Systems And Methods For Rendering Disease Data For Agricultural Fields Through Improved Interfaces

Systems and methods are provided for directing crop disease treatments to agricultural fields. An example computer-implemented method includes receiving a user input from a user at a communication device associated with an interface indicative of likelihood of a first disease in a plurality of agricultural fields and, in response, determining a likelihood of occurrence of the first disease for ones of the plurality of agricultural fields. The method then also includes generating an interface indicative of the likelihood of occurrence of the first disease in the ones of the plurality of agricultural fields and causing the interface to be displayed at the communication device associated with the user.

FIELD

The present disclosure generally relates to systems and methods for providing improved interfaces for rendering disease data for agricultural fields, generally, in real time.

BACKGROUND

In agricultural fields, crops are planted, grown and harvested. The performance of the crops in the agricultural fields is often measured in terms of yield. The performance of the crop may be based on a number of factors, including, for example, soil properties, weather, etc. In addition, management practices, such as treatments (e.g., pesticides, fertilizers, etc.), irrigation, etc., is also known to impact the performance of the crops in the agricultural fields.

SUMMARY

Example embodiments of the present disclosure generally relate to computer-implemented methods for use in rendering interface(s) indicative of likelihood of occurrence and/or severity of disease(s) in an agricultural field, based on disease observation data and, in connection therewith, to directing crop disease treatments to the agricultural field. In one example embodiment, such a method generally includes: receiving, by a computing device, a user input from a user at a communication device associated with an interface indicative of likelihood of a first disease in a plurality of agricultural fields; accessing, by the computing device, data associated with the agricultural fields and a disease model, the data associated with the agricultural fields including disease observation data indicative of an observation of the first disease in a first field of the agricultural fields, the disease observation data including a temporal indicator, an identifier of the first disease, and an identifier of the first field; determining, by the computing device, the likelihood of occurrence of the first disease for ones of the plurality of agricultural fields, based on: (i) a model value based on the disease model; and (ii) a tuning value, using a spatially misaligned regression; wherein the tuning value is based on at least: a distance between a location of the first field and locations of the ones of the plurality of agricultural fields; generating, by the computing device, an interface indicative of the likelihood of occurrence of the first disease in the ones of the plurality of agricultural fields; and causing the interface to be displayed at the communication device associated with the user.

Example embodiments of the present disclosure also generally relate to systems for use in rendering interface(s) indicative of likelihood of occurrence of disease(s) in an agricultural field, based on disease observation data, and to directing crop disease treatments to agricultural fields. In one example embodiment, such a system comprises at least one computing device configured to: receive a user input from a user at a communication device associated with an interface indicative of likelihood of a first disease in a plurality of agricultural fields; access data associated with the agricultural fields and a disease model, the data associated with the agricultural fields including disease observation data indicative of an observation of the first disease in a first field of the agricultural fields, the disease observation data including a temporal indicator, an identifier of the first disease, and an identifier of the first field; determine the likelihood of occurrence of the first disease for ones of the plurality of agricultural fields, based on: (i) a model value based on the disease model; and (ii) a tuning value, using a spatially misaligned regression; wherein the tuning value is based on at least: a distance between a location of the first field and locations of the ones of the plurality of agricultural fields; generate an interface indicative of the likelihood of occurrence of the first disease in the ones of the plurality of agricultural fields; and cause the interface to be displayed at the communication device associated with the user.

Example embodiments of the present disclosure also generally relate to non-transitory computer readable storage media including computer-executable instructions for use in rendering interface(s) indicative of likelihood of occurrence and/or severity of disease(s) in an agricultural field, based on disease observation data, which when executed by at least one processor, cause the at least one processor to perform one or more of the above operations.

DETAILED DESCRIPTION

Growers make decisions related to the planting, treating, and/or harvesting of crops in agricultural fields, based on available data, prior experience, etc., often with the aim of enhancing the performance of the crops. One example decision relates to application, or non-application, of one or more treatments to agricultural fields, to prevent or treat one or more potential crop diseases. In general, given the resources expended by the treatment, a grower is likely to decide to apply the treatment based on an expected impact on the performance of the treated crop (versus untreated crop). The expected impact is difficult to understand in the absence of accurate and up-to-date data about occurrences and/or severities of disease, which may be pertinent to specific agricultural fields, and also neighboring agricultural fields. The lack of accurate and up-to-date data causes poor decisions in treatments, which, generally, lead to underuse and associated damaged crops or overuse and the associated unnecessary resource expenditure, etc. One technique used to rely on available data includes modeling of the disease(s), whereby the model is trained based on available data, and then used to predict the disease(s). Because model training (and validation) is time consuming, additional observations may occur, which are pertinent to the likelihood of occurrence and/or severity of the disease(s) but are not relied on in training (or validating) the model. The lack of consideration of this up-to-date data leads in inaccuracies in the disease prediction, which may result, again, in underuse or overuse of treatments.

Uniquely, the systems and methods herein provide for leveraging observation data immediately to render interface(s) indicative of likelihood of occurrence and/or severity of disease(s) in agricultural fields, whereby up-to-date data is used (e.g., in real time, etc.), in combination with modeling, to predict disease(s).

In particular, after training one or more disease models related to one or more diseases, additional disease observations may be reported. The new disease observation(s) is/are compiled into a tuning value, which augments the model value to provide up-to-date prediction of disease for one or more agricultural fields. The up-to-date prediction is rendered, per field or otherwise, to provide a grower, for example, a direct view of the impact of the disease observation(s) on the likelihood of occurrence and/or severity of the one or more diseases. In this manner, full use of the available data in the form of one of one or more interfaces is provided to enhance accuracy of likelihoods of occurrence of the one or more diseases, while also extending the life-cycle of the trained disease models.

FIG.1illustrates an example system100in which one or more aspect(s) of the present disclosure may be implemented. Although the system100is presented in one arrangement, other embodiments may include the parts of the system100(or other parts) arranged otherwise depending on, for example, types of crops, types of crop diseases present in growing spaces; types and/or locations of agricultural fields; and/or privacy and/or data requirements; etc.

The system100generally includes a plurality of agricultural fields102, which may be of any suitable size, acreage, etc. The agricultural fields102should be understood to include plots, sub-fields, greenhouses, etc. (broadly, growing spaces), wherein each of the agricultural fields102is a physical location, in which crops are planted, grown and harvested, etc. The agricultural fields102are defined by boundaries, which are, in turn, defined by growers, location, analysis, or other suitable techniques. The agricultural fields102may also be organized into regions, or otherwise. For example, the agricultural fields102may be organized by the owner and/or grower thereof.

The agricultural fields102, then, are associated with data indicative of the crops in the fields (or to be planted in the fields), and also the conditions of the agricultural fields102, such as, for example, soil data, weather data, etc. In connection therewith, the data is gathered at or from the agricultural fields102. The data may be gathered manually, or automatically, for example, by farm equipment, sensors associated with the farm equipment or apart therefrom (e.g., located in the fields102, traversing the fields102, etc.), etc. The data may include plant/seed identifiers, plant/seed types, crop disease identifiers and/or types, crop disease observations (e.g., existence, severity, etc.), observation dates, planting dates, growing temperature days, location data, field identifiers, soil conditions (e.g., moisture, drainage, etc.), plant performance (e.g., height, strength, yield, etc.) (e.g., at one or more regular or irregular interval(s), etc.), plant growth stages, treatments, weather conditions (e.g., precipitation, temperature, humidity, etc.), field topology (e.g., elevation, change in slope, surrounding terrain, etc.), management practices (e.g., crop rotation, fungicide application, tiling, etc.), and other suitable data to identify the seed/plant, a performance of the seed/plant, crop diseases associated with the seed/plant, etc., in the agricultural fields102.

In addition, the data includes crop disease data. Crop disease may be identified via visual inspection, qPCR testing, a specified test protocol, and/or any other suitable techniques for determining whether a disease is present or not, in the crop in the agricultural field102. When disease is identified, disease observation data is compiled to indicate a presence (e.g., 1 for disease present, 0 for disease not present; etc.) or a severity of the disease on a scale, such as an integer scale from 0 to 9, a ranking of level 1, level 2, a percentage from 0 to 100% of the leaf area with visible symptoms, etc. The crop disease observations data may also include an identification of the agricultural field (e.g., Field1234, etc.), the type of crop, the type of disease (e.g., corn grey leaf spot, soybean frogeye leaf spot, corn northern leaf blight, corn southern rust, soybean white mold, etc.), and potentially, severity, etc. The crop disease observation data may also include a location of the observation (such as a latitude and longitude of the agricultural field102), etc. That said, other suitable data may be included for specific crops, diseases, etc., as desired or required.

The data for the agricultural fields102may be gathered over a single year, or multiple years. As such, the data may be indicative of the agricultural fields102for one year, fives years, ten years, or twenty years, and may include all, or at least a portion of the data above for each of the years.

With continued reference toFIG.1, the system100also includes farm equipment106(e.g., agricultural machines, etc.), a data server108(or multiple data servers), and an agricultural computer system116, each of which is coupled to (and is in communication with) one or more network(s). The network(s) is/are indicated generally by arrowed lines inFIG.1, and may each include, without limitation, one or more of a local area network (LAN), a wide area network (WAN) (e.g., the Internet, etc.), a mobile/cellular network, a virtual network, and/or another suitable public and/or private network capable of supporting communication among parts of the system100illustrated inFIG.1, or any combination thereof.

It should be appreciated that while illustrated separately, the data server108and the agricultural computer system116may be integrated together, in whole or in part. In general, however, in this example embodiment, the data described above is stored in the data server108.

Further, in this example embodiment, the farm equipment106may include, without limitation, one or more harvesting devices, sprayers, planters, etc. In particular in the illustrated system100, as shown inFIG.1, the farm equipment106may include one or more of a combine, a picker, or other mechanism for harvesting plants/crops, etc. Additionally, or alternatively, the farm equipment106may include other land-based or air-based equipment, such as, for example, unmanned aerial vehicles (UAVs), manned aerial vehicles (MAVs), tillers, irrigators, or other suitable equipment, configured to carry out one or more operations at the agricultural fields102, such as, for example, surveying, image capture, tilling, application of treatments, watering, etc.

It should also be appreciated that a different number and/or type of farm equipment may be included in other system embodiments. For instance, the system100may include multiple of the same or different farm equipment for performing desired task at the fields102.

The farm equipment106is configured to measure, capture, or identify data, and additionally to compile data, which are specific to the crop and/or agricultural fields102as the equipment is performing one or more defined tasks related to the crop or agricultural fields102, etc. The data may include, without limitation, rates, soil compositions, times, dates, yield, weights, applications, moisture content, volumes, flow, or other suitable data, etc., relating to treatments, irrigation, harvested crops, etc. Moreover, in this example, the farm equipment106may be configured to track their locations at given times, as each traverses the agricultural fields102, as expressed in latitude/longitude coordinates, or otherwise, and to correlate the locations to other data gathered/compiled by the farm equipment106(e.g., permitting the data to be correlated to a specific plant and/or seed based on planting data for the growing spaces, etc.).

The farm equipment106is further configured to transmit the compiled data to the data server, either directly or via the agricultural computer system116, which stores the data therein (and/or in the data server108). That said, a different number of data servers may be included in other system embodiments, with the different data servers each potentially being specific to certain ones (or more) of the agricultural fields102or regions, or farm equipment, etc.

In the example embodiment, the system100further includes a user103, who generally includes a grower, operator, or worker associated with one or more of the agricultural fields102. The user103is associated with and/or responsible for making and/or implementing decisions related to the agricultural fields102, for example, for planting, treatments, irrigation, harvesting, etc.

It should be understood that the user103may include one person, or multiple people, where each is associated with one or more specific tasks related to the agricultural fields102.

As shown inFIG.1, the user103is associated with a communication device104(e.g., as a field manager computing device, etc.), which is generally a computing device (e.g., mobile device, etc.), which may be configured to provide access to the user103to various agricultural data. In particular, in this example embodiment, the communication device104is configured by an application105, to provide access to the data, or to permit the user103to enter, modify, delete, etc. data. The application105is a mobile application, which may include, for example, the CLIMATE FIELDVIEW application and/or associated tools, commercially available from Climate LLC, Saint Louis, Missouri. The application105may further include a browser in cooperation with the CLIMATE FIELDVIEW application and/or associated tools. The communication device104is configured, by the application105, to access data from, and, potentially, to receive data from and/or provide data to different devices inFIG.1, via the one or more networks (as indicated by the arrowed lines). Data input by the user103may include, for example, without limitations, planting data (e.g., seed type, seed rate, etc.), treatment data (e.g., fungicide application, etc.), field boundary indicators, disease observation data, or other relevant data observed or known by the user103, etc.

The communication device104associated with the user103may be further configured to transmit the gathered data, directly or via the agricultural computer system116, to the data server108, which stores the data therein.

The data server108is configured to store the data described herein in one or more data structures. In general, in this example embodiment, the data server108is configured to store data by year (e.g., Year_X, Year_X+1, etc.), by grower, and/or otherwise, etc. Then, for each year or grower or grower/year, the data includes data for each of the agricultural fields102including, for example (and without limitation), disease observation data, performance data, identifier of crops, brands for seeds, relative maturity, planting dates, temperature days, growing mode of action, prior crops, types of traits or trait stacks, treatments, positions/distributions of seeds in the agricultural fields102(e.g., seeding rates, etc.), location definitions of the agricultural fields102or of seeds within the agricultural fields102(e.g., field boundaries, latitude and longitude, centroid or other boundary, etc.), acreage of the growing spaces, populations of seeds planted in the agricultural fields102, yields and harvest grain moisture (e.g., based on location and seed products, etc.), etc. The data may also include soil conditions (e.g., soil moisture, drainage levels, etc.), field elevations (which may include slopes of a plot, surrounding terrain information, etc.), precipitation amounts, relative humidity, temperature, solar radiation, irrigation amounts, management practices (e.g., crop rotation, fungicide application, tiling, drainage, etc.) or any other data indicative of the growing conditions for the seeds/plants in the given agricultural fields102, etc.

It should be appreciated that any available and/or desired data may be collected and/or received (or obtained) with regard to the agricultural fields102. What's more, the data included in the data structure(s) of the data server108may be augmented with additional information about the crops and/or agricultural fields102from one or more other sources, such as, for example, external data120from external data server122. The external data server122may include one or more additional entities, which is configured to capture and store data relevant to the agricultural fields102. One example external data server122includes a weather source, while other examples include soil data sources, treatment data sheets sources, boundary data sources (e.g., boundary definitions, centroids, etc.), field topology sources, etc.

Given the above, in this example embodiment, the agricultural computer system116is configured to cooperate with the communication device104to display various interfaces to the user103(at the communication device104), which may be viewed by the user103in order to aid the user103in decisions related to the agricultural fields102.

In particular, the likelihoods of occurrence and/or severity of one or more diseases in the agricultural fields102is determined based on a number of different factors, which are generally reflected in the data obtained (by the farm equipment106, the agricultural computer system116, and the communication device104) in/from the data server108. In connection therewith, the agricultural computer system116is configured to access the data therein (and from the external data server122) and to train a model (e.g., a disease model, etc.) to determine likelihoods of occurrence and/or severity of one or more diseases occurring in one or more of the agricultural fields102, based on the data included in the data server108, on or before a given training date (i.e., available data is used to train the model).

The trained model may include any suitable model, including, for example the joint disease model described in Applicant's U.S. Provisional App. No. 63/438,975, filed Jan. 13, 2023, which is incorporated herein by reference in its entirety, and which is based on a latent Gaussian process, etc. Once trained, the model may be retrained at one or more regular or irregular intervals, as additional data, as described above, becomes available from the agricultural fields102in the data server108or external server122.

The agricultural computer system116is configured to store the trained model therein, and then also to generate, based on the trained model, likelihoods of occurrence and/or severity of one or more diseases, for each of the agricultural fields102in the system100, or a subset thereof. In connection therewith, the communication device104is configured, by the application105, to request one or more disease interfaces, from the agricultural computer system116, whereby the agricultural computer system116is configured to provide the disease data in one or more forms to the communication device104. For example, the agricultural computer system116may be configured to provide the disease data as structured data, for example, in a disease map interface, which includes the disease data over a regional map view of the agricultural fields102. Upon receipt of the disease map interface (or other disease interface), the communication device104is configured to render the disease map interface (or other interface) for viewing by the user103.

FIG.2Aillustrates an example interface200, which is displayed at the communication device104and which includes a disease map for the agricultural fields102for the northern leaf blight disease. The likelihoods of occurrence of the disease, then, are visually represented by a color located at a position in the map corresponding to ones of the agricultural fields102. The different colors then, in this example, illustrate the different likelihoods of occurrence of the specific disease. In particular, the interface200includes a likelihood scale202, which illustrates the likelihoods of occurrence of the disease in a color change from one darker color (e.g., purple, etc.) to a lighter color (e.g., yellow, etc.). It should be understood that any different colors, or transitions between the colors may be used in other interface embodiments to illustrate the likelihoods of occurrence of the disease. Moreover, colors may represent disease data other than likelihood of occurrence, such as, for example, severity from 0 to 9 (or other suitable range), disease damage from 0 to 100%, etc. More generally, it should be appreciated that other visual distinctions, such as, for example, patterns, hatching, etc., may be used to indicate the different likelihoods of occurrence of a specific disease in other interface examples. As shown inFIG.2A, the scale202runs from 0.0 to 1.0, which generally, indicates no chance (0% chance) to certain (100% chance). Different scales may also be included in other embodiments to inform the user103about the potential for disease occurrence.

It should be appreciated that the agricultural fields102included in the interface200may include agricultural fields in a given region, agricultural fields associated with a specific grower (e.g., the user103, etc.), agricultural fields associated with a given treatment application, and/or agricultural fields specific to a user input, etc. The agricultural fields102may further be filtered based on region, location, grower (or location of the user103), or user input criteria, etc., in the interface200, to show only ones of the desired agricultural fields102consistent with the filter criteria.

FIG.2B, for example, illustrates a grower specific interface204, which may be selected in the communication device104. In particular, from a pulldown, checkbox, text, or other input, the user103may select the grower's agricultural fields, or a subset thereof, to be presented in a tile-view, and in response, the communication device104is configured, by the application105, in connection with the agricultural computer system116, to display the tile-view interface204. In the tile-view interface204, each agricultural field is shown separately, with boundaries, in a grid or other pattern of agricultural fields, yet with likelihoods of occurrence of one or more diseases shown in the color of each agricultural field. It should be appreciated, again, that other visual distinctions may be included to depict the chance of occurrence and/or severity of the one or more disease (e.g., different patterns, hatching, etc.). Also, as shown inFIG.2B, the interface204includes labels for each of the agricultural fields (e.g., Field585, Field1840, etc.), so that the user103is able to distinguish the agricultural fields by their shape or by the labels.

As shown, the tile view interface204also includes the scale202, which, like above, defines the colors as indicative of particular likelihoods of occurrence of the specific disease.

FIG.2Cillustrates an example line-graph interface206, which may be selected in the communication device104. In particular, from a pulldown, checkbox, text, or other input, the user103may select the grower's agricultural fields, or a subset thereof, to be presented in a line-graph view, and in response, the communication device104is configured, by the application105, in connection with the agricultural computer system116, to display the line-graph interface206. In the line-graph interface206, each agricultural field is shown separately as a line in the graph, where the line indicates the likelihood of occurrence of the color-coded disease over time. The color-codes are shown in the key in the upper left corner of the interface206, with a first color being gray leaf spot (GLS), a second color being northern leaf blight (NLB), a third color being southern rust (SR), a fourth color being white mold (WM), and a fifth color being frogeye leaf spot (FLS). When a line is selected, the communication device104is configured, by the application, to display a lightbox over the interface206, which includes details of the agricultural field (e.g., identifier, size, crop, likelihood of occurrence of the specific disease, etc.). It should be appreciated that the line-graph interface206may include other forms or formats of graphs, lines, objects, shapes, etc., in other interface embodiments, etc., indicating one or more of the same or other relevant diseases, etc.

That said, it should be appreciated that the interfaces204,206ofFIGS.2B and2C, respectively, generally represent alternative manners of illustrating the grower information fromFIG.2A. To this point, it should also be appreciated that such information may be shown in still other interfaces in other example embodiments.

Referring again toFIG.2A, as noted above, the interface200is illustrated as being associated with the likelihood of occurrence of northern leaf blight (NLB), whereby the coloring is indicative of the specific likelihoods of occurrence (as indicated above). As shown inFIG.2A, the user103may interact with a further interface208, which is included in or overlaid on the interface200, to provide for a selection to a pulldown for different diseases. As shown, the user103may also (or alternatively) select the frogeye leaf spot disease instead of norther leaf blight, whereby the communication device104is configured, by the application105, in connection with the agricultural computer system116, to modify the interface200to show colors indicative of the likelihoods of occurrence of frogeye leaf spot in the agricultural fields, instead of norther leaf blight. Again, the color or other visual distinction for each of the agricultural fields102then indicates the chances of occurrence the selected disease. It should be appreciated that the interface208may be used to select other diseases, such as, for example, corn grey leaf spot, corn southern rust, soybean white mold, corn tar spot, soybean brown spot, etc., or to otherwise alter the interface200according to a desire of the user103. While not shown, a similar interface208may be included with (or overlaid on) the interface204,206ofFIGS.2B and2C, respectively, in other example embodiments.

In connection therewith, the user103may access the interface200, as shown, or as modified through the interface208or similar interfaces, or in the other interfaces204,206, to understand the likelihood of occurrence and/or severity of one or more diseases on or near ones of the agricultural fields102, for which the user103is to make decisions related to treatment, etc.

As explained above, the model herein is trained based on available data at a given time. Logically, additional disease observations may be generated after the training of the model. It should be appreciated that from time to time, as indicated above, the model may be retrained to account for the new disease observation data. In connection therewith, the model is trained and validated, before being accessible to the user103, through the associated application105. As such, even with the retraining, while the retraining is occurring or some interval associated with retraining or validation, there is potential for the model to generate likelihoods of disease occurrence, which is incomplete as to additional, subsequently generated disease observations.

In this example embodiment, the agricultural computer system116is configured to tune the likelihood of disease occurrence, form the model, based on one or more disease observations.

In particular, in connection with the inspection of one or more of the agricultural fields102, the user103may observe the presence of a disease. Based on the observation, the user103submits the disease observation to the agricultural computer system116. Specifically, with reference toFIG.2A, the user103may drop a pin210in the interface200, where the location of the pin is specific to one of the agricultural fields102, in which the disease is observed. The communication device104is configured, by the application105(e.g., CLIMATE FIELDVIEW application tool referred to as Scouting Notes or from other similar entry applications, tools, or features, etc.), to display the interface200and to accept the pin210from the user103, via a continued touch, or double click, or other input from the user103on the interface200at that location. It should be appreciated that the communication device104may be configured, by the application105, to solicit additional information from the user103about the observed disease, such as, for example, date/time, manner of observation, type of disease, severity, etc.

It should also be appreciated that while a pin drop is used in the example inFIG.2A, other manners of inputting the disease observation may be used. For example, the user103may input the field name and/or field number and disease type to a text input (or selection input), through one or more other interfaces. For example, the user103may double click on a specific agricultural field in interface204ofFIG.2B, whereby the communication device104is configured, by the application, to display a lightbox to solicit details of the observation and to accept the disease observation, from the user103, based on selecting a submit button in the lightbox, for that specific agricultural field. Or, the user103may drop a pin in one of the specific fields in the interface204, resulting in the same operation of the communication device104.

Regardless of the manner of inputting the disease observation, the communication device104is configured, by the application, to transmit disease observation data indicating the disease observation to the agricultural computer system116. Table 1, for example, illustrates example disease observation data that may be received from the communication device104or other computing device (e.g., another communication device, etc.), for one of the agricultural fields102(having Field ID A). As shown, in this example, the disease observation data includes a field identifier, a crop type, a disease type, a severity rating and an observation date (or temporal indicator).

It should be appreciated that the disease observation data may include more or less data in other embodiments. For example, the crop and/or the severity of the disease may be omitted in the disease observation data, while other data may be included in other examples.

Based on the disease observation data, the agricultural computer system116is configured to generate a tuning value, for each of the agricultural fields (as presented by the colors per agricultural field in interface200, for example). Specifically, new likelihoods of occurrence of disease are based on the original risk indicated by the model value (wi) and also the tuning value (Σi,ZTΣZ−1Z), which is provided by the following expression of a spatially misaligned regression:

In the above expression, Y is the regular training data; T is an operator for transpose; Z is a vector of data indicative of the new observation data, where the length of the matrix is based on the number of agricultural fields and the value is indicative of disease (1) (or other suitable value) or no disease (0); and wiis the value from the trained disease model (e.g., joint disease model, etc.) for agricultural field i. It should be appreciated that an existing value for wimay be retrieved from memory, where it was previously determined for the specific agricultural field, or the agricultural computer system116may be configured to determine the value for wiin connection with the above expression to determine the new likelihood of occurrence to, for example, account for additional model-specific data such, as for example, weather data, etc. In one example, the trained model may be executed daily to determine the value for wiand then during that day, the value for wimay be retrieved in connection with the above, thereby enabling the trained model to account for up-to-date data (e.g., weather data, etc.), while also limiting processing resource in connection with multiple determinations of the new likelihood of occurrence (e.g., only determine tuning value, etc.).

The variable Σ is the cross-covariance matrix between Y and Z, which is expressed as provided below:

In this expression, covariance is determined between disease risk on agricultural field i and agricultural field j; and d and d′ are the diseases of interest on field i and field j respectively (e.g., disease of interest versus disease included in observation data, etc.) multiplied by disease effect matrix B. In particular, the matrix B indicates a correlation between different diseases. For example, where the disease model is a joint disease model, which indicates the likelihood of occurrence of a given disease (e.g., see interface206, etc.), the matrix B includes a value for how each disease impacts each other disease (e.g., the effect of NLB on grey leaf spot, etc.). The matrix B thus defines a dimension based on number of diseases considered (e.g., 5×5 for five diseases, etc.), which is indicative of impact of one disease on another (e.g., 1 for same disease, but otherwise for differing diseases, etc.).

Additionally, s-s′ is the distance between agricultural field i and agricultural field j; and t-t′ is the time between the observation in agricultural field i and the current time. Also, Φ1and Φ2are spatial and temporal decay parameters, respectively, that are learned and/or tuned during training the model. The parameters account for diminishing effect diseases have on each other as the distance between the agricultural field and the disease observation grows. For example, the parameters may indicate that NLB on field i will have a stronger effect on GLS on a nearby field j than on a very distant field k.

It should be appreciated that the above expressions may be different in other examples, or may be changed and/or altered based on the data to be considered in adjusting the likelihoods of occurrence and/or severity of the one or more diseases. For example, time differences may be omitted, or distances may be omitted for a certain set of agricultural fields (e.g., only fields within a few miles, etc.), or differences in disease may be omitted for certain diseases, or models, or distances in some directions (east-west) may be treated differently from distances in other directions (north-south), or the disease-space-time correlations may not be multiplicatively separable, etc.

Based on the above, the agricultural computer system116is configured to determine the likelihoods of occurrence and/or severity of the specific disease, for each of the agricultural fields, based on the model value and the tuning value. Next, the agricultural computer system116is configured to generate (or regenerate or update) the interface200(e.g., update the interface200, etc.) to include the likelihoods of occurrence of the specific disease, whereby the coloring thereof may change. The updates are then illustrated in interface212ofFIG.2D. As shown, for example, in the interface212, the coloring indicative of the likelihoods of occurrence of the norther leaf blight disease is changed (as compared to the interface200inFIG.2A) thereby taking into account the new disease observation represented by the pin210. In this manner, in this exemplary embodiment, the agricultural computer system116is configured to respond to the pin210(i.e., determining the likelihoods of occurrence and/or severity and generating the interface) in real time or near real time to update the interface200to be the interface212. In this example, real time includes within milliseconds, a few seconds, or up to ten seconds, while near real time may include from more than ten seconds, up to a minute, or up to three minutes, or up to five minutes, etc.

Based on the interface in the communication device104(which includes the new/updated likelihoods of occurrence and/or severity of the specific disease), the user103may respond in one or more different ways. For example, if the time series forecast inFIG.2Cindicates disease risk will increase on a majority of the agricultural field for which the user103is responsible in the near future, the user103may direct an application of one or more fungicides (e.g., whereby an input is provided from the grower (via the communication device104) to the agricultural computer system116indicating fungicide, timing, agricultural fields(s), etc. In connection therewith, the agricultural computer system116may be configured to generate one or more scripts (e.g., via script generation instructions305, etc.), in response to the input from the user103, to effect application of the one or more fungicides in the specific agricultural fields (e.g., via equipment106, etc.). In another example, where the user103sees on the tile view inFIG.2Bthat many of the agricultural fields for which the grower is responsible have low risk, the grower may opt to not proceed with one or more fungicide, through understanding that the overall risk to those agricultural fields is limited. In another example, where the user103scouts one or more of the agricultural fields102and witnesses the disease, the grower may then input another observation(s) (consistent with the above) to again view the likelihood of occurrence in other surrounding fields. The user103may rely on the new likelihoods of occurrence to schedule the fungicide application, through the agricultural computer system116or otherwise, etc.

Apart from the specific response of the user103, it should be appreciated that additional disease observation data may be entered from time to time by the user103or other growers/users in the region (before the model is retrained), as observed in the agricultural fields102or other agricultural fields in the region, with the likelihoods of occurrence of one or more diseases in the agricultural fields being determined/updated, in real time or near real time, through the tuning value accordingly as described above. That is, the agricultural computer system116is configured to determine the likelihoods of occurrence of the disease based on any additional observations through the tuning value above, and to display the same through various interfaces in the communication device104, whereby the user103is able to further respond as explained above.

Subsequently, at some time thereafter, the agricultural computer system116is configured to retrain the model (e.g., which is represented by wi) based on a prior training set of data for the model and also some or all of the observation data entered through the communication device104, by the user103, or other users to the agricultural computer system116. After training, the model is again used as above, with new observations after the retaining again being accounted for through the tuning value.

FIG.3illustrates an example method300for rendering interface(s) indicative of likelihood of occurrence of disease(s) in agricultural fields, based on disease observation data. The example method300is described herein in connection with the system100, and may be implemented, in whole or in part, in the agricultural computer system116of the system100. However, it should be appreciated that the method300, or other methods described herein, are not limited to the system100or the agricultural computer system116. And, conversely, the systems, data structures, and the computing devices described herein are not limited to the example method300.

At the outset in the method300, the agricultural computer system116receives, at302, a request from the user103(broadly, an input) to view one or more interfaces, which are indicative of likelihood of occurrence and/or severity of one or more disease(s) in one or more agricultural fields (e.g., one or more of the fields102, etc.). The request may include an indication of one or more specific diseases, designation(s) of one or more agricultural fields, and/or other parameters of what the user103wants to be included in the one or more interfaces, etc. Further, the request may be received, by the agricultural computer system116, from the communication device104, through the application105therein (e.g., the CLIMATE FIELDVIEW application, commercially available from Climate LLC, Saint Louis, Missouri.).

In response to the request, the agricultural computer system116accesses, at304, data consistent with the request (e.g., from the data server108, etc.). The data includes relevant data for the trained model described above to generate a likelihood of occurrence and/or severity of one or more diseases in the agricultural fields102indicated by the request, and also any additional observation data (if any, for example, since training of the model) in the region of the agricultural fields (e.g., within the state, threshold mileage, etc.), or associated with the agricultural fields (e.g., having the same crop, by the same grower, etc.).

The agricultural computer system116also accesses the trained model and the algorithm described above, and associated matrices, weights, variables, etc.

At306, the agricultural computer system116determines a likelihood of occurrence and/or severity of the one or more diseases for ones of the agricultural fields102. In particular, consistent with the above, the agricultural computer system116determines a model value for one of the agricultural fields102, which is based on the trained model and associated data related to the agricultural fields (e.g., weather, etc.). The agricultural computer system116may also determine the tuning value for that one agricultural field, for each specific disease, based on available observation data (if any). And, the agricultural computer system116combines the model value and the tuning value (if/when determined), for each disease and specific agricultural field, into the likelihood of occurrence and/or severity of the disease in that agricultural field. The agricultural computer system116repeats the above for each of the agricultural fields102and/or diseases consistent with the request from the user103. That said, it should be appreciated that the agricultural computer system116may determine the likelihood of occurrence and/or severity of the one or more diseases for ones of the agricultural fields102with or without tuning (e.g., with or without the tuning value, etc.). For instance, if observation data is not available (or not yet available from the user103or others), the agricultural computer system116may determine the likelihood of occurrence and/or severity of the one or more diseases for ones of the agricultural fields102without tuning. Then, as observation data becomes available, the agricultural computer system116may determine/update the likelihood of occurrence and/or severity of the one or more diseases for ones of the agricultural fields102with tuning (taking into account the available observation data, etc.).

As such, the agricultural computer system116determines the likelihoods of occurrence and/or severity for each of the one or more diseases for each of the one or more agricultural fields102.

The agricultural computer system116then generates, at308, an interface indicative of the likelihoods of occurrence of the disease(s), which includes a map of the agricultural fields102with each color-coded (or otherwise visually distinguished) specific to the likelihood of occurrence of a first disease.

At310, the agricultural computer system116causes the interface to be displayed at the communication device104, generally, in response to the request for the interface. The user103is then able to view the map of likelihoods of occurrence of the first one of the diseases in the agricultural fields102, and manipulate the interface to view the likelihoods of occurrence of a second one of the diseases in the agricultural fields, or view severity instead, etc.

Thereafter, the user103may enter one or more new disease observations, after, for example, inspecting one or more agricultural fields102. In particular, the user103may select an option to enter a disease observation, whereby the user103is permitted to drop a pin (broadly, user input) at a specific agricultural field (or otherwise indicate the disease observation) in the interface displayed to the user103at the communication device104. The user103may indicate the specific disease from a pulldown menu, or otherwise, and any additional data associated with the observation. For example, the user103may drop a pin in Field1234and define the pin as an observation of fusarium.

In turn, the communication device104communicates the new disease observation data, which includes a temporal indicator (e.g., date/time, etc.), the disease type, field identifier (or location), etc., to the agricultural computer system116, which receives the new disease observation data, at312. In the example above, the disease observation data includes an indication of fusarium, Field1234, June 24, etc. It should be appreciated that the user103may enter more than one new disease observation, whereby disease observation data indicative of each disease observation is received by the agricultural computer system116.

The agricultural computer system116returns to step306and determines, again, the likelihood of occurrence and/or severity of each disease in each of the agricultural fields102, as described above (or at least updates the tuning value for each field/disease), further based on the new disease observation data received at312. The agricultural computer system116then generates the interface, at308, as an updated interface, and causes the updated interface to be displayed to the user103, at the communication device104, at310.

While step312is described with reference to the user103, it should be appreciated that the new disease observation data may be entered by another grower associated with the same or different agricultural fields102, or by another system, database, etc.

With reference again toFIG.1, as described, the data server108is communicatively coupled to the agricultural computer system116and is configured to send external data120(e.g., data associated with growing spaces, or data as otherwise described herein, etc.) to agricultural computer system116, via the external data server122and the network(s) herein (e.g., for use with the multiple disease joint model (e.g., training, validation, application, etc.), etc.). The data server108may be owned or operated by the same legal person or entity as the agricultural computer system116, or by a different person or entity, such as a government agency, non-governmental organization (NGO), and/or a private data service provider. Examples of external data120may include location data, weather data, imagery data, soil data, seed data and treatment data as described herein, data from the various growing spaces herein, or statistical data relating to crop yields, among others (or other data as described herein). External data120may include the same type of information as field data. In some embodiments, the external data120may also be provided by data server108owned by the same entity that owns and/or operates the agricultural computer system116. For example, the agricultural computer system116may include a data server focused exclusively on a type of data that might otherwise be obtained from third party sources, such as weather data to trial data to treatment data. In some embodiments, data server108may be incorporated or integrated, in whole or in part, in the agricultural computer system116.

Also, the farm equipment106may have one or more remote sensors fixed thereon (e.g., sensor107, etc.), where the sensor(s) are communicatively coupled, either directly or indirectly, via the farm equipment106to the agricultural computer system116and are configured to send sensor data to agricultural computer system116.

As generally described above, examples of farm equipment106that may be included in the system100include tractors, combines, pickers, sprayers, planters, trucks, fertilizer equipment, aerial vehicles including unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture and/or related to operations described herein. In some embodiments, a single unit of the farm equipment106may comprise a plurality of sensors that are coupled locally in a network on the apparatus/equipment. A controller area network (CAN) is an example of such a network that can be installed in combines, harvesters, sprayers, and cultivators. In connection therewith, then, an application controller associated with the farm equipment106may be communicatively coupled to agricultural computer system116via the network(s) and programmed, or configured, to receive one or more scripts (e.g., from the agricultural computer system116, etc.) that are used to control an operating parameter of the farm equipment106(or another agricultural vehicle or implement). For instance, a CAN bus interface may be used to enable communications from the agricultural computer system116to the farm equipment106(e.g., to a computing device109of the farm equipment106, etc.), for example, such as through the CLIMATE FIELDVIEW DRIVE, available from Climate LLC, Saint Louis, Missouri. Sensor data may include the same type of information as field data. In some embodiments, remote sensors may not be fixed to the farm equipment106but may be remotely located in the field and may communicate with one or more networks of the system100.

As indicated above, the network(s) of the system100are generally illustrated inFIG.1by arrowed lines. In connection therewith, the network(s) broadly represent any combination of one or more data communication networks including local area networks, wide area networks, internetworks or internets, using any of wireline or wireless links, including terrestrial or satellite links. The network(s) may be implemented by any medium or mechanism that provides for the exchange of data between the various elements ofFIG.1. The various elements ofFIG.1may also have direct (wired or wireless) communications links. For instance, the farm equipment106in the system100, data server108, agricultural computer system116, and other elements of the system100may each comprise an interface compatible with the network(s) and programmed, or configured, to use standardized protocols for communication across the networks, such as TCP/IP, Bluetooth, CAN protocol and higher-layer protocols, such as HTTP, TLS, and the like.

That said, the agricultural computer system116is configured, generally, to receive field data from farm equipment106, the communication device104, the external data120from external data server122, and sensor data from one or more remote sensors in the system100. Agricultural computer system116may be further configured to host, use or execute one or more computer programs, other software elements, digitally programmed logic, such as FPGAs or ASICs, or any combination thereof, to perform translation and storage of data values, construction of digital models of one or more crops on one or more fields, generation of recommendations and notifications, and generation and sending of scripts, in the manner described further in other sections of this disclosure.

In an embodiment, agricultural computer system116is programmed with or comprises a communication layer132, a presentation layer134, a data management layer140, a hardware/virtualization layer150, and a model and field data repository layer160. “Layer,” in this context, refers to any combination of electronic digital interface circuits, microcontrollers, firmware, such as drivers, and/or computer programs, or other software elements.

Communication layer132may be configured to perform input/output interfacing functions including sending requests to communication device104, data server108, and remote sensor(s) for field data, external data120, and sensor data respectively. Communication layer132may be configured to send the received data to the model and field data repository layer160to be stored as field data (e.g., in agricultural computer system116, etc.). Presentation layer134may be configured to generate a graphical user interface (GUI) to be displayed on communication device104, via one or more applications, (e.g., such as the interfaces illustrated inFIGS.2A-2D, etc.) (e.g., to interact with the agricultural computer system116, to identify the target field(s), to select inputs, etc.), or other computers that are coupled to the agricultural computer system116through the network(s). The GUI may comprise controls for inputting data to be sent to the agricultural computer system116, generating requests for models and/or recommendations, and/or displaying recommendations, notifications, models, and other field data.

Data management layer140may be configured to manage read operations and write operations involving the repository layer160and other functional elements of the system100, including queries and result sets communicated between the functional elements of the system and the repository layer160. Examples of data management layer140include JDBC, SQL server interface code, and/or HADOOP interface code, among others. The repository layer160may comprise a database. As used herein, the term “database” may refer to either a body of data, a relational database management system (RDBMS), or both. As used herein, a database may comprise any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, distributed databases, and any other structured collection of records or data that is stored in a computer system. Examples of RDBMS's include, but are not limited to, ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. That said, any database may be used that enables the systems and methods described herein.

When field data is not provided directly to the agricultural computer system116via farm equipment (e.g., equipment106, etc.) that interacts with the agricultural computer system116, the user103may be prompted via one or more user interfaces on the communication device104(served by the agricultural computer system116) to input such data to the agricultural computer system116. In an example embodiment, the user103may specify disease observations by accessing a map on the communication device104(served by the agricultural computer system116) and selecting locations for the disease observation, as explained above, that have been graphically shown on the map. The user103may then indicate on the map the specific disease observation.

In an example embodiment, the agricultural computer system116is programmed to generate and cause displaying of one or more interfaces comprising a data manager for data input. After one or more fields (or associated data) have been identified using the methods described above, the data manager may provide one or more graphical user interface widgets which when selected can identify changes to the field, soil, crops, tillage, or nutrient practices, and/or which may provide comparison data related to treatments, yields, etc. identified by the disclosure herein for fields of the growing spaces. The data manager may include a timeline view, a spreadsheet view, a graphical view, and/or one or more editable programs.

In an embodiment, the above described trained disease model and data is stored in the model and field data repository layer160. “Model,” in this context, refers to an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more stored or calculated output values indicative of likelihood of occurrence of one or more diseases that can serve as the basis of computer-implemented output data displays, or machine control, among other things.

With continued reference toFIG.1, in an embodiment, instructions135of the agricultural computer system116may comprise a set of one or more pages of main memory, such as RAM, in the agricultural computer system116into which executable instructions have been loaded and which when executed cause the agricultural computer system116to perform the functions or operations that are described herein. For example, the instructions135may comprise a set of pages in RAM that contain instructions which, when executed, cause determining likelihoods of occurrence of one or more diseases as described herein. The instructions may be in machine executable code in the instruction set of a CPU and may have been compiled based upon source code written in JAVA, C, C++, OBJECTIVE-C, or any other human-readable programming language or environment, alone or in combination with scripts in JAVASCRIPT, other scripting languages and other programming source text. The term “pages” is intended to refer broadly to any region within main memory and the specific terminology used in a system may vary depending on the memory architecture or processor architecture. In another embodiment, the instructions135also may represent one or more files, or projects of source code, that are digitally stored in a mass storage device, such as non-volatile RAM or disk storage, in the agricultural computer system116or a separate repository system, which when compiled or interpreted cause generating executable instructions which when executed cause the agricultural computer system116to perform the functions or operations that are described herein.

Hardware/virtualization layer150comprises one or more central processing units (CPUs), memory controllers, and other devices, components, or elements of a computer system, such as volatile or non-volatile memory, non-volatile storage, such as disk, and I/O devices or interfaces as illustrated and described, for example, in connection withFIGS.2A-2D. The hardware/virtualization layer150also may comprise programmed instructions that are configured to support visualization, virtualization, containerization, or other technologies.

For purposes of illustrating a clear example,FIG.1shows a limited number of instances of certain functional elements. However, in other embodiments, there may be any number of such elements. For example, embodiments may use thousands or millions of different mobile computing devices associated with different users/growers. Further, the agricultural computer system116and/or data server108may be implemented using two or more processors, cores, clusters, or instances of physical machines or virtual machines, configured in a discrete location or co-located with other elements in a datacenter, shared computing facility or cloud computing facility.

In an embodiment, the implementation of the functions described herein using one or more computer programs, or other software elements that are loaded into and executed using one or more general-purpose computers, will cause the general-purpose computers to be configured as a particular machine or as a computer that is specially adapted to perform the functions described herein. Further, each of the flow diagrams that are described further herein may serve, alone or in combination with the descriptions of processes and functions in prose herein, as algorithms, plans or directions that may be used to program a computer or logic to implement the functions that are described. In other words, all the prose text herein, and all the drawing figures, together are intended to provide disclosure of algorithms, plans or directions that are sufficient to permit a skilled person to program a computer to perform the functions that are described herein, in combination with the skill and knowledge of such a person given the level of skill that is appropriate for disclosures of this type.

In an embodiment, the user103interacts with the agricultural computer system116using the communication device104configured with an operating system and one or more applications or apps (e.g., application105, etc.). The communication device104also may interoperate with the agricultural computer system116independently and automatically under program control or logical control and direct user interaction is not always required. The communication device104broadly represents one or more of a smart phone, PDA, tablet computing device, laptop computer, desktop computer, workstation, or any other computing device capable of transmitting and receiving information and performing the functions described herein. The communication device104may communicate via a network using a mobile application stored on the communication device104, and in some embodiments, the communication device104may be coupled using a cable or connector to one or more sensors and/or other apparatus in the system100. The particular user103may own, operate or possess and use, in connection with system100, more than one communication device at a time.

The application associated with the communication device104may provide client-side functionality, via the network to one or more mobile computing devices. Again, the communication device104may access the application, via a web browser or a local client application or app. The communication device104may transmit data to, and receive data from, one or more front-end servers, using web-based protocols, or formats, such as HTTP, XML and/or JSON, or app-specific protocols. In an example embodiment, the data may take the form of requests (e.g., filter criteria, selections, etc.) and user information input, such as data (e.g., disease observation, etc.), into the communication device104.

A commercial example of the application described above is CLIMATE FIELDVIEW, commercially available from Climate LLC, Saint Louis, Missouri. The CLIMATE FIELDVIEW application and associated tools, or other applications, may be modified, extended, or adapted to include features, functions, and programming that have not been disclosed earlier than the filing date of this disclosure. In one embodiment, the application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare. The combinations and comparisons may be performed in real time and are based upon scientific models that provide potential scenarios to permit the grower to make better, more informed decisions.

FIGS.4A-4Billustrate two views of an example logical organization of sets of instructions in main memory when an example mobile application is loaded for execution. Each named element represents a region of one or more pages of RAM, or other main memory, or one or more blocks of disk storage, or other non-volatile storage, and the programmed instructions within those regions. In one embodiment, inFIG.4A, a mobile computer application300comprises account, fields, data ingestion, sharing instructions302, overview and alert instructions304, digital map book instructions306, seeds and planting instructions308, treatment decision instructions310, weather instructions312, crop disease type instructions314, and performance instructions316.

In one embodiment, a mobile computer application300comprises account, fields, data ingestion, sharing instructions302which are programmed to receive, translate, and ingest field data from third party systems via manual upload or APIs. Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others. Data formats may include shape files, native data formats of third parties, and/or farm management information system (FMIS) exports, among others. Receiving data may occur via manual upload, e-mail with attachment, external APIs that push data to the mobile application, or instructions that call APIs of external systems to pull data into the mobile application. In one embodiment, mobile computer application300comprises a data inbox. In response to receiving a selection of the data inbox, the mobile computer application300may display a graphical user interface for manually uploading data files and importing uploaded files to a data manager.

In one embodiment, digital map book instructions306comprise field map data layers stored in device memory and are programmed with data visualization tools and geospatial field notes. This provides growers with convenient information close at hand for reference, logging and visual insights into field performance and other options provided herein. In one embodiment, overview and alert instructions304are programmed to provide an operation-wide view of what is important to the grower, and timely recommendations to take action or focus on particular issues. This permits the grower to focus time on what needs attention, to save time and preserve yield throughout the season. In one embodiment, seeds and planting instructions308are programmed to provide tools for seed selection, hybrid placement, and script creation, including variable rate (VR) script creation, based upon scientific models and empirical data. This enables growers to maximize yield, or return on investment, through optimized seed purchase, placement and population.

In one embodiment, script generation instructions305are programmed to provide an interface for generating scripts, including variable rate (VR) fertility scripts, disease treatment scripts, etc. The interface enables growers to create scripts for field implements, such as treatment decisions, planting, and irrigation. For example, a planting script interface may comprise tools for identifying a type of seed for planting. Upon receiving a selection of the seed type, mobile computer application300may display one or more fields broken into management zones, such as the field map data layers created as part of digital map book instructions306. In one embodiment, the management zones comprise soil zones along with a panel identifying each soil zone and a soil name, texture, drainage for each zone, or other field data. Mobile computer application300may also display tools for editing or creating such as graphical tools for drawing management zones, such as soil zones, over a map of one or more fields. Planting procedures may be applied to all management zones or different planting procedures may be applied to different subsets of management zones. When a script is created, mobile computer application300may make the script available for download in a format readable by an application controller, such as an archived or compressed format. Additionally, and/or alternatively, a script may be sent directly to a cab computer (e.g., associated with farm equipment106, etc.) from mobile computer application300and/or uploaded to one or more data servers and stored for further use.

In one embodiment, treatment decision instructions310are programmed to provide tools to inform decisions by visualizing or instruction about the application of one or more candidate treatments to crops in a particular field. This enables growers to potentially enhance yield or return on investment through treatment application during the season.

Example programmed functions include displaying images to enable tuning application(s) of treatment across multiple zones; output of scripts to drive machinery; tools for mass data entry and adjustment; and/or maps for data visualization, among others. Treatment decision instructions310also may be programmed to generate and cause displaying a treatment graph, indicative of the application of the treatment to one or more target fields, but not others based on the functions explained herein. In one embodiment, the treatment graph may include one or more user input features, such as dials or slider bars, to dynamically change the candidate treatment programs so that the grower may alter the parameters of the treatment decision. Treatment instructions310also may be programmed to generate and cause displaying a treatment decision or indications.

In one embodiment, weather instructions312are programmed to provide field-specific recent weather data and forecasted weather information. This enables growers to save time and have an efficient integrated display with respect to daily operational decisions.

In one embodiment, crop disease type instructions314are programmed to provide timely remote sensing images highlighting in-season crop variation, multiple crop disease types and potential concerns. Example programmed functions include cloud checking, to identify possible clouds or cloud shadows; determining treatment indices based on field images; graphical visualization of scouting layers, including, for example, those related to field health, and viewing and/or sharing of scouting notes; recording observations of different crop disease type presence and/or severity; and/or downloading satellite images from multiple sources and prioritizing the images for the grower, among others.

In one embodiment, performance instructions316are programmed to provide reports, analysis, and insight tools using on-farm data for evaluation, insights and decisions. This enables the grower to seek improved outcomes for the next year through fact-based conclusions about why return on investment was at prior levels, and insight into yield-limiting factors. The performance instructions316may be programmed to communicate via the network(s) to back-end analytics programs executed at agricultural computer system116and/or data server108and configured to analyze metrics, such as yield, yield differential, hybrid, population, SSURGO zone, soil test properties, or elevation, among others. Programmed reports and analysis may include yield variability analysis, treatment effect estimation, benchmarking of yield and other metrics against other growers based on anonymized data collected from many growers, or data for seeds and planting, among others.

Applications having instructions configured in this way may be implemented for different computing device platforms while retaining the same general user interface appearance. For example, the mobile application may be programmed for execution on tablets, smartphones, or server computers that are accessed using browsers at client computers. Further, the mobile application300as configured for tablet computers, or smartphones, may provide a full app experience, or a cab app experience, that is suitable for the display and processing capabilities of a cab computer (e.g., associated with farm equipment10, etc.). For example, referring now toFIG.4B, in one embodiment a cab computer application320(e.g., as accessible in one of farm equipment106, etc.) may comprise maps-cab instructions322, remote view instructions324, data collect and transfer instructions326, machine alerts instructions328, script transfer instructions330, and scouting-cab instructions332. The code base for the instructions ofFIG.4Bmay be the same as forFIG.4Aand executables implementing the code may be programmed to detect the type of platform on which they are executing and to expose, through a graphical user interface, only those functions that are appropriate to a cab platform or full platform. This approach enables the system to recognize the distinctly different user experience that is appropriate for an in-cab environment and the different technology environment of the cab. The maps-cab instructions322may be programmed to provide map views of fields, farms or regions that are useful in directing machine operation. The remote view instructions324may be programmed to turn on, manage, and provide views of machine activity in real-time or near real-time to other computing devices connected to the agricultural computer system116via wireless networks, wired connectors or adapters, and the like. The data collect and transfer instructions326may be programmed to turn on, manage, and provide transfer of data collected at sensors and controllers to the agricultural computer system116via wireless networks, wired connectors or adapters, and the like (e.g., via network(s) in the system100, etc.). The machine alerts instructions328may be programmed to detect issues with operations of the machines or tools that are associated with the cab and generate operator alerts. The script transfer instructions330may be configured to transfer in scripts of instructions that are configured to direct machine operations or the collection of data. The scouting-cab instructions332may be programmed to display location-based alerts and information received from the agricultural computer system116based on the location of the communication device104, farm equipment106, or sensors in the field (of the growing spaces) and ingest, manage, and provide transfer of location-based scouting observations to the agricultural computer system116based on the location of the farm equipment106, or sensors in the field.

In an embodiment, data server108stores external data120from the data server122, including soil data representing soil composition for the one or more fields and weather data representing temperature and precipitation on the one or more fields (and/or other data). The weather data may include past and present weather data as well as forecasts for future weather data. In an embodiment, data server108comprises a plurality of servers hosted by different entities. For example, a first server may contain soil composition data while a second server may include weather data. Additionally, soil composition data may be stored in multiple servers. For example, one server may store data representing percentage of sand, silt, and clay in the soil while a second server may store data representing percentage of organic matter (OM) in the soil. Further, in some embodiments, the data server108, again, may include data associated with the growing spaces with regard to available seeds for use in comparisons, etc.

In an embodiment, remote sensors in the system100may comprise one or more sensors that are programmed, or configured, to produce one or more observations related to growing spaces, trials therein, etc. Remote sensors may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields (e.g., associated with one or more of the growing spaces, etc.). In an embodiment, farm equipment106may include an application controller programmed, or configured, to receive instructions from agricultural computer system116. The application controller may also be programmed, or configured, to control an operating parameter of the farm equipment106. Other embodiments may use any combination of sensors and controllers, of which the following are merely selected examples.

The system100may obtain or ingest data under grower control, on a mass basis from a large number of growers who have contributed trial data or other data to a shared database system. This form of obtaining data may be termed “manual data ingest” as one or more user-controlled computer operations are requested, or triggered, to obtain data for use by the agricultural computer system116. As an example, the CLIMATE FIELDVIEW application, commercially available from Climate LLC, Saint Louis, Missouri, may be operated to export data to agricultural computer system116for storing in the field data repository160.

For example, seed monitor systems can both control planter apparatus components and obtain planting data, including signals from seed sensors via a signal harness that comprises a CAN backbone and point-to-point connections for registration and/or diagnostics. Seed monitor systems can be programmed, or configured, to display seed spacing, population and other information to the user via a cab computer of the apparatus, or other devices within the system100.

Likewise, yield monitor systems may contain yield sensors for harvester apparatus that send yield measurement data to a cab computer of the apparatus, or other devices within the system100. Yield monitor systems may utilize one or more remote sensors to obtain grain moisture measurements in a combine, or other harvester, and transmit these measurements to the user103via the cab computer, or other devices within the system100.

In an embodiment, examples of sensors that may be used with any moving vehicle, or apparatus of the type described elsewhere herein, include kinematic sensors and position sensors. Kinematic sensors may comprise any of speed sensors, such as radar or wheel speed sensors, accelerometers, or gyros. Position sensors may comprise GPS receivers or transceivers, or WiFi-based position or mapping apps that are programmed to determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors that may be used with tractors, or other moving vehicles, include engine speed sensors, fuel consumption sensors, area counters or distance counters that interact with GPS or radar signals, PTO (power take-off) speed sensors, tractor hydraulics sensors configured to detect hydraulics parameters, such as pressure or flow, and/or and hydraulic pump speed, wheel speed sensors or wheel slippage sensors. In an embodiment, examples of controllers that may be used with tractors include hydraulic directional controllers, pressure controllers, and/or flow controllers; hydraulic pump speed controllers; speed controllers or governors; hitch position controllers; or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors that may be used with seed planting equipment, such as planters, drills, or air seeders, include seed sensors, which may be optical, electromagnetic, or impact sensors; downforce sensors, such as load pins, load cells, pressure sensors; soil property sensors, such as reflectivity sensors, moisture sensors, electrical conductivity sensors, optical residue sensors, or temperature sensors; component operating criteria sensors, such as planting depth sensors, downforce cylinder pressure sensors, seed disc speed sensors, seed drive motor encoders, seed conveyor system speed sensors, or vacuum level sensors; or pesticide application sensors, such as optical or other electromagnetic sensors, or impact sensors. In an embodiment, examples of controllers that may be used with such seed planting equipment include: toolbar fold controllers, such as controllers for valves associated with hydraulic cylinders; downforce controllers, such as controllers for valves associated with pneumatic cylinders, airbags, or hydraulic cylinders, and programmed for applying downforce to individual row units or an entire planter frame; planting depth controllers, such as linear actuators; metering controllers, such as electric seed meter drive motors, hydraulic seed meter drive motors, or swath control clutches; hybrid selection controllers, such as seed meter drive motors, or other actuators programmed for selectively allowing or preventing seed or an air-seed mixture from delivering seed to or from seed meters or central bulk hoppers; metering controllers, such as electric seed meter drive motors, or hydraulic seed meter drive motors; seed conveyor system controllers, such as controllers for a belt seed delivery conveyor motor; marker controllers, such as a controller for a pneumatic or hydraulic actuator; or pesticide application rate controllers, such as metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors that may be used with tillage equipment include position sensors for tools, such as shanks or discs; tool position sensors for such tools that are configured to detect depth, gang angle, or lateral spacing; downforce sensors; or draft force sensors. In an embodiment, examples of controllers that may be used with tillage equipment include downforce controllers or tool position controllers, such as controllers configured to control tool depth, gang angle, or lateral spacing.

In an embodiment, examples of sensors that may be used in relation to an apparatus for applying fertilizer, insecticide, fungicide, herbicide, and the like, such as on-planter starter fertilizer systems, subsoil fertilizer applicators, or fertilizer sprayers, include: fluid system criteria sensors, such as flow sensors or pressure sensors; sensors indicating which spray head valves or fluid line valves are open; sensors associated with tanks, such as fill level sensors; sectional or system-wide supply line sensors, or row-specific supply line sensors; or kinematic sensors, such as accelerometers disposed on sprayer booms. In an embodiment, examples of controllers that may be used with such apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM and the like; or position actuators, such as for boom height, subsoiler depth, or boom position.

In an embodiment, examples of sensors that may be used with harvesters include yield monitors, such as impact plate strain gauges or position sensors, capacitive flow sensors, load sensors, weight sensors, or torque sensors associated with elevators or augers, or optical or other electromagnetic grain height sensors; grain moisture sensors, such as capacitive sensors; grain loss sensors, including impact, optical, or capacitive sensors; header operating criteria sensors, such as header height, header type, deck plate gap, feeder speed, and reel speed sensors; separator operating criteria sensors, such as concave clearance, rotor speed, shoe clearance, or chaffer clearance sensors; auger sensors for position, operation, or speed; or engine speed sensors. In an embodiment, examples of controllers that may be used with harvesters include header operating criteria controllers for elements, such as header height, header type, deck plate gap, feeder speed, or reel speed; separator operating criteria controllers for features such as concave clearance, rotor speed, shoe clearance, or chaffer clearance; or controllers for auger position, operation, or speed.

In an embodiment, examples of sensors that may be used with grain carts include weight sensors, or sensors for auger position, operation, or speed. In an embodiment, examples of controllers that may be used with grain carts include controllers for auger position, operation, or speed.

In an embodiment, examples of sensors and controllers may be installed in unmanned aerial vehicle (UAV) apparatus or “drones.” Such sensors may include cameras with detectors effective for any range of the electromagnetic spectrum including visible light, infrared, ultraviolet, near-infrared (NIR), and the like; accelerometers; altimeters; temperature sensors; humidity sensors; pitot tube sensors or other airspeed or wind velocity sensors; battery life sensors; or radar emitters and reflected radar energy detection apparatus; other electromagnetic radiation emitters and reflected electromagnetic radiation detection apparatus. Such controllers may include guidance or motor control apparatus, control surface controllers, camera controllers, or controllers programmed to turn on, operate, obtain data from, manage and configure any of the foregoing sensors.

In an embodiment, sensors and controllers may be affixed to a soil sampling and measurement apparatus that is configured, or programmed, to sample soil and perform soil chemistry tests, soil moisture tests, and other tests pertaining to soil.

In an embodiment, sensors and controllers may comprise weather devices for monitoring weather conditions of fields.

In an embodiment, the agricultural computer system116is programmed, or configured, to create an agronomic model. In this context, an agronomic model is a data structure in memory of the agricultural computer system116that comprises field data, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties which describes either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both. Additionally, an agronomic model may comprise recommendations based on agronomic factors, such as crop recommendations, irrigation recommendations, planting recommendations, fertilizer recommendations, fungicide recommendations, pesticide recommendations, harvesting recommendations and other crop management recommendations. The agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield. The agronomic yield of a crop is an estimate of quantity of the crop that is produced, or in some examples, the revenue or profit obtained from the produced crop.

In an embodiment, the agricultural computer system116may use a preconfigured agronomic model to calculate agronomic properties related to currently received location and crop information for one or more fields. The preconfigured agronomic model is based upon previously processed field data, including but not limited to, identification data, harvest data, fertilizer data, and weather data. The preconfigured agronomic model may have been cross validated to ensure accuracy of the model. Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge or sensor providing weather data at the same or nearby location, or a comparison of treatment recommendations to validation data.

Computer system500further includes a read only memory (ROM)508, or other static storage device coupled to bus502, for storing static information and instructions for processor504. A storage device510, such as a magnetic disk, optical disk, or solid-state drive, is provided and coupled to bus502for storing information and instructions.

As will be appreciated based on the foregoing specification, the above-described embodiments of the disclosure may be implemented using computer programming or engineering techniques including computer software, firmware, hardware or any combination or subset thereof, wherein the technical effect may be achieved by performing at least one of the following operations: (a) receiving a user input from a user at a communication device associated with an interface indicative of likelihood of a first disease in a plurality of agricultural fields; (b) accessing data associated with the agricultural fields and a disease model, the data associated with the agricultural fields including disease observation data indicative of an observation of the first disease in a first field of the agricultural fields, the disease observation data including a temporal indicator, an identifier of the first disease, and an identifier of the first field; (c) determining the likelihood of occurrence of the first disease for ones of the plurality of agricultural fields, based on: (i) a model value based on the disease model; and (ii) a tuning value, using a spatially misaligned regression; wherein the tuning value is based on at least: a distance between a location of the first field and locations of the ones of the plurality of agricultural fields; (d) generating an interface indicative of the likelihood of occurrence of the first disease in the ones of the plurality of agricultural fields; (e) causing the interface to be displayed at the communication device associated with the user; and/or (f) treating the agricultural field with a treatment in response to the interface indicating a chance of occurrence of the first disease above a defined threshold.

When a feature is referred to as being “on,” “engaged to,” “connected to,” “coupled to,” “associated with,” “in communication with,” or “included with” another element or layer, it may be directly on, engaged, connected or coupled to, or associated or in communication or included with the other feature, or intervening features may be present. As used herein, the term “and/or” and the phrase “at least one of” includes any and all combinations of one or more of the associated listed items.