up a network of agriculture telemetry units along with their sensors for subsequent data collection, transmission and subsequent control of such units and agriculture systems.

[0023] Fig. 10 depicts example method steps for actual sensor data collection, transmission and subsequent control performed by the control unit in Figs. 4 and 5.

[0024] Fig. 1 1 depicts a high-level diagram illustrating different data retrieved from several sources that are used for layering on a map.

[0025] Fig. 12 depicts a central system of a wireless system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems.

[0026] Fig. 13 depicts example method steps for processing and selectively displaying data layers as desired.

[0027] Fig. 14 is an example of the layering in expanded form.

[0028] Fig. 15 depicts a diagram of an example screen shot wherein indicia calibration is performed.

[0029] Figs. 16 depicts a diagram of an example map of a grower's property (layer) along with soil moisture indicia and other data layering as described above [0030] Figs. 17-18 depict example map of a grower's property (layer) along with soil moisture indicia and other data layering as described above.

[0031] Fig. 19 depicts screen shot of a map layer of another grower's property along with one or more layers of data.

[0032] Fig. 20 depicts a diagram of another example map layer (on a screen) of a grower's property along with soil moisture indicia and other data layering.

[0033] Fig. 21 a general-purpose computer to support the embodiments of the computer-implemented systems and methods including computer components disclosed in this application.

[0034] Fig. 22 depicts a perspective view of an example agriculture telemetry unit (ATU) used in a system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems.

DETAILED DESCRIPTION OF THE INVENTION

[0035] Embodiments of the present disclosure are described herein with reference to the drawing figures.

[0036] Fig. 1 depicts a perspective view of example agriculture telemetry unit (ATU) 100 used in a system 200 for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems (also referred to as agriculture telemetry system 200 or system 200 described herein) with respect to Figs. 2 and 3. In brief, system 200 enables a user to quickly deploy, i.e., install, configure, manage/control the operation of one or more ATUs and other agriculture systems. (In brief, system 200 is configured to, among other things, receive and process agricultural sensor data for subsequent viewing and analysis by a user.) ATU 100 incorporates one or more sensors. The sensors may be located on the ATU or remote from it as described below. These sensors, as known to those skilled in the art, are designed to sense data parameters (also referred to as readings) that relate to or have an affect on agriculture growth (and ultimately production). That is, the data parameters may relate or lead to an agriculture condition. Examples of the condition over-irrigated, under irrigated crops. Examples of sensors include temperature sensors, soil sensors and wind sensors to name a few. Soil sensors may include soil moisture sensors and other sensors for sensing characteristics of the soil. HSTI, Aquacheck, Decagon and Davis Instruments make a variety of sensors that can be used with system 200. There are others sensor options that may be used as known to those skilled in the art.

[0037] ATU 100 is constructed to be implanted into the soil near a sensor or agriculture system to enable the sensors to sense and record data associated with the soil (e.g., moisture content). An agriculture system (also referred to as agriculture device) includes an irrigation system or device and any other system or device in the field that can be monitored or controlled including pumps (or other input/output devices known to skilled in the art), valves (or other activators known to those skilled in the art), chemical or fertilizer injection and center pivots (to name a few). In short, ATU 100 is one of several ATUs that are incorporated within wireless agriculture telemetry system 200 (below). In brief, wireless system 200 is used to collect sensor data remotely from infield ATUs wirelessly (i.e., wireless communication, e.g., radio frequency (RF), WIFI, satellite, cellular, and spread spectrum to name a few) to avoid manual data collection. Agriculture telemetry system 200 as described herein uses the collected sensor data for a variety of purposes including sensor settings and irrigation control. This is described in more detail below. The sensor data may also be referred to as sensor data readings or sensor readings in this disclosure.

[0038] Fig. 2 depicts a perspective view of an example environment in which example system 200 operates. System 200 incorporates several ATUs 202-224 (each of which is the same as ATU 100 shown in Fig. 1 ). These ATUs are scattered across a grower's property (e.g., ranch or field). The property is broken into several blocks, each comprising one or more lots. A grower will typically define the blocks and lots by the terrain (soil type) and particular crop. In Fig. 2, each ATU is installed in a block of agriculture land. Each land block is shown separated by dashed lines in Fig. 2. As discussed in more detail below, ATUs 202-224 work together as a computer network (also referred to as a mesh network) wherein ATU 202 functions as a gateway (also referred to as a base unit, master, gateway or primary node) while the remaining ATUs 204-224 function as nodes in that network.

[0039] In this embodiment, ATU 202 incorporates cellular (communication) technology to enable ATU 202 (base unit) to communicate with central system 226 directly as known to those skilled in the art. ATU 202 as well as ATUs 204-224 incorporate wireless technology such as RF, WIFI or other communication technology to enable (1 ) wireless communication between individual ATUs and (2) communication between node ATUs 204-224 and gateway ATU 202 as known to those skilled in the art. In this embodiment, sensor data collected by the ATUs 204- 224 (nodes) hop between adjacent ATUs (nodes) until such sensor data reaches the gateway ATU 202. In other embodiments, ATUs 204-224 may also incorporate cellular (communication) technology to enable ATUs 204-224 to communicate directly with the gateway unit ATU 202 or the remote system 226 (below) via one or more cellular tower(s) 228. The dashed lines depict example communication paths. GPS may also be employed to enable location identification of an ATU or client (user). A user may communicate with an ATU via client 230 (e.g., mobile device) or client 232 (e.g., computer with monitor) wirelessly (as described below) as known those skilled in the art. Clients 230, 232 may be used to program and ultimately control ATUs 202-224 as disclosed herein.

[0040] Fig. 3 depicts example wireless system 300 for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems (also referred to as agriculture telemetry system) wherein agricultural telemetry units (ATUs) 302, 306, 310 are shown along with clients 314, 316 and central system 318. In this embodiment, ATUs 306, 310 function as a network as described above wherein ATUs 306, 310 wirelessly communicate with ATU 302 directly (e.g., RF, WIFI) while ATU 302 communicates wirelessly (e.g., cellular) to central system 318 via network 320. However, ATUs 306, 310 may communicate wirelessly (e.g., cellular) with central system 318 over network 320 in other embodiments. Alternatively, the sensors may connect wirelessly to an ATU or network 320.

[0041] Client 314 may wirelessly connect to ATU 302 as shown (solid line) or to network 320 (dashed line). (The connections are both wireless communication paths). ATU 302 actually incorporates sensors 304 within it as shown. ATUs 306, 310, however, are connected to and separate from sensors 308, 312, respectively. As discussed above, these sensors, as known to those skilled in the art, are designed to sense data parameters (also referred to as readings) that relate to or have an affect on agriculture growth (and ultimately production). Examples of data parameters include temperature, soil moisture and wind. [0042] In Fig. 3, three ATUs are shown and described herein. However, those skilled in the art know that any number of ATUs may be employed as needed.

System 300 also includes other agriculture systems 322, 324 that are connected via wired connection to ATUs 306, 310, respectively (or alternatively via wireless connection). Agriculture systems 322, 324 are under the control of the platform for managing and controlling the operation of the agriculture telemetry units and agriculture systems as described herein.

[0043] System 300 is similar to system 200 including corresponding labeled components, but they are renumbered (and in block diagram format). Clients 314, 316 each may be a personal computer and a monitor or mobile devices such as smartphones, cellular telephones, tablets, PDAs, or other devices equipped with industry standard (e.g., HTML, HTTP etc.) browsers or any other application having wired (e.g., Ethernet) or wireless access (e.g., cellular, Bluetooth, RF, WIFI such as IEEE 802.1 1 b etc.) via networking (e.g., TCP/IP) to nearby and/or remote

computers, peripherals, and appliances, etc. TCP/IP (transfer control

protocol/Internet protocol) is the most common means of communication today between clients or between clients and systems (servers), each client having an internal TCP/IP/hardware protocol stack, where the "hardware" portion of the protocol stack could be Ethernet, Token Ring, Bluetooth, IEEE 802. lib, or whatever software protocol is needed to facilitate the transfer of IP packets over a local area network. Clients 314, 316 communicate with ATUs 302, 306, 310 and central system 318 over network 320. Network 320 may be the Internet, local area network (LAN) or other network known to those skilled in the art.

[0044] As described in more detail below, central system 318 includes one or more servers 318-1. The one or more servers may include a web server. Each server includes several internal components (e.g., processor, memory, drives, etc.), databases, software modules and applications (e.g., browser) as known to those skilled in the art. These servers may incorporate part of or all of the platform for managing and controlling the operation of ATUs and other agriculture systems as described above. (As indicated above, central system 318 is configured to, among other things, receive and process agricultural sensor data for subsequent viewing and analysis by a user.) Central system 318 is accessible by clients 314, 316 via website or dedicated application. [0045] Fig. 4 depicts example ATU 400 in an exploded configuration illustrating the components therein. ATU 400 is the same as ATU 100 in Fig. 1 (but

renumbered). In particular, ATU 400 includes antenna 402, antenna connector 404, solar cap 406, main cylinder body 408, control unit 410, cylinder seal 412, end cone 414 and pole (or lower) assembly 416. In the embodiment shown, control unit 410 includes a number of components that are electrically connected to a circuit board as known to those skilled in the art.

[0046] In assembled form, solar cap 406 is mounted over the top cylinder body 408 to create a seal. Solar cap 406 incorporates a small solar panel 406-1 that is connected to control unit 410 by wire as known to those skilled in the art for providing power to the ATU 400. Control unit 410 thereby provides power to the sensors as described below with respect to Fig. 6. Antenna 402 is mounted to solar cap 406 via connector 404, and the antenna 402 is also coupled to control unit 410 by cable through connector 404 as known to those skilled in the art. Control unit 410 includes two cables extending therefrom, each cable 402-1 , 401 -2 having a plug that fits into a hole within cylinder seal 412 (thereby creating a seal themselves). These cables are represented or identified by dashed lines (labeled "A" in Fig. 4). In other examples, control unit 410 may comprise only one cable or more than two cables as known to those skilled in the art.

[0047] In one embodiment, adapter cables are used to connect the sensors to the plugs of cables 410-A, 410-B as shown in Fig. 6. In this respect sensor setup is described below with respect to Fig. 8. In another embodiment, the sensors are connected (i.e., coupled) directly to these plugs using standard cables (shown in Fig. 7). In this respect, sensor setup is performed as described in Fig. 9. Antenna 402, antenna connector 404, solar cap 406, main cylinder body 408, control unit 410, cylinder seal 412, end cone 414 (and adapter cables) in combination is referred to as an upper assembly (or alternatively node assembly).

[0048] Lower or pole assembly 416 includes an upper segment 416-1 , a middle segment 416-2 and a lower segment 416-3. Upper segment has opposing threaded ends, one of which is threaded within the bottom of end cone 414 while the other is threaded within the end of middle segment 416-2. The opposing end of middle segment 416-2 is threaded onto lower segment 416-3. Middle segment 416-2 comprises an opening to enable sensor cables to reach one or more sensors attached to their ends (within the soil). Lower segment 416-3 extends into cement and/or soil to stabilize and prevent ATU 400 from unintended movement as known to those skilled in the art.

[0049] Figs. 5A-5B depict the hardware and software components, respectively of control unit 410 of example agriculture telemetry unit 400 (Fig. 4). ATU 400 has the same components as ATU 100 including control unit 410. As shown in Fig. 5A, control unit 410 includes at least one processor 410-1 and system memory 410-2 (e.g., volatile - RAM or non-volatile - Flash or ROM). System memory 410-2 may include computer readable media that is accessible to the processor 410-1 . The memory 410-2 may also include instructions for processor 410-1 , an operating system (described below) and one or more application/platform including a part of software modules or one or more software applications (e.g., the method steps herein).

[0050] Control unit 410 further includes storage 410-3 such as a hard drive or SSDs for storing data such as ID an database, driver database, program data (e.g. ATU settings data, sensor readings database) and other software described above (described below). Control unit 410 further includes wireless communication(s) unit 410-4 that enables control unit 410 to communicate wirelessly with a computer or mobile device directly or over a network. Wireless communications unit 410-4 incorporates technology known to those skilled in the art that enables control unit 410 to communicate via Bluetooth, cellular, or WIFI as desired. In addition, wireless communication unit 410-4 may also include a GPS chip to enable location based tracking as known to those skilled in the art.

[0051] Control unit 410 further includes one or more communication connections such as interface 410-5. Interface 410-5 is configured to interface with cables 410-A, 410-B to connect with the appropriate sensors (via adapter cables) as discussed below. Interface 410-5 may also provide connection points to enable control unit 410 to communicate with a computer or mobile device directly by wire/cable Control unit 410 also includes accelerometer 410-6 to detect vibrations or movement of ATU 400. For example, a tractor may hit an ATU causing it to vibrate and move. Control unit 410 may also include a video card or other technology to enable direct access to a display and other components known to those skilled in the art. Control unit 410 also includes a battery for power and solar circuitry 410-12 connected to the solar cap 406 for alternate power.

[0052] In Fig. 5B, control unit 410 incorporates operating system 410-8 may be Linux or a version of Microsoft Windows, Macintosh OSX or other operating system (including those specially programmed, e.g. using Python or C++, C for the application disclosed herein) that includes the TCP/IP protocol stack for

communication over the Internet as known to those skilled in the art. Further control unit 410 includes program data 410-9 (e.g., settings data, sensor readings database, etc.), sensor ID database 410-10 and driver database 410-1 1. Sensor ID database 410-10 is a database of IDs (identifications) associated with a number of sensors. Driver database 410-1 1 is a database of drivers used to enable control unit 410 to communicate with selected sensors. The ID and drivers are described in more detail below. The databases used herein are structures for such data but the data may be stored in other formats as known to those skilled in the art. Control unit 410 further includes one or more applications 410-12 (e.g., modules or applications that perform parts of the system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems described herein (e.g., the method steps described herein)).

[0053] Fig. 6 depicts example adapter cables 600, 602 for connecting example sensors 604, 606 with control unit 410 in Fig. 5A (and 5B) though cables 410-A, 410- B respectively. Adapter cables 600, 602 include connectors 600-1 , 602-1 on one end thereof, respectively. These connectors are used connect directly to the plugs on cables 410-A, 410-B. (In alternate embodiments, however, connectors 600-1 , 602-1 may be configured to connect directly to the control unit (e.g., circuit board) as shown in Fig. 7 and described below). Adapter cables 600, 602 include plugs on the opposing ends thereof for connecting to the plugs on sensors 604, 606, respectively as known to those skilled in the art. This connection is represented as a circle (labeled "A"). Connectors 600-1 , 602-1 of adapter cables 600, 602, incorporate (integrate) memory 600-2, 602-2, respectively for storing an identification (ID) or code representing a particular sensor. Memory 600-2, 602-2 maybe take many forms including a memory chip, ID chip or other component for storing the ID as known to those skilled in the art. This ID will be used to identify the appropriate driver for a sensor associated with that ID to enable communication with the sensor. [0054] A driver is a program that controls a particular device as known to those skilled in the art. In this instance, the device is a sensor or other agriculture system. The driver acts like a translator between the control unit 410 and the sensor. The driver is specifically programmed (configured) based on the codes/parameters provided by the manufacturer of its sensor. The codes/parameters function as instructions (or roadmap) on how to communicate with the sensor in order to take accurate readings from it (e.g., proper pins for communication, type of

communication protocol, energized/excitement time and voltage, algorithm to convert the reading into actionable data, e.g., from voltage to a degree or pressure). The driver database includes several drivers that may be selected and used with a number of sensors. (These drivers may be software or part of the firmware as known to those skilled in the art.) Setup and operation of the embodiment in Fig. 6 are described in more detail below with respect to Figs. 8 and 10.

[0055] Fig. 7 depicts two example sensors connected directly with control unit 410 in Fig. 5A. This is an alternative embodiment to that shown in Fig. 6. In this embodiment, the plugs on the distal ends of cables 410-A, 410-B are coupled to the plugs on the cables of sensors 700, 702, respectively. The connection point is represented as a circle (and identified as "A"). The drivers described above are also used in this embodiment. Setup and operation of the embodiment in Fig. 7 are described in more detail below with respect to Figs. 9 and 10.

[0056] Reference is now made to Fig. 8. Fig. 8 depicts example method steps for setting up a network of ATUs along with their sensors. (These method steps involve the embodiment shown in Fig. 6.) These example method steps are part of the system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems. Method execution involves three overall steps. These steps include (1 ) setting up an ATU as a base station, (2) setting up an ATU as a node on a network with the base station and (3) setting up the sensors on the ATU node. Other ATUs are setup as nodes and sensors are setup similarly.

[0057] Specifically, execution begins at step 800 wherein an ATU is setup as a gateway (base station or primary node) of a network. As part of this step 800, a user will select a location for the ATU at step 800-1 . The user will likely select a high point on a property unobstructed or place it best for radio communication with closest ATUs to ensure strong communication with other ATUs. Execution moves to steps 800-2, 800-3 wherein the ATU is activated (turned on) and communication is established with the ATU via Bluetooth or other means of communication known to those skilled in the art. That is, a mobile device (client) will establish communication with the ATU. (Also, the ATU is named as gateway (base station), and the user ensures that the ATU has good connection to the Internet and connects to the central system). Then, the user configures the ATU as a gateway (base station) at step 800-4 as known to those skilled in the art 226. Once the user completes the configuration, the ATU is installed at step 800-5. This entails installing the ATU vertically to a pole which maybe set into concrete, pole (lower) assembly is installed into concrete and the upper assembly is mounted to the pole (lower) assembly. Similarly the ATU may be installed horizontally near or below ground level to conform to the needs of specific crops. In this configuration, the ATU will be mounted on a horizontal bracket allowing the installer to push its stakes into the ground to secure its location (Fig. 22). Thus, allowing the ATU to lay in the same rows as the crops that are being monitored while tractors and other similar equipment can travel over as they perform their infield duties. A weather station can be mounted to the pole (lower) assembly and its cable to the upper assembly (gateway assembly) as known to those skilled in the art. (Many other sensors and agriculture systems may be connected to the base as well. This may set up with auto sensor detection (ID) similar to the nodes described herein.)

[0058] Next, execution moves to step 804 wherein the sensors are setup for that ATU configured as a node. As part of step 804, execution proceeds to step 804-1 wherein a user selects and installs a sensor for use with the ATU. That is, the sensor will be installed on the ATU or into the soil. For example, a user may select a soil moisture probe with multiple (e.g., six) levels of sensors and install or embed it into the ground (soil). Execution proceeds to step 804-2 wherein the user will connect an adapter cable to the sensor by and connecting respective plugs (from the cables in the control unit) to the adapter cable connectors and the opposing adapter cable connectors to the sensor cable plug as known to those skilled in the art. Note that the adapter cable must correspond to the particular type of sensor selected (to enable the correct identification (ID) matching and driver retrieval for that sensor as discussed below). Note that the ID described can also be embedded into the cable itself similar to the adapter portion as described above and integrated into a sensor. Also an ID in a third party sensor or agriculture system may be recognized wirelessly through a same procedure described herein.

[0059] One the adapter cable properly connects the sensor to the control unit, execution proceeds to sub-step 804-3 wherein the control unit within the ATU requests and retrieves the sensor ID stored in memory on the adapter cable as disclosed above. At this point, the control unit searches for a driver locally in the ID database for that sensor by its ID at sub-step 804-4. (As part of this step, a comparison will be performed comparing the sensor ID with the ID under search.) Execution proceeds to decision step 804-5 wherein the control unit determines if there is a match between the sensor ID from the memory on the adapter cable and the ID stored in the sensor ID database in the control unit. If there is an ID match, execution proceeds to steps 804-6 and 804-7 wherein the particular sensor driver associated with that sensor ID is retrieved locally and installed in the control unit. Once installed, the control unit activates the driver at step 804-8. This driver enables, among other things, the control unit to communicate with and retrieve data readings from a sensor. (In one embodiment, driver activation turns on a switch which cause certain pins on the memory (ID chip) to process sensor data read from the sensors.) If there is no ID match, then execution proceeds to step 804-9 wherein the control unit will search for a driver remotely at locations/sites in the cloud (i.e., ID database on remote servers accessed over a network). Then, the control unit will retrieve this driver at step 804-6 when it is found. In most instances, a driver will be uncovered for the particular sensor. In the rare event that a sensor does not exist for a particular sensor, the sensor will not be usable and is shown on a user interface as a non-configured sensor (not shown in Fig. 7).

[0060] Now that the driver is installed, execution proceeds to step 804-10 wherein the ATU control unit for that sensor is configured. In this respect, a user will receive a user's settings for the sensor and/or agriculture systems. These settings included all threshold and other settings for sensor readings will be entered and stored. For example, sensor reading frequency is set (e.g., every 15 minutes). In another example, a user may set a threshold for temperature (or any other sensor) to trigger a response or action when a sensor reading reaches or exceeds 33 degrees or its threshold. The action may involve activating a pump (agriculture system) to release water to irrigate a block in which the ATU is installed. Alternatively, the action may be to change the settings so that the temperature sensor reading frequency is increased for a period of time. This is described in more detail below with respect to Fig. 8. At this point, the user settings are stored at step 804-1 1. The control unit is now configured for that sensor.

[0061] Execution then proceeds to decision step 806 wherein it is determined if there is another sensor the user desires to setup with that ATU (node). If yes, execution returns to step 804 (to begin executing the sub-steps). If there are no more sensors to setup, execution proceeds to decision step 808 to determine if there are additional ATUs to setup. If so, then execution returns to step 802 to setup the next ATU as a node on the network. As part of this step, execution proceeds to steps 802-1 and 802-2 wherein the next ATU is activated (turned on) and

communication is established between the mobile device and the ATU similar to the above using Bluetooth or other communication known to those skilled in the art. Next, the user configures the ATU as a node on the network at step 802-3. As part of this, the node is named and base station selected. A location for the first ATU is determined for the node to be placed. Typically, the best location for the node is one in which it has line of sight with the gateway. The user will determine that the connection signal to the base station is strong (view mobile device application to determine signal strength by the number of bars for reception). Once the user completes configuration, the user will install the ATU into the soil at step 802-4. In one embodiment, the upper (node) assembly will be installed vertically or horizontally depending on the crop and can be placed into concrete within the ground (soil) and the upper assembly of the ATU is mounted to the pole (lower) assembly of the ATU. In another embodiment, the pod assembly and pole assembly are first attached together and then embedded into the ground. Sensors and agriculture systems are installed as desired to this location.

[0062] If there are no more ATUs to setup, then execution ends. The network is not set. Communication and data readings are now set.

[0063] Fig. 9 depicts another example method steps for setting up a network of ATUs along with their sensors. As with the method in Fig. 8, the steps in Fig. 9 represent the initial part of the system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems. Similarly, method execution involves three overall steps. These steps include (1 ) setting up an ATU as a gateway (base station), (2) setting up an ATU as a node on a network with the gateway and (3) setting up the sensors on the ATU node. Other ATUs are setup as nodes and sensors are setup similarly. The steps involve the embodiment shown in Fig. 7.

[0064] Accordingly, execution of method step 900 (and the steps within) is the same as method steps 800. Therefore, step 900 (and steps within) will not be repeated herein. From step 900, execution proceeds to step 904 wherein a sensor is setup for that ATU configured as a node. As part of step 904, execution proceeds to step 904-1 wherein a user selects and installs a sensor for use the ATU. That is, the sensor will be installed on the ATU or into the soil. For example, a user may select a soil moisture probe with many levels of sensors and install or embed it into the ground (soil). Execution proceeds to step 904-2 wherein the user will connect a sensor directly to a cable (410-A, 410-B) of the control unit (of the ATU). At this point, the user will select the driver for the connected sensor via an application or website on a mobile device or computer at step 904-3. (This presumes the mobile application is booted up or website is accessed for this to happen). Execution then proceeds to step 904-4, the control unit searches for a driver locally in the sensor database for that sensor.

[0065] Execution proceeds to decision step 904-5 wherein the control unit determines if there is a sensor found locally in the driver database 410-8. If there is a sensor stored locally, the driver is retrieved at step 904-6. If not, execution proceeds to step 904-7 wherein the control unit searched for the driver remotely in the cloud (i.e., over a network such the Internet to access a sensor ID database stored in remote server(s). Either way, execution proceeds to step 904-8 wherein the driver is installed/loaded in the control unit.

[0066] Once installed, the control unit activates the driver at step 904-9. This driver enables, among other things, the control unit to communicate with and retrieve data readings from a sensor as described above. (E.g., the driver activation affectively acts as a switch to enable communication through the pins on the sensor plug to process sensor data readings.)

[0067] Now that the driver is installed, execution proceeds to step 904-10 wherein the ATU control unit for that sensor is configured as in step 804-10 in Fig. 8. In this respect, a user will receive settings for the sensor and/or agriculture systems. These settings including all threshold and other settings for sensor readings will be entered and stored. For example, sensor reading frequency is set (e.g., every 15 minutes). In another example, a user may set a threshold for temperature to trigger a response or action when a sensor reading reaches or exceeds 33 degrees. The action may involve activating a pump (agriculture system) to release water to irrigate a block in which the ATU is installed. Alternatively, the action may be to change the settings so that the temperature sensor reading frequency is increased for a period of time. These settings may be changed/updated through the network (e.g., web interface). This is described in more detail below with respect to Fig. 10. At this point, the user settings are stored at step 904-1 1 . The control unit is now configured for that sensor.

[0068] Execution then proceeds to decision step 906 wherein it is determined if there is another sensor the user desires to setup with that ATU (node). If yes, execution returns to step 904 (to begin executing the sub-steps). If there are no more sensors to setup, execution proceeds to decision step 908 to determine if there are additional ATUs to setup. If so, then execution returns to step 902 to setup the next ATU as a node on the network. Step 902 (and steps within) is the same as step 802. Therefore, step 902 (and steps within) will not be discussed here. If there are no more ATUs to setup, then execution ends. The network is now set.

Communication and data readings are now enabled.

[0069] Fig. 10 depicts example method steps of another part of the system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems (and agriculture systems). Specifically, this figure depicts example steps for sensor data collection, transmission and subsequent management and control performed by the control unit in Figs. 4 and 5. In particular, execution begins at step 1000 when a user sets up an ATU and the sensors associated with it. Settings are entered (e.g., sensing data parameters). This is accomplished as shown and described with respect to Fig. 7. These sensors essentially remain inactive or dormant until needed. A counter/clock will continue to count as the process continues at 1002. At decision step 1004, it is determined whether a predetermined period has elapsed as set forth in the settings stored in the control unit. This decision step repeats until the predetermined time period has been exceeded. When this occurs, the control unit activates (wakes-up) a first sensor, reads the first sensor data (i.e., value for the parameter) and records (stores) the data in memory (and subsequently in the storage) on the ATU at steps 1006, 1008, 1010. (In practice, three readings are taken when a digital sensor is awake and ten readings are taken for the analog sensors. An average is used in either embodiment and any readings more than 10% outside of the others are thrown out to minimize bad data.)

[0070] Next, execution proceeds to step 101 1 wherein it the recorded data (i.e., value for the parameter) is compared to a threshold. The user set this threshold earlier. The threshold is typically a parameter that relates to or has an affect on agriculture growth. Execution then proceeds to decisions step 1012 wherein it is determined if a threshold (stored) is exceeded with respect to that sensor. For example, the step determines if the temperature exceeds 105 degrees. If the threshold has not been exceeded, execution proceeds to steps 1014 and 1016 wherein the first sensor is deactivated (put to sleep) and it is determined if there are additional sensors to activate to take readings. If there are additional sensors, execution returns to step 1006 where the next sensor is activated (awakened). In short, all sensors are activated (awakened), sensor data (parameters) read and deactivated (put to sleep). If there are no more sensors, execution returns to step 1002.

[0071] Now, if the threshold has been exceeded, then execution proceeds to step 1018 wherein the control unit generates and transmits an alert relating to this occurrence. When this alert is transmitted, it may be sent to different users and in different ways depending on their settings (e.g., voice call, text, MMS, app alert, email or others known to those skilled in the art. (These alerts may be categorized or sent based on a hierarchical range.) Execution then proceeds to step 1020 wherein the control unit will perform functions associated with the user settings for alerting specific users in a specific order while simultaneously managing and controlling the sensors and agriculture systems or devices (e.g., irrigation systems). Specifically, it is determined whether the function actually relates to a sensor setting at decision step 1020-1 . If it does, then the control unit will modify the sensor settings (data or values) at step 1020-2 and store the settings in memory (and possibly storage) in the control unit of the ATU at step 1020-3. For example, the settings may initially require temperature readings to be taken every 15 minutes, but the control unit may modify the settings to require temperatures readings to increase, for example, to every 5 minutes. If the function does not apply to sensor settings, execution proceeds to step 1020-4 to determine if the function relates to an agriculture system. For example, the function may relate to pump activation. If the function does relate to an agriculture system, the execution proceeds to step 1020-5 wherein the control unit proceeds to execute the control function on the agriculture system (e.g., pumps, chemical controls, drives). Now, it the function does not relate to an agriculture system, execution ends. While the steps are performed in the order described above, those skilled in the art know that certain steps may be performed in a different order or certain steps may be eliminated or added to achieve desired results.

[0072] Fig. 1 1 depicts a high-level diagram illustrating different data retrieved from several sources that are used for layering on a map. The data layering is displayed on the display of client 1 100. In this embodiment, client 1 100 is a tablet such as an Apple iPad wherein display 1 100-1 (dotted lines) displays the indicia (as described in more detail below) and other data received from several sources.

These data layers or representations thereof may be stacked, i.e., layered over a map of a property that an owner provides as described in more detail below. (The map is typically retrieved from Google Maps or any other known mapping service as known to those skilled in the art. Then, owner's property data is uploaded from a variety of file types is layered over that base map as polygonal boundaries.) The data layers may be derived from (data reading) sources such as soil moisture 1 102, temperature 1 104 and pressure 1 106 collected from the related sensors. Other data sources include weather data 1 108, irrigation data 1 1 10, and local images 1 1 12 taken from individuals scouting in the field (e.g., smartphone photos of crops and soil). Remote data including remote images 1 1 14 such as satellite or aerial images and irrigation and/or other schedule layers 1 1 16 may also be used as data sources for layering. In addition, other data layer 1 1 18 may be derived via an API including, for example, fertilization spray, system health data as well as plant and/or

equipment-based data 1 120 as known to those skilled in the art. As indicated above, the data may be pulled from a satellite, network, tractor, drone or any other source known to those skilled in the art. All of these layers may be employed as desired by a grower (user) to determine problem areas on his/her property.

[0073] Fig. 12 depicts central system 1200 of a wireless system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems. Central system 1200 is the same as the central system 318 shown in Fig. 3, but renumbered in Fig. 12. Central system 1200 includes sensor data (readings) collection engine 1200-1 , sensor data (readings) processing engine 1200-2, layering engine 1200-3, sensor data (readings) database 1200-4 and indicia database 1200-5. Central system 1200 further includes sensor ID database 1200-6, driver database 1200-7 and a property registration database 1200-8. Property registration database 1200-8 stores registration data relating to a user's property. These engines and databases are part of the system for efficient installation, configuration, data acquisition and management of in-field agricultural data and visualization systems (and agriculture systems).

[0074] Sensor readings (data) collection engine 1200-1 is configured to collect and process sensor data (readings) received from the ATUs in the field for any number of users (growers/service providers) wishing to access the central system 1200 and then store such data in the sensor readings database 1200-4. Sensor data (readings) processing engine 1200-2 is configured to process sensor readings (data) with respect to indicia from indicia database 1200-5. The process involves assigning the indicia to the sensor data as described below. Layering engine 1200-3 is configured to create and present layer(s) of sensor data over designed parts of a map to enable a user to quickly view real time data with respect to agriculture conditions. (Sensor ID database 1200-6 and driver database 1200-6 are used to identify sensor IDs and retrieve related drivers that are not recognized or found in individual ATUs requiring such drivers.)

[0075] Fig. 13 depicts example method steps for processing and selectively displaying data layers as desired. In particular, the example may be broken down into two parts: (1 ) execution of steps 1300-1314 that involve indicia calibration and (2) execution of steps 1316-1320 that involve the actual request for and presentation of that indicia by a user. In brief, steps 1300-1314 will calibrate the actual indicia (for the presentation thereof) that will be associated with the sensor data (readings). As described above, these sensor data (readings) relate to one or more parameters on a grower's (user's) property. The parameters include, for example, soil moisture, temperature and wind readings (to name a few). Fig. 15 provides specific example of indicia calibration. Steps execution steps 1316-1320 involve the actual request for and the presentation of the indicia. Figs. 16-18 depict specific examples of the presentation of such indicia.

[0076] In more detail, method execution begins at step 1300 wherein then user's (grower) map is retrieved. Execution proceeds to step 1302 wherein the user will select a sensor for setting indicia. Next, execution proceeds to step 1304 wherein sensor data is retrieved from a central system (e.g., central system 318, 1200) relating to a parameter that concerns an agricultural characteristic or condition on a grower's (user) property. As indicated above, the sensor data may relate to parameters such as soil moisture, temperature or pressure. The sensor data are retrieved from database 1200-4 or other locations in different examples. (The sensor data are the same as previously used for creating graphs for viewing.)

[0077] Execution then proceeds to step 1306 wherein an indicia is selected for numbers within the range. In one example, the indicia are colors within a spectrum, and color spectrum is used to represent the numbers within the range. Those skilled in the art know, however, that the indicia may be other markings or other symbols (collectively referred to as "markings"). The indicia are also stored.

[0078] Execution proceeds to step 1308 wherein a range of the sensor data (readings) for the parameter is set. In one example, the user actually selects and enters a range for the parameter. The range may be associated with a single sensor or multiple sensors as an average. The range generally consists of maximum and minimum thresholds that determine a maximum operating range for a crop. The user can also select a target range that would not take the crop into an overstressed state and would provoke a different type of alert than the maximum thresholds. (In another example, this may be preset automatically without user involvement or set using a learning algorithm based on historical readings.) Other data may be entered or calculated by the system with the influence of other sensors, (example; historic data, data imports or manual entry, mapping of soils or climate, remote sensing and others known to those skilled in the art) at this stage that may affect the range or general presentation of the indicia as described below. For example, a user may select 85 and 55 degrees for the maximum and minimum temperature range settings. Any temperature above 85 or below 55 constitutes a danger to a crop. If the parameter is soil moisture, a user must review soil moisture graphs to determine the proper soil moisture range. Soil moisture will vary per grower (user) based upon growing cycle, soil composition, location, crop type and other variables known to those skilled in the art. The soil composition may vary based on a block or lot or an individual crop. ATU soil moisture sensors (probe) are influenced by soil moisture characteristics within an 8-10 inch radius. However, for purposes of review and analysis in this example, soil moisture data (readings) are extrapolated to represent a larger radius or distance to cover or span a property block as known by those skilled in the art. The indicia (e.g., colors, numerals, icons, images, arrows or other indicia known to those in the art) are retained within a polygonal perimeter of that property. These ranges are entered and stored in the central system.

[0079] Execution proceeds to step 1310 where the sensor data is processed with respect to the indicia by sensor data processing engine 1200-2. Specifically, the indicia are assigned to the sensor data as well as a geographical location at steps 1310-1 and 1310-2, respectfully. That is, indicia are assigned to represent the states of a parameter (e.g., soil moisture at different depths of a grower's soil). If the indicia are colors and the parameter is temperature, for example, a color may be assigned to each degree within a temperature span (range). The colors may span from dark blue to bright red (in a color wheel) if a user desires or any other desired color span. For example, temperatures above 85 may remain the same color to indicate that any temperature above 85 is at an abnormal point that may destroy a crop.

Temperatures below 55 may remain bright blue to indicate that the temperature below 55 degrees is at an abnormal point that may destroy the crop. Various colors of the spectrum (e.g., between bright blue and red or any others) may be used for temperatures between the span of 30 degrees (55-85 degrees). The indicia are also associated with the geographic location of the sensor data (GPS tag) and recorded within the users specific lot, block, section or other areas known to those skilled in the art.

[0080] Execution then proceeds to step 1312 wherein the indicia assignments are actually stored. By allowing a user (e.g., grower) to select the colors (indicia) to their desired ranges, the user may calibrate the colors that are reflected on a map as described below. [0081] Execution then proceeds to decision step 1314 wherein it is determined if there are additional parameters to process (e.g., wind, soil moisture etc.). If so, execution returns to step 1302. If not, execution proceeds to step 1316 (thereby ending indicia calibration).

[0082] As stated execution proceeds to decision step 1316 wherein it is determined if a user makes a request for the indicia relating to the sensor data to be displayed over a map of the user's property. If no request has been made, then execution repeats this step until a request is made. If a user request is detected, then the map is displayed (layer) at step 1318, respectively. Then, the colors assigned to the sensor data are stacked, i.e., layered over the map at step 1320 by layering engine 1200-3. An example of the layering is shown in Fig. 14 (expanded form). Any of this can also be set on a web interface on a computer, laptop or any other device by those known of the art.

[0083] In Fig. 14, the layers include a map layer, aerial/satellite layer 1402, soil moisture layer 1404, ATU layer 1406, scheduling and any other sensory or calculated reading, as desired by the user. Aerial layer 1402 may be an NDVI imaging layer (normalized difference vegetation index image) or any other image or remote sensor as known to those skilled in the art. Soil moisture layer 1404 illustrates sections with indicia (e.g., colors) representing soil moisture. If a grower subsequently wishes to view soil moisture at a certain depth in soil between sensor locations, central system 318 will automatically populate a color (indicia) by calculating the percentage of soil between such sensors. (This is described in more detail below.) Execution then ends.

[0084] Fig. 15 depicts a diagram of an example map wherein indicia calibration is performed. Specifically, a map (layer) 1500 of the grower's property is depicted comprising several blocks 1502 of variable size, each having one or more lots as known to those skilled in the art. ATUs 1504 are also depicted. In this example, there are six ATUs shown. A user may access window 1506 to enter data ranges and other information that will determine subsequent layering. Window 1506 depicts a graph of soil moisture over a week (September 7-13) by percentages. A grower has selected all sensors 1508 so the soil moisture graph is the average soil moisture over all depths and/or sensor locations. (Alternatively, indicia calibration may be performed for each individual sensor on each ATU.) [0085] The grower also selects specific ranges or critical points for soil moisture taking into account factors such as soil composition, crop type, location, etc. These ranges include the target zone 1510 for soil moisture, an over-watered range 1512 for soil moisture (range in which the crops are considered over-watered) and a wilting range 1514 for soil moisture (range in which the crops may be considered damaged or on the verge of being damaged without intervention). Regardless of the actual data values the grower (user) selects for such ranges, the central system will assign indicia for such ranges based on prior configuration. For example, the central system may be programmed to assign blue (as indication 1516) to indicate the soil moisture is in the overwatered range, green (as indication 1518) to indicate soil moisture is within the target zone range and red (as indication 1520) to indicate soil moisture is within the wilting range. In one example, nine distinct colors may be used, with three defined specifically for the established ranges above and shown. The other six colors are assigned to equal increments between such ranges.

However, those skilled in the art know that any number of colors or other indicia may be assigned to the ranges and increments between those ranges. In the example shown, the grower has set the values of 65-100% as an overwatered range, 45-51 % for a target zone for soil moisture and 0-15% for the wilting range for soil moisture. Once the grower has applied these settings, indicia (color) calibration is set and the grower may quickly view different layers of sensor data as desired on the grower's own property.

[0086] Fig. 16 depicts a diagram of an example map (on a screen) of a grower's property (layer) along with soil moisture indicia and other data layering as described above. Figs. 17-18 depict example maps of a grower's property (layer) along with soil moisture indicia and other data layering as described above.

[0087] More specifically, as indicated above, Fig. 16 depicts a diagram of an example map of the grower's property (layer). Soil moisture depth tool or gauge 1600 is depicted in the top right wherein a soil root 1602 of a crop is shown. This tool or gauge can also be used to represent other sensors, such as temperature sensors whereas the adjustable levels would be heights above ground. The gradations along the gauge represent distances below the ground level along the root 1602 of the crop in the example of soil moisture as stated above. As the grower moves the gauge bar 1604 down the root 1602, the layers of data including the indicia will be displayed at different depths along the root. In this example, indicia associated with soil moisture may be shown at different depths along the root. If the sensors are deployed every 10 inches, the gauge bar will show soil moisture at different depths of 10" increments. In Fig. 16, the indicia that are associated with soil moisture throughout the grower's property are shown at a depth of 10 inches.

[0088] As shown, property blocks 1606 and 1608 for example, depict soil moisture as represented by corresponding color (indicia) spread within each blocks respectively. In particular, block 1606 may depict orange patches or shading to represent soil moisture that falls between the target and wilting zones. Blocks 1608 and 1610 may depict green patches or shading (target zone/range) and block 1612 may depict blue patches or shading (overwatered range). In other examples, the indication may encompass an entire block or property.

[0089] In Fig. 17 a map of the grower's property is shown as in Fig. 16. Soil moisture depth gauge 1600 is again depicted in the top right wherein a soil root 1602 of a crop is shown. In this instance, indicia assigned to sensor data throughout the blocks are (e.g., 1606, 1608) are displayed at a 20 inch depth. In this respect, blocks 1606 and 1608 may both display green shading (e.g., patches) to indicate that soil moisture is within the target zone for those blocks of crops. The color can be a solid color within the area or vary to create a "heat" map when multiple sensors are uses in the area parameters selected.

[0090] In Fig. 18, a map of the grower's property is shown as in Figs. 16 and 17. Soil moisture gauge 1600 is again depicted in the top right wherein a soil root 1602 of a crop is shown. In this instance, however, indicia representing soil moisture throughout the blocks are (e.g., 1606, 1608) displayed at a 40 inch depth. In this respect, block 1606 may display red shading or patches that indicate that soil moisture is in the wilting range. Thus, a grower will quickly know that the crops may die if the grower does not intervene. Block 1608 may display green shading or patches that indicate that soil moisture falls within the target zone. The grower quickly realizes that no intervention will be necessary.

[0091] Now, if a grower moves gauge bar 1604 along the root to a position between actual sensor positions or locations on a probe, the central system will calculate the percentage of soil moisture at that position as a function of the soil moisture percentage of the two nearest sensor locations. That is, the calculation will take into account the weight or influence of soil moisture percentages of the upper and lower sensors. Specifically, if a grower moves gauge bar 1604 to a depth of (D), then a virtual sensor (VS) is defined as hypothetical sensor at position (D) between an upper sensor (US) and lower sensor (LS). The percentage of the upper sensor (%US) is the percentage in fraction that will be taken from an upper sensor's reading. %LS is the percentage in fraction that will be taken from lower sensor's reading. D is the desired depth. As indicated, a virtual sensor is the sensor positioned between the US and LS at depth D. Given this,

Virtual Sensor (VS) = ((%US) * (US data reading)) + ((%LS) * (LS data reading)) whereby

%US = [1 - (D - US depth)/(LS depth - US depth)] and %LS = 1 - %US.

[0092] For example, let's assume that sensors are positioned or displaced every 10 inches (e.g., 10, 20, 30 inches) along a probe embedded into the soil. If US = sensor at 10 inches, LS is the sensor at 20 inches and D = 16 inches (the desired depth). Then, given the formula above, sensor moisture at 10 inches = 40% (upper sensor) and soil moisture at 20 inches is 53% (lower sensor). Accordingly, the %US = [1 - (16-10)/20-10)] or .4 and %LS = 1 - .6 or .4. The VS = (.4)(40%) + (.6)(53%) = 16% + 31 .8% = 47.8%. Thus, the soil moisture is assumed to be 47.8% at 16 inches. Therefore, a color assigned to this value is established during calibration. While this example describes the application of soil moisture, the tool (e.g., gauge) and formula above can also apply to temperature or any other sensed parameter value. If temperature or others are used, the gauge will be above ground.

[0093] Figs. 19 depicts screen shot of a map layer of another grower's property along with one or more layers of data. Fig. 20 depicts a diagram of another example map (on a screen) of a grower's property (layer) along with soil moisture indicia and other data layering as described above.

[0094] In Fig. 19, layer 1902 (of a user's property) is depicted along with several layers of sensor readings (data). As indicated above, property 1900 consists of several blocks identified by lines as shown. Each block may consist of any number of lots (e.g., three lots identified by dashed lines). In this figure, menu 1904 of additional layers is shown in the top right corner. Several layers are selected for presentation on map layer 1902. Specifically, layer 1906 is an aerial/satellite (e.g., NDVI). Layer 1908 is a data layer illustrating ATUs (utilities) in the field. Layer 1910 illustrates problem areas represented by red surrounded by white circles (or another color as set during calibration). Users identify problem areas in the field when they are scouting a property for issues with the crops. For example, the user may take a few pictures of an affected field location (while using the scouting feature within the application on a user's smart phone or device), upload the pictures along with an alert setting (defining the extent of the problem) to the central system (via cloud) along with a description of the problem. They may also turn this information in to a work order or apply or send this to another user(s) for review or action. The problem may be fungal or other disease. Scouting may also apply to equipment and non- plant based problem areas. The user's GPS will identify the location with this problem area. This data will be stored in the property data database 1200-7. This data are turned into layers that are presented to the user over his/her property (map) and can be sorted and reported by property bock and lot (PBL) or other are known to those skilled in the art. (The soil moisture layer is not shown here.) In addition to the layering menu, a block may be clicked to open a window 1912 that provides information including pictures, crop type, scale of the problem, temperature and other information.

[0095] In Fig. 20, three data layers are selected for viewing. Specifically, layer 2000 is a map while layer 2002 is an aerial/satellite (e.g., NDVI). Map layer 2000 includes several blocks and lots within the property as shown. Layer 2004 is a data layer illustrating ATUs (utilities) in the field, layer 2006 is the indicia layer, i.e., soil moisture layer and layer 2008 illustrates problem areas represented by circles as described above. In this figure, layer menu 2010 is shown in the top right corner as described above to enable the user to select layers for display and view.

[0096] With layer 2006, color may be used as the indicia to depict soil moisture in various blocks and/or lots on the property. The color designations are selected as desired and may shade or fill the entire area of the block or lot. The changes in color shading may be gradual or abrupt between color areas. In Fig. 20 for example, blocks or lots with green shading 2006a, for example, may represent soil moisture within a target range while blocks or lots with orange shading 2006b may represent soil moisture that falls between a target range/zone and a wilting zone (described above). Blocks or lots with blue shading 2006d may represent soil moisture within an overwatered range/zone. Blocks or lots with grey (or light blue) shading 2006c may represent soil moisture between the target range/zone and overwatered range/zone. These color designations for the soil moisture are an example. Those skilled in the art know that other color designations may be used to achieve desired results.

[0097] Soil moisture depth tool or gauge 2012 is also depicted wherein gauge bar 2014 and soil root 2016 of a crop are shown. Gauge 2012 functions the same as the gauge described above. If a user desires, he/she may move the bar down, colors will represent the moisture at each level of soil 10", 20", 30", 40" and 50" etc. This enables the user to know exactly where water is being stored and what areas are experiencing dryness within the planted root zone. In addition to the layering menu 2010, a block area may be clicked to open a window 2018 that provides information including pictures, crop type, scale of the problem, temperature and other information at that location as described above. Suffice it to say, the user may select a location/area of interest and generate sensor data list of measurements or a graph. As a user deploys more sensors, the image will increase in accuracy to show which areas need attention by the user.

[0098] With this data over-layering, a user need not view individual graphs to make immediate agriculture decisions. The user has the ability to import or export (through and API or upload) all data layers into one user interface. Thus allowing the user/grower to stack layers upon layers all in map form. This enables the user to visualize reports and make decisions. By stacking soil moisture, with temperature, irrigation maps, imagery and pest/fungal reports or others familiar to those skilled in the art, a user can easily begin to visualize geospatial problem areas that they may need to make corrective actions within.

[0099] Fig. 21 depicts a block diagram of a general-purpose computer to support the embodiments of the systems and methods disclosed herein. In a particular configuration, the computer 2100 may be a server or a computer (client) as described hereinabove. The computer 2100 typically includes at least one processor 2100-1 and system memory 2100-2 (e.g., volatile - RAM or non-volatile - Flash or ROM). System memory 2100-2 may include computer readable media that is accessible to the processor 2100-1. The memory 2100-2 may also include instructions for processor 2100-1 , an operating system 2100-3 and one or more application platforms 2100-4 such as Java and a part of software modules or one or more software applications (i.e., steps) and/or modules 2100-9. The computer will include one or more communication connections such as network interfaces 2100-5 to enable the computer to communication with other computers over a network, storage 2100-7 such as a hard drives for storing data 800-8 and other software described above, video cards 2100-6 and other conventional components known to those skilled in the art. This computer 2100 typically runs Unix, Linux, Microsoft Windows or Macintosh OSX or other as the operating system and includes the TCP/IP protocol stack (to communicate) for communication over the Internet as known to those skilled in the art. A display 2150 is optionally used. The server typically includes TCP/IP protocol stack (to communicate) for communication over the Internet as known to those skilled in the art. Program Data 2100-8 is also stored within computer server 2100. The content providers also include a web server along with other servers hosted by the content provider as known by those skilled in the art. The content providers also include a web server along with other servers hosted by the content provider as known by those skilled in the art.

[00100] The mobile device described above includes similar components as computer 2100 including a processor, memory, storage, etc. as well as a display within it.

[00101] It is to be understood that the disclosure teaches examples of the illustrative embodiments and that many variations of the invention can easily be devised by those skilled in the art after reading this disclosure and that the scope of the present invention is to be determined by the claim(s) below.