FIELD OF THE INVENTION

The invention relates to the field of smart farming, and more specifically to the field of location based smart farming.

BACKGROUND OF THE INVENTION

Smallholders and farmers in remote regions often face numerous challenges that can lead to them becoming trapped in subsistence level farming, a cycle of poverty and gender inequality. In addition, the effects of climate change can often reduce the productivity of mainstream crops, which smallholders often rely on.

These challenges are often magnified due to a lack of access to agronomic and sustainable farming information, for example relating to crop diseases and soil quality degradation, available to smallholders across the globe in urban and remote regions. In many developing regions, dynamics of culture, lack of access to finance and poor information dissemination can preclude smallholders from accessing best-in-class sustainable farming practices as they are developed.

As a result, farmers and smallholders in these regions can find themselves trapped in a vicious cycle of poor productivity and poverty. For example, in Nigeria preventable insect pests and diseases in yams resulted in a 25% mean annual yield loss in 2003.

As the effects of climate change spread, and the global population continues to grow and become urbanized, it is imperative that farmers in these regions, who are the mainstay of rural communities, become successful in the future.

There is therefore a need to improve the productivity and welfare of smallholder agricultural areas in remote rural regions.

SUMMARY OF THE INVENTION

The invention is defined by the claims.

According to examples in accordance with an aspect of the invention, there is provided a computer implemented method for defining a geographical boundary of an agricultural area within an agricultural region, the method comprising: obtaining location data as a user moves along a geographical boundary by way of a mobile computing device comprising a global positioning unit and an imaging unit, the location data relating to the global position of the mobile computing device; deriving one or more geographical coordinates based on the location data; obtaining a map of the agricultural region; mapping the one or more geographical coordinates to the map of the agricultural region; defining a geo-fence representing a boundary of an agricultural area based on the one or more mapped geographical coordinates; obtaining image data representative of a crop or livestock within the agricultural area by way of the imaging unit; obtaining crop or livestock location data representative of the global position of the crop or livestock; deriving a geographical crop or livestock coordinate for the crop or livestock based on the crop or livestock location data; processing the image data to identify the crop or livestock and generate a crop or livestock identity tag; linking the geographical crop or livestock coordinate and the crop or livestock identity tag to each other; mapping the linked geographical crop or livestock coordinate and crop or livestock identity tag to the map of the agricultural region; obtaining crop or livestock disorder location data representative of the global position of a crop or livestock disorder; deriving a geographical crop or livestock disorder coordinate based on the crop or livestock location data; linking the geographical crop or livestock disorder coordinate and the crop or livestock disorder identity tag to each other; and mapping the linked geographical crop or livestock disorder coordinate and crop or livestock disorder identity tag to the map of the agricultural region; predicting the spread of the crop or livestock disorder in the agricultural region based on the crop or livestock identity tag, the geographical crop or livestock coordinate and the geographical crop or livestock disorder coordinate.

The invention provides a means of accurately defining a boundary of agricultural area within an agricultural region. In particular, the invention provides a means of defining a custom boundary using any widely available mobile computing device having a global positioning unit, such as a smartphone. Indeed, the user may define any custom boundary within an agricultural region by walking, riding or driving the boundary of the agricultural area holding the mobile computing unit.

In other words, by obtaining location data about the boundary of the agricultural area, such as a field of a farm, which may be clear in person, the method provides a means of translating said location data into a form that can be interpreted by a user to give context of the positioning of the agricultural area within the agricultural region.

Put another way, by generating a geo-fence boundary about the agricultural region in combination with a map of the agricultural region, the method provides a means of expanding the information available to a user, such as the owner of the agricultural area, i.e. the farmer, for example by providing an instantaneous assessment of land area occupied by the agricultural area or crops and crop swaps suitable for that area.

In this way, the defined geo-fence may be associated with a given crop or livestock as identified by processing image data captured by the mobile computing device. By performing the identification of the crop or livestock by way of image processing, the accurate identification of a crop or livestock is not limited by the knowledge of the farmer, or smallholder, or the literacy skills required to identify a crop or livestock by typing in a crop or livestock name or selecting the correct crop or livestock from a list. Thus, the reliability of the identification of the crop or livestock, and so the quality of the management information provided to the user, is increased.

The mapped geolocation coordinates are used to combine disease identification data collected via the device with satellite meteorological and other data to calculate and predict disease spread.

The location data may be gathered as the user moves around the area with the device. Alternatively the location data may be input an alternative way, for example by plotting points manually on a map.

In an embodiment the method further comprises: obtaining meteorological data relating to the agricultural region; using the meteorological data relating to the agricultural region to predict the spread of the crop or livestock disorder in the agricultural region.

Use of the meteorological data can be used to provide an improved prediction about the spread of a disorder. In a further embodiment, the method further comprises: deriving a plurality of geographical crop or livestock coordinates based on the crop or livestock location data; linking the plurality of geographical crop or livestock coordinates and the crop or livestock identity tag to each other; and mapping the linked geographical crop or livestock coordinate and crop or livestock identity tag to the map of the agricultural region; and defining a geo-fence representing a boundary of a crop or livestock area within the agricultural area based on the one or more mapped geographical crop or livestock coordinates.

In this way, one or more different crop or livestock types and crop or livestock areas may be defined within the agricultural area belonging to the user. In this way, the amount of information, such as the land area occupied by each crop and projected yield, which can be presented to the user is increased.

In an embodiment, the obtaining of crop or livestock disorder location data comprises identifying a crop or livestock disorder based on the image data representative of the crop or livestock.

In this way, a crop or livestock disease or a crop or livestock pest may be accurately identified without having to rely on the farmer’s access to the required agricultural knowledge to perform such an identification.

In an embodiment, the method further comprises: obtaining additional crop or livestock disorder location data representative of an additional global position of the crop or livestock disorder; and deriving an additional geographical crop or livestock disorder coordinate based on the crop or livestock location data. In an embodiment the method further comprises tracking a spread of the crop or livestock disorder based on the geographical crop or livestock disorder coordinate and the additional geographical crop or livestock disorder coordinate.

In this way, the location of the crop or livestock pest or crop or livestock disease may be mapped to the map of the agricultural region, thereby enabling the farmer, and other farmers within the agricultural region, to be aware of the location of a given crop or livestock disorder and make the appropriate crop or livestock management decisions based on this information.

In this way, the spread of the crop or livestock disorder can be tracked within the agricultural region in order to provide more information to the farmer as to the effect of the crop or livestock disorder within the agricultural region. In this way, the farmer may be appraised of the potential effect of the crop or livestock disorder on the crop or livestock yields from the spreading crop or livestock disorder.

In an embodiment, predicting the spread of the crop or livestock disorder in the agricultural region comprises one or more of: predicting and/or tracking the spread of the crop or livestock disorder in a first agricultural area; and predicting the spread of the crop or livestock disorder from the first agricultural area to a second agricultural area; generating a warning for the second agricultural area based on the predicted spread of the crop or livestock disorder.

In this way, farmers within the agricultural region may be warned of the potential spread of the crop or livestock disorder before the spread occurs, thereby enabling the farmers to take preventative action against the spread of the crop or livestock disorder.

In an embodiment, the method further comprises: generating one or more recommendations of how to treat the crop or livestock disorder in the agricultural area; and providing the one or more recommendations of how to treat the crop or livestock disorder to a user.

In this way, farmers without access to the agricultural knowledge on how to treat a crop or livestock disorder may be automatically provided with a recommended course of action based on the identification of the crop or livestock and crop or livestock disorder, thereby reducing the potential loss of crop or livestock yield to the crop or livestock disorder.

In an embodiment, the method further comprises: predicting one or more crop disorders that may occur based on the crop or livestock identity tag and the geographical crop or livestock coordinate; generating one or more recommendations of how to prevent the one or more predicted crop or livestock disorders; and providing the one or more recommendations of how to prevent the crop or livestock disorder to a user.

In an embodiment, the method further comprises: obtaining soil characteristic data relating to one or more characteristics of the soil within the agricultural area; and linking the soil characteristic data to the geo-fence of the agricultural area. In this way, additional information regarding the agricultural area may be made available to the farmer.

In a further embodiment, the method further comprises: generating one or more recommendations of crops to be planted in the agricultural area based on the soil characteristic data; providing the one or more recommendations of crops to a user.

In this way, farmers without access to the agricultural knowledge on which crops or livestock may be suitable for the soil of the agricultural area may be automatically provided with a crop or livestock recommendation based on the soil characteristics, thereby increasing the potential crop or livestock yield of the agricultural area.

In an embodiment, the method further comprises: obtaining an indication of a crop to be planted or livestock to be kept in an agricultural area; generating one or more crop or livestock tending recommendations based on the indicated crop or livestock to be planted in the agricultural area and the soil characteristic data; and providing the one or more crop or livestock tending recommendations to a user.

In this way, farmers may be automatically provided with an automatically generated crop tending regime based on the combination of crop or livestock identification and soil characteristics, thereby increasing the potential crop or livestock yield of the agricultural area.

In an embodiment, the method further comprises: obtaining an indication of a crop or livestock to be planted in an agricultural area; predicting one or more crop or livestock disorders that may occur based on the indicated crop or livestock and the soil characteristics; generating one or more recommendations of how to prevent the one or more predicted crop or livestock disorders; and providing the one or more recommendations of how to prevent the crop or livestock disorder to a user.

In this way, farmers may be automatically provided with one or more automatically generated recommendations for preventing crop or livestock disorders for the specific crop or livestock identification and soil characteristics of the agricultural area, thereby increasing the potential yield of the agricultural area. In an embodiment, the method further comprises: obtaining further soil characteristic data from the agricultural area, wherein the further soil characteristic data is obtained at a time after the original soil characteristic data; updating the soil characteristic data based on the further soil characteristic data; and updating the recommendations to be provided to the user based on the updated soil characteristic data.

In this way, as soil characteristics change over time, the recommendations provided to the farmer may also change over time, thereby avoiding providing the farmer with outdated recommendations that may lead to a reduction in crop or livestock yield.

In a further embodiment, the method further comprises: tracking a change in soil characteristics based on the updated soil characteristic data and the original soil characteristic data; predicting a future change in soil characteristics based on the tracked change in soil characteristics; generating one or more soil management recommendations based on the predicted future change in soil characteristics; and providing the one or more soil management recommendations to a user.

In this way, the farmer may be provided with one or more automatically generated recommendations on how changes in the soil quality may be reduced or mitigated, thereby leading to an increased crop or livestock yield in the agricultural area.

In an embodiment, the method further comprises: obtaining environmental data relating to environmental characteristics of the agricultural region; and linking the environmental data to the geo-fence of the agricultural area.

In a further embodiment, the environmental data comprises one or more of: climate data; pollution data.

In this way, additional information may be associated with the geo-fenced agricultural area.

In an embodiment, the method further comprises: generating one or more recommendations of crops to be planted or livestock to be kept in the agricultural area based on the environmental data; providing the one or more recommendations of crops or livestock to a user. In this way, farmers without access to the agricultural knowledge on which crops or livestock may be suitable for the environmental conditions of the agricultural area may be automatically provided with a crop or livestock recommendation based on the environmental data, thereby increasing the potential crop or livestock yield of the agricultural area.

In an embodiment, the method further comprises: obtaining an indication of a crop to be planted or livestock to be kept in an agricultural area; generating one or more crop or livestock tending recommendations based on the indicated crop or livestock to be planted in the agricultural area and the environmental data; and providing the one or more crop or livestock tending recommendations to a user.

In this way, farmers may be automatically provided with an automatically generated crop or livestock tending regime based on the combination of crop or livestock identification and soil characteristics, thereby increasing the potential crop or livestock yield of the agricultural area.

In an embodiment, the method further comprises: obtaining an indication of a crop or livestock to be planted in an agricultural area; predicting one or more crop or livestock disorders that may occur based on the indicated crop and the environmental data; generating one or more recommendations of how to prevent the one or more predicted crop or livestock disorders; and providing the one or more recommendations of how to prevent the crop or livestock disorder to a user.

In this way, farmers may be automatically provided with one or more automatically generated recommendations for preventing crop or livestock disorders for the specific crop or livestock identification and soil characteristics of the agricultural area, thereby increasing the potential crop or livestock yield of the agricultural area.

In an embodiment, the method further comprises: obtaining further environmental data from the agricultural area, wherein the further environmental data is obtained at a time after the original environmental data; updating the environmental data based on the further environmental data; and updating the recommendations to be provided to the user based on the updated environmental data.

In this way, as environmental conditions change over time, for example due to seasonal changes or climate change, the recommendations provided to the farmer may also change over time, thereby avoiding providing the farmer with outdated recommendations that may lead to a reduction in crop yield.

In an embodiment, the method further comprises: tracking a change in environmental characteristics based on the updated environmental data and the original environmental data; predicting a future change in environmental characteristics based on the tracked change in environmental characteristics; generating one or more recommendations on tending the agricultural area based on the predicted future change in environmental characteristics; and providing the one or more recommendations on tending the agricultural area to a user.

In this way, the farmer may be provided with one or more automatically generated recommendations on how the management of the agricultural area may be adjusted based on changes to the environment, thereby leading to an increased crop yield in the agricultural area.

According to examples in accordance with an aspect of the invention, there is provided a computer program product comprising computer program code means which, when executed on a computing device having a processing system, cause the processing system to perform all of the steps described above.

According to examples in accordance with an aspect of the invention, there is provided a mobile computing device for defining a geographical boundary of an agricultural area in an agricultural region, wherein the device comprises: a global positioning unit adapted to obtain location data relating to the global position of the mobile computing device; a memory unit, wherein the memory unit is adapted to store a map of the agricultural region; and a processing unit in communication with the location data unit and the memory unit, wherein the processing unit is adapted to: derive one or more geographical coordinates based on the location data; map the one or more geographical coordinates to the map of the agricultural region; and define a geo-fence representing a boundary of an agricultural area based on the one or more mapped geographical coordinates.

These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, and to show more clearly how it may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

Figure 1A to Figure IE show schematic representations of generating a geo fence;

Figure 2 shows a method according to an aspect of the invention;

Figure 3 shows a plan view an agricultural region comprising an agricultural area showing a planted crop;

Figure 4 shows a plan view an agricultural region comprising an agricultural area showing soil characteristics;

Figure 5 shows a plan view an agricultural region comprising a plurality of agricultural areas with a crop disorder spreading between the agricultural areas; and

Figure 6 shows a mobile computing device according to an aspect of the invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The invention will be described with reference to the Figures.

It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of the apparatus, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatus, systems and methods of the present invention will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the Figures are merely schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the Figures to indicate the same or similar parts.

The invention provides a computer implemented method for mapping a geographical boundary of an agricultural area within an agricultural region. The method includes obtaining location data by way of a handheld mobile device comprising a location data unit and deriving one or more geographical coordinates based on the location data. A map of the agricultural region is obtained and the one or more geographical coordinates are mapped to the map of the agricultural region. A geo-fence representing a boundary of an agricultural area is generated based on the one or more mapped geographical coordinates.

Provided is a means of defining a custom geo-fence around an agricultural area using a mobile computing device by combining a preloaded map of the agricultural region and one or more geographical coordinated derived from global positioning data obtained by the mobile computing device.

Put another way, there is provided a means for an individual to divide parts of an agricultural region into geo-fenced agricultural areas using a mobile computing device.

Prior to the detailed description of the invention, there is provided herein a brief explanation of how a mobile computing device with a global positioning unit may acquire location data relating to the global positon of the mobile computing device.

Standalone global positioning devices often depend solely on information from satellites in order to obtain location data relating to the global position of the global positioning device. However, in rural agricultural areas, satellite coverage may not be comprehensive enough to establish an accurate position of the global positioning device.

Mobile computing devices, such as smartphones, which possess more functionality than standalone global positioning devices, are capable of leveraging a number of different technologies in order to derive location data relating to the global position of the mobile computing device.

For example, a mobile computing device may be capable of utilizing an assisted, or augmented, global positioning system (A-GPS) in order to derive location data relating to the global position of the mobile computing device. A-GPS augments the data received from satellites by using cell tower data to enhance the quality and precision of the location data in poor satellite signal conditions.

In a further example a mobile computing device having a subscriber identity module (SIM) may employ a SIM-based global positioning method in order to derive the location data. Using the SIM of the mobile computing device, it is possible to obtain raw radio measurements from the handset of the mobile computing device. The raw radio measurements may be processed, for example to derive time of flight measurements from each radio source, in order to derive the location data.

In a further example, a mobile computing device may utilize crowdsourced Wi Fi data to identify the mobile computing devices location.

Indeed, the mobile computing device may make use of any combination of the location data derivation methods desribed above in the methods of the invention described below. It should be noted that the examples given above are not limited to functioning with only GPS, but may be employed using any global navigation satellite system (GNSS).

Figure 1 A to Figure ID show schematic representations of how a geo-fence may be generated by a user, such as a farmer or smallholder, using a mobile computing device according to an aspect of the invention.

Figure 1A shows a plan view 100 of a schematic representation of an agricultural region 110. In Figure 1 A, a user 120, who may be a farmer or smallholder, is shown standing at a starting position for defining a geographical boundary of an agricultural area within the agricultural region. In the example shown in Figure 1 A the user is holding a mobile computing device (not shown) that includes a global positioning unit capable of obtaining location data relating to the global positon of the mobile computing device and the user. For example, the mobile computing device may obtain location data using any of the methods described above.

Figure IB shows the user 120 having moved along a portion of the agricultural area being defined. As the user moves along the boundary of the agricultural area, the mobile computing device continues to obtain location data relating to the global position of the user as they walk, ride or drive the boundary of the agricultural area. One or more geographical coordinates 130 are derived based on the location data obtained by the mobile computing device.

Figure 1C shows the user 120 having moved around the full boundary of the agricultural area 140 holding the mobile computing device. The one or more geographical coordinates now describe the boundary of the agricultural area.

Figure ID shows the user 120 holding the mobile computing device 150 having moved around the full boundary of the agricultural area 140. In the example shown in Figure ID, the user interface 160 of the mobile computing device is shown in more detail.

A map 170 of the agricultural region is obtained and displayed to the user on the user interface 160. The one or more geographical coordinates are mapped to the map of the agricultural region and used to define a geo-fence 180 representing the boundary of the agricultural area. In other words, a visual representation of the boundary on the map may be generated to be directly interpreted by a user.

The one or more geographical coordinates may be editable by the user by way of the user interface in order to redefine the geo-fence of the agricultural area. In this way, geographical that are considered to be inaccurate, for instance in cases where the mobile computing device is unable to obtain location data of sufficient accuracy.

Figure ID shows the user 120 having moved around the full boundary of the agricultural area 140 holding the mobile computing device. Further, in the example shown in Figure IE, the user has further defined a first additional agricultural area 190 and a second additional agricultural area 195 within the agricultural area 140. In other words, the user may define a number of different agricultural areas within a larger agricultural area, or agricultural region. For example, each of the additional agricultural areas, within the larger agricultural area, may be planted with different crops, used to keep different live stock or a mixture of both crops and livestock.

The invention leverages the features available in a relatively affordable mobile computing device, such as a smartphone, to provide the opportunity to integrate boundary mapping technologies into the smallholder farming process. Establishing habits of integrating mobile technologies is essential at an early stage in the evolution of smart-farming practices, particularly at the small scale farming of smallholders. Thus, the invention provides a means of access to smart-farming practices on a small scale without requiring any additional hardware that the farmer does not have access to already.

Once the geo-fence of the boundary of the agricultural region has been defined, the mobile computing device may be adapted to generate prompts to be provided to the user based on the user’s relative position to the geo-fence. For example, the mobile computing device may alert the user when the user enters or leaves the geo-fence. In a further example, the user may provide an indication to the mobile computing device that they are planting a crop or keeping livestock in the agricultural area and the mobile computing device generate guidance for the user to plant the crop or keep the livestock in the agricultural area in an efficient manner without straying from the agricultural area or without having to leave a large margin of unplanted area.

It should be noted that the boundary of the agricultural area does not need to encompass an agricultural area entirely. For example, where the agricultural area is a large field that the user wishes to divide into two parts, the user may simply walk across the field to define a boundary dividing the field into two parts.

The invention provides a means of implementing a “Digitalisation for Agriculture” smart farming application using technologies, such as machine learning and data analysis, as described in further detail below, to provide smallholders in remote developing regions with access to integrated, location specific, sustainable farming information, boundary mapping facilities and early disease identification resources. The productivity, welfare and route to market of smallholder farmers may be enhanced by way of a holistic accessible resource described herein.

The defining of the geolocation of a farm, by way of the geo-fence, may be fed into a networking facility, by way of a communication unit of the mobile computing device, which may be accessible by a plurality of smallholders within, or within a vicinity of, the agricultural region, to aggregate logistical and cost information data. Put another way, the geolocation of the farm, and surrounding farms, may be used to form the basis of a virtual cooperative between smallholders, which may be utilized to improve negotiation power and promote farm output sales and routes to market. In addition, establishing a direct route to local and international food chains for aggregated farm outputs may increase profitability for smallholders, thereby leading to more inward investment and increased international purchasing power.

Further, the mobile computing device may establish a communication link with a server, by way of a communication unit on the mobile computing device, such as a government land registry server. Accordingly, the geo-fence defined by the user may be directly communicated to a land registry server to register the agricultural area, i.e. the farm, as officially belonging to the smallholder. Using an API to overlay the geo-fence on a global map a connection may be established to a government land registration portal to enable smallholder farmers who own land through customary law to map their land and be easily identified for access to financial services, such as loans and insurance.

Figure 2 shows a computer implemented method 200 for defining a geographical boundary of an agricultural area within an agricultural region as shown in Figures 1 A to ID. The method described with respect to Figure 2 may be performed using a processor of the mobile computing device.

In step 210, location data is obtained by way of the mobile computing device comprising a global positioning unit, the location data relating to the global position of the mobile computing device, and in step 220, one or more geographical coordinates are derived based on the location data.

In step 230, a map of the agricultural region is obtained and in step 240, the one or more geographical coordinates are mapped to the map of the agricultural region.

In step 250, a geo-fence representing a boundary of an agricultural area is defined based on the one or more mapped geographical coordinates.

The method may be repeated for any number of different agricultural areas, for example fields or pastures, within an agricultural region, such as a farm.

The methods described above with reference to Figures 1 and 2 provide a means of subdividing large expanses of land and managing individual plots, or agricultural areas, on a custom and individual basis. In other words, the methods described above provide a means for a farmer or smallholder with limited access to technology in a remote agricultural region to employ Site Specific Crop Management (SSCM), which is typically only available to large scale agricultural concerns.

In this way, individual farmers can gain a greater understanding of their agricultural area characteristics using GPS mapping, which can then be used for creating tailored cultivation advice, as described further below. Further, the geo-fencing of the agricultural areas of multiple farmers may be used as a basis for aggregating multiple smallholders’ inputs/outputs in the route to market for the crops, livestock or livestock products, which may establish greater negotiating power, more efficient logistics, greater food security and improved phyto-sanitary standards.

The inventors have recognised the lack of a holistic approach to small scale farm management, which may exist in part due to the barrier to access of agricultural technology in remote regions. Existing boundary mapping tools and algorithms are satellite or drone based, which may not be accessible to users in remote regions. The inventors have identified a means of leveraging the GPS, or other GNSS, units present in mobile computing devices to generate geographic information system (GIS) coordinates, which are then mapped to the map of the agricultural region to superimpose these coordinates on an existing map interface.

The mapped coordinates form the basis of the further inventive concepts described below. In an example, when the geo-fence of the agricultural area has been defined, the mobile computing device may determine the local language of the user based on the determined global position of the agricultural area. The user interface, and any future interactions with the user, may then be provided in said local language in the form of written, audio or visual prompts. In addition, the mapped geographical coordinates may be linked directly to the land registry of the jurisdiction in which the agricultural region is located to enable the establishment of the user’s ownership of the agricultural area. Put another way, using an API to overlay the geo-fence on a global GPS map, a connection can be established to a government land registration portal to empower smallholder farmers who own land through customary law to map their land and be easily identified for access to financial services, such as loans and insurance.

Figure 3 shows a plan view 100 of a schematic representation of an agricultural region 110. In the example shown in Figure 3, the mobile computing device held by the user 120 further comprises an imaging unit, such as a camera.

Using the imaging unit of the mobile computing device, image data 300 representative of a crop 310 or livestock within the agricultural area 320 is obtained. For example, in the case where the mobile computing device is a smartphone, the user 120 may acquire image data representative of the crop or livestock by taking a picture of the crop or livestock using the smartphone camera.

The image data may be processed, for example by way of a processing unit on the mobile computing device, in order to identify 330 the crop and generate a crop or livestock identity tag. For example, the user may take a photo of the leaves of a crop and the mobile computing device may analyze the size, shape and color of the leaves in the photo in order to identify the crop. In a particular example, the user may take a photo of the leaves of a crop and the mobile computing device may identify the crop as a potato crop and generate the crop identity tag “potato”.

As the image data is being obtained, crop or livestock location data may also be obtained, in a similar manner to the location data acquisition described above, in order to correlate the location of the crop or livestock with the crop or livestock identity tag. In other words, a geographical crop or livestock coordinate may be derived based on the crop or livestock location data and linked to the crop or livestock identity tag.

The linked geographical crop coordinate and crop or livestock identity tag may then be mapped to the map of the agricultural region 110. Put another way, the geo-fence of the agricultural area may be linked to the crop or livestock identity tag based on the crop or livestock geographical coordinate in order to allocate a given crop or livestock to a given geo- fenced agricultural area.

By linking the crop or livestock identity tag to a geo-fenced agricultural area, information on the crops planted or livestock kept in the agricultural areas, the land area used for a given crop or livestock, projected crop or livestock yields and the like can be collated and presented to the user. This information may then be used to aggregate supplies, inputs needed to maintain the farm, outputs from the farm and the like. Put another way, the invention provides a means of generating a data-based approach to farming on a small, localised scale using customizable parameters defined on a user by user basis.

In a further example, a plurality of geographical crop or livestock coordinates may be derived based on the crop or livestock location data, which may then be linked to the crop or livestock identity tag and mapped to the map of the agricultural region. A geo-fence representing a boundary of a crop or livestock area within the agricultural area may then be defined based on the one or more mapped geographical crop or livestock coordinates.

Put another way, more localized geo-fences may be defined within the agricultural area based on the crop or livestock location data, thereby providing a means of further sub-dividing an agricultural area for more granular management of the agricultural area.

In a further example crop or livestock disorder location data is obtained. This may be obtained using image data, as described in more detail below or from a database. The crop or livestock disorder location data is representative of the global position of a crop or livestock disorder.

Based on the crop or livestock location data geographical crop or livestock disorder coordinate(s) can be derived and the geographical crop or livestock disorder coordinate and the crop or livestock disorder identity tag linked to each other.

The geographical crop or livestock disorder coordinate and crop or livestock disorder identity tag may be linked to the map of the agricultural region. In this way the crop or livestock disorder is linked to an agricultural area.

The method comprises predicting the spread of the crop or livestock disorder in the agricultural region based on at least one of the crop or livestock identity tag, the geographical crop or livestock coordinate and the geographical crop or livestock disorder coordinate. The prediction will often use each of the crop or livestock identity tag, the geographical crop or livestock coordinate and the geographical crop or livestock disorder coordinate. The technical effect of this is to provide the user with knowledge of where the disorder may spread to next. A user can then take action and precautions as necessary.

By providing information regarding disease and disorder spread a user may take proportionate and preventative measures, thus allowing yields to increase at minimal cost, and reduced environmental impact. In an example the method comprises obtaining meteorological data relating to the agricultural region and using the meteorological data relating to the agricultural region to predict the spread of the crop or livestock disorder in the agricultural region. This could include rainfall data, temperature data, wind speed data, wind direction data, humidity data and/or cloud cover data. For example, a crop disease may be spread by the wind and so the wind speed and direction can be used to predict the spread of a disorder in a particular direction over a particular distance. Similarly some disorders may be temperature dependent and may spread faster within a specific temperature range. For example, if the temperature is within a specific range the method may predict the spread of the disorder over a specific distance. The prediction can use the meteorological data to provide a more accurate prediction of the spread of the disorder.

The method may further comprise tracking a spread of the crop or livestock disorder based on the geographical crop or livestock disorder coordinate. The crop or livestock disorder can be tracked over time to analyse how far and fast it spreads.

In addition to identifying the crop or livestock in an agricultural region based on the image data, the image data may be further analyzed, for example once the crop has been identified, to determine a growth cycle stage of the crop. Further, the user may continue to acquire image data of the crops or livestock over time, which may be analyzed to track the growth of the crops or livestock over time, thereby providing real-time information on the productivity of an agricultural area.

Figure 4 shows a plan view 100 of a schematic representation of an agricultural region 110. In the example shown in Figure 4, soil characteristic data 400 relating to one or more characteristics of the soil within the agricultural area is obtained and linked 410 to the geo-fence of the agricultural area. The soil characteristic data may comprise one or more of: soil pH data; soil electrical conductivity data; total organic carbon data; soil texture characteristic data; available soil water capacity data; soil chemical composition data; and the like.

The soil characteristic data may be obtained by way of any suitable method. For example, the soil characteristic data may be obtained by way of a soil testing device, wherein the soil characteristic data is input to the mobile computing device automatically from the soil testing device or manually by the user. Alternatively, soil characteristic data may be stored on a remote database, such as a government topological database relating to the agricultural region, which may be accessed by the mobile computing device in order to obtain the soil characteristic data. Soil and topographical information uploaded by the user may be utilized to build a database of soil characteristics within the agricultural region which may be accessed by a plurality of users within a smallholder community in the agricultural area as a localized, bespoke resource. Soil characteristic data input by smallholders may be collated over time to form a soil monitoring system for soil issues, such as soil degradation.

In addition, or alternatively, to the soil characteristic data, environmental data relating to environmental characteristics of the agricultural region may be obtained and linked to the geo-fence of the agricultural area. The environmental data may comprise one or more of: weather data; climate data; pollution data; rainfall data; temperature data; wind speed data; humidity data; cloud cover data; and the like.

The environmental data may be obtained by way of any suitable method. For example, the environmental data may be obtained by way of a weather station, wherein the environmental data is input to the mobile computing device automatically from the weather station or manually by the user. Alternatively, environmental data may be stored on a remote database, such as a government weather database relating to the agricultural region, which may be accessed by the mobile computing device in order to obtain the environmental data. An example is satellite earth observation data.

The mobile computing device may be adapted to generate several recommendations to be provided to the user based on the soil characteristic data and/or the environmental data. For example, the mobile computing device may determine which crops are most suitable to be planted in the agricultural area based on the soil characteristic data and/or the environmental data. The most suitable crops may then be presented to the user, for example by way of the user interface, as recommendations for planting in the agricultural area.

In this way, the method of the invention may provide a means of guiding the user in making intelligent crop or livestock and soil management decisions for managing the agricultural area. Example recommendations that may be provided to the user may relate to one or more of: soil management; land area optimization; crop rotation; crop tending; interplanting; micro-dosing; composting; pasture rotation; and the like. In addition, the potential of weather analysis based on the environmental data may lead to recommendations relating to one or more of: improving the welfare of farming communities; reducing fertilizers used in an agricultural area; improved applications of insecticides and herbicides; improved management of water resources; and the like. Further recommendations may relate to encouraging the planting of native and orphan crops resilient to climate change effects. In addition, the data obtained from multiple users in a region containing multiple mapped agricultural areas may be analyzed in order to derive crop or livestock optimisation and planting suggestions for new crop or livestock varieties.

Establishment of farm boundaries by way of the geo-fencing of agricultural areas using a mobile computing device as described above provides a means for farmers to identify alternative and complementary or livestock crops suited to the specific soil characteristics and environmental data of the agricultural region.

Illiteracy is a common issue in some remote agricultural regions, meaning that recommendations provided to the user in the form of plain text may not be suitable in all applications of the invention. Accordingly, the recommendations may be provided to the user by way of accessible visual educational content delivered, for example, by way of the user interface of the mobile computing device. For example, a visual based representation of the recommendation may be provided to the user, thereby guiding them to practice new and improved smart farming methods regardless of access to education. Where more detailed or technical written information is required as part of the recommendations, an audible voice-over element in the local language may be provided to enable access to the information to less literate users.

Soil and environmental characteristics may change over time, for example due to soil degradation or climate change. Accordingly, further soil characteristic data and/or further environmental data may be obtained from the agricultural area at a time after the original soil characteristic data and/or further environmental data. The soil characteristic data and/or environmental data may be updated based on the further soil characteristic data and/or further environmental data. The recommendations to be provided to the user may then be updated based on the updated soil characteristic data and/or updated environmental data.

Put another way, the recommendations to be provided to the user, such as a recommended crop or livestock tending method or a recommended crop to be planted or livestock to be kept, may change over time according to the most up to date soil characteristic and environmental data captured in the agricultural area.

Further, changes in soil characteristics and/or environmental characteristics may be tracked over time based on the updated soil characteristic data the updated environmental data. The tracked changes in the soil characteristic and/or the environmental characteristics of the agricultural area may then form the basis for predicting a future change in soil characteristics and/or environmental characteristics. Recommendations for mitigating, or coping with, the predicted change in soil characteristics and/or environmental characteristics may then be generated and provided to the user. In other words, the soil and crop management data may be used in combination with the geo-fenced agricultural area and the additional climate change data to predict and warn farmers of possible changes to their soil and choice of crops or livestock. Further, the recommendations may be provided to a wider community of farmers in an agricultural region in order to help implement community based soil management strategies where larger scale changes are required.

Figure 5 shows a plan view 100 of a schematic representation of an agricultural region 110 comprising a plurality of geo-fenced agricultural areas 501, 502 and 503. In the example shown in Figure 5, the mobile computing device held by the user 120 comprises an imaging unit, such as a camera.

As described above with reference to Figure 3, image data representative of a crop or livestock within the agricultural area is obtained.

The image data may be processed, for example by way of a processing unit of the mobile computing device, in order to identify a crop or livestock disorder, such as a crop or livestock disease or a crop or livestock pest, based on the image data representative of the crop or livestock, thereby generating a crop or livestock disorder identity tag 510.

When determining both the crop or livestock identity and the crop or livestock disorder identity, the identification may be performed by way of a machine-learning algorithm applied to the image data.

A machine-learning algorithm is any self-training algorithm that processes input data in order to produce or predict output data. Here, the input data comprises image data representative of the crop or livestock and the output data comprises a crop or livestock identity or a crop or livestock disorder identity.

Suitable machine-learning algorithms for being employed in the present invention will be apparent to the skilled person. Examples of suitable machine-learning algorithms include decision tree algorithms and artificial neural networks. Other machine learning algorithms such as logistic regression, support vector machines or Naive Bayesian models are suitable alternatives.

The structure of an artificial neural network (or, simply, neural network) is inspired by the human brain. Neural networks are comprised of layers, each layer comprising a plurality of neurons. Each neuron comprises a mathematical operation. In particular, each neuron may comprise a different weighted combination of a single type of transformation (e.g. the same type of transformation, sigmoid etc. but with different weightings). In the process of processing input data, the mathematical operation of each neuron is performed on the input data to produce a numerical output, and the outputs of each layer in the neural network are fed into the next layer sequentially. The final layer provides the output.

Methods of training a machine-learning algorithm are well known. Typically, such methods comprise obtaining a training dataset, comprising training input data entries and corresponding training output data entries. An initialized machine-learning algorithm is applied to each input data entry to generate predicted output data entries. An error between the predicted output data entries and corresponding training output data entries is used to modify the machine-learning algorithm. This process can be repeated until the error converges, and the predicted output data entries are sufficiently similar (e.g. ±1%) to the training output data entries. This is commonly known as a supervised learning technique.

For example, where the machine-learning algorithm is formed from a neural network, (weightings of) the mathematical operation of each neuron may be modified until the error converges. Known methods of modifying a neural network include gradient descent, backpropagation algorithms and so on.

The training input data entries correspond to example image data representative of a crop. The training output data entries correspond to a crop identity or a crop disorder identity.

The use of a machine learning algorithm by the mobile computing device for crop disorder detection may provide real-time and reliable feedback to farmers for effective control and prevention of crop disorders. Further, as the identification of the crop disorder is combined with the geographical information of the geo-fenced agricultural area, the crop disorder may be identified and tagged with a geographical coordinate meaning that crop disorders such as tracking of pests and diseases may be tracked and their progression across farms, boundaries, states or countries can be predicted. Warnings may be provided to other farmers to guide them on how to protect against a detected disease or pest infestation in the agricultural region or an adjoining agricultural region or area.

By making the machine learning algorithm available for use by the mobile computing device when offline, farmers may quickly identify crop or livestock disorders regardless of the current connection status of the mobile computing device.

By way of an example, the user may capture image data of a crop or livestock using an imaging unit of the mobile computing device. The captured image data may include a representation of a leaf of the crop. A crop disease may be identified, for example, by analysing a colour or condition of the leaf. Alternatively, the image data may be representative of a crop or livestock pest, such as an insect, which may be identified based on characteristics of the pest, such as colour, wings, number of legs and the like.

As shown in Figure 5, crop or livestock disorder location data representative of the global position of the crop or livestock disorder may be obtained and a geographical crop or livestock disorder coordinate may be derived. The geographical crop or livestock disorder coordinate and the crop or livestock disorder identity tag 510 may be linked to each other and mapped to the map of the agricultural region, for example the crop or livestock disorder may be mapped as being present in agricultural area 501.

Additional crop or livestock disorder location data representative of an additional global position of the crop or livestock disorder, for example from agricultural area 502, and an additional geographical crop or livestock disorder coordinate may be derived. The spread 520 of the crop or livestock disorder may then be determined based on the geographical crop or livestock disorder coordinate and the additional geographical crop or livestock disorder coordinate, i.e. from agricultural area 501 to agricultural area 502.

In addition, the mobile computing device may determine a predicted spread 530 of the crop or livestock disorder from agricultural region 501 and/or 502 to the remaining healthy agricultural region 503. A warning may be generated based on the predicted spread of the crop or livestock disorder and presented to the user in order to encourage them to take action against the possible spread of the crop or livestock disorder. The mobile computing device may also generate one or more recommendations of how to treat the crop or livestock disorder in the agricultural area to be provided to the user, for example by way of the user interface in a manner described above.

Further, the mobile computing device may predict one or more crop or livestock disorders that may be likely to occur based on the crop or livestock identity tag and the geographical crop or livestock coordinate, the soil characteristics and/or the environmental data obtained by the mobile computing device. The mobile computing device may then generate one or more recommendations to be provided by to the user on how to prevent the one or more predicted crop or livestock disorders.

A further benefit to defining a geo-fence of an agricultural area is the ability to prove the existence of the farm, for example to financial institutions. Indeed, the identification of the mobile computing device and the geo-fence may be used to prove the existence and ownership of the agricultural area.

Thus, the invention may provide a trustworthy platform for farmers to access high quality smart farming tools and to act as an aid in establishing land ownership for smallholders who have acquired the agricultural area. This proof of ownership may help to user gain access to financial services hitherto inaccessible to traditional smallholders due to the inherent risk in investing or insuring smallholdings.

The methods described above may be utilized fortending crops and/or livestock within an agricultural area. It should be noted that any of the methods and devices referred to herein may be applied to the tending of both crops and livestock and are not limited to solely crops or solely livestock.

The methods described above may be performed by way of a mobile computing device, such as a smartphone. The mobile computing device according to an aspect of the invention comprises a global positioning unit adapted to obtain location data relating to the global position of the mobile computing device; a memory unit, wherein the memory unit is adapted to store a map of the agricultural region; and a processing unit in communication with the location data unit and the memory unit, wherein the processing unit is adapted to: derive one or more geographical coordinates from the location data; map the one or more geographical coordinates to the map of the agricultural region; and define a geo-fence representing a boundary of an agricultural area based on the one or more mapped geographical coordinates. Further, the mobile computing device may include an imaging unit adapted to capture image data relating to a crop or livestock.

By way of further example, Figure 6 illustrates an example of a mobile computing device 600 within which one or more parts of an embodiment may be employed. Various operations discussed above may utilize the capabilities of the computer 600. For example, one or more parts of a system for processing location data or crop image data with a CNN may be incorporated in any element, module, application, and/or component discussed herein. In this regard, it is to be understood that system functional blocks can run on a single computer or may be distributed over several computers and locations (e.g. connected via internet).

The computer 600 includes, but is not limited to, PCs, workstations, laptops, PDAs, palm devices, servers, storages, smartphones, smart watches and the like. Generally, in terms of hardware architecture, the computer 600 may include one or more processors 601, memory 602, and one or more I/O devices 607 that are communicatively coupled via a local interface (not shown). The local interface can be, for example but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface may have additional elements, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface may include address, control, and/or data connections to enable appropriate communications among the aforementioned components.

The processor 601 is a hardware device for executing software that can be stored in the memory 602. The processor 601 can be virtually any custom made or commercially available processor, a central processing unit (CPU), a digital signal processor (DSP), or an auxiliary processor among several processors associated with the computer 600, and the processor 601 may be a semiconductor based microprocessor (in the form of a microchip) or a microprocessor.

The memory 602 can include any one or combination of volatile memory elements (e.g., random access memory (RAM), such as dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and non-volatile memory elements (e.g., ROM, erasable programmable read only memory (EPROM), electronically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), tape, compact disc read only memory (CD-ROM), disk, diskette, cartridge, cassette or the like, etc.). Moreover, the memory 602 may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory 602 can have a distributed architecture, where various components are situated remote from one another, but can be accessed by the processor 601.

The software in the memory 602 may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The software in the memory 602 includes a suitable operating system (O/S) 605, compiler 604, source code 603, and one or more applications 606 in accordance with exemplary embodiments. As illustrated, the application 606 comprises numerous functional components for implementing the features and operations of the exemplary embodiments. The application 606 of the computer 600 may represent various applications, computational units, logic, functional units, processes, operations, virtual entities, and/or modules in accordance with exemplary embodiments, but the application 606 is not meant to be a limitation.

The operating system 605 controls the execution of other computer programs, and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. It is contemplated by the inventors that the application 606 for implementing exemplary embodiments may be applicable on all commercially available operating systems.

Application 606 may be a source program, executable program (object code), script, or any other entity comprising a set of instructions to be performed. When a source program, then the program is usually translated via a compiler (such as the compiler 604), assembler, interpreter, or the like, which may or may not be included within the memory 602, so as to operate properly in connection with the O/S 605. Furthermore, the application 606 can be written as an object oriented programming language, which has classes of data and methods, or a procedure programming language, which has routines, subroutines, and/or functions, for example but not limited to, C, C++, C#, Pascal, BASIC, PYTHON, API calls, HTML, XHTML, XML, ASP scripts, JavaScript, FORTRAN, COBOL, Perl, Java, ADA, NET, and the like.

The I/O devices 607 may include input devices such as, for example but not limited to, a mouse, keyboard, scanner, microphone, camera, etc. Furthermore, the EO devices 607 may also include output devices, for example but not limited to a printer, display, etc. Finally, the I/O devices 607 may further include devices that communicate both inputs and outputs, for instance but not limited to, a NIC or modulator/demodulator (for accessing remote devices, other files, devices, systems, or a network), a radio frequency (RF) or other transceiver, a telephonic interface, a bridge, a router, etc. The I/O devices 607 also include components for communicating over various networks, such as the Internet or intranet.

If the computer 600 is a PC, workstation, intelligent device or the like, the software in the memory 602 may further include a basic input output system (BIOS) (omitted for simplicity). The BIOS is a set of essential software routines that initialize and test hardware at startup, start the O/S 605, and support the transfer of data among the hardware devices. The BIOS is stored in some type of read-only-memory, such as ROM, PROM, EPROM, EEPROM or the like, so that the BIOS can be executed when the computer 600 is activated.

When the computer 600 is in operation, the processor 601 is configured to execute software stored within the memory 602, to communicate data to and from the memory 602, and to generally control operations of the computer 600 pursuant to the software. The application 606 and the O/S 605 are read, in whole or in part, by the processor 601, perhaps buffered within the processor 601, and then executed.

When the application 606 is implemented in software it should be noted that the application 606 can be stored on virtually any computer readable medium for use by or in connection with any computer related system or method. In the context of this document, a computer readable medium may be an electronic, magnetic, optical, or other physical device or means that can contain or store a computer program for use by or in connection with a computer related system or method. The application 606 can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this document, a "computer-readable medium" can be any means that can store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality.

A single processor or other unit may fulfill the functions of several items recited in the claims.

The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

If the term "adapted to" is used in the claims or description, it is noted the term "adapted to" is intended to be equivalent to the term "configured to".

Any reference signs in the claims should not be construed as limiting the scope.