COMPUTER-IMPLEMENTED METHOD FOR MONITORING OPERATIONS IN AGRICULTURAL REGIONS

Abstract

This document describes systems and methods for analyzing agricultural data. In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural data, including yield and field data, and at least one processing unit coupled to the database. This at least one processing unit is configured to execute instructions for monitoring field operations, storing agricultural data, automatically determining whether at least one correlation between different variables or parameters of the agricultural data exceeds a threshold, and performing analysis of the agricultural data to identify a category of human-caused or other problems that may have caused the correlation when at least one correlation occurs between different variables or parameters of the agricultural data.

Description

The embodiments of the present invention relate to systems and methods for the analysis of agricultural data. BACKGROUND Seeders are used to plant crop seeds (e.g., corn, soybeans) in a field. Some seeders include a display monitor inside a cab to show a coverage map that indicates which areas of the field have been planted. The seeder's coverage map is generated based on the planting data collected by the seeder. Row width control prevents the seeder from planting in an area that has already been planted by the same seeder. A combine harvester is a machine that harvests crops. A combine harvester coverage map shows regions of the field that have been harvested by that combine harvester. A coverage map allows the combine harvester operator to know which areas of the field have already been harvested by the same combine harvester. The operator may have difficulty with 238326 1647602 of 57 operate the machine, operate the instruments, and analyze the data and maps provided by the display monitor in a timely manner. BRIEF DESCRIPTION OF THE INVENTION In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural data, including field and yield data, and at least one processing unit coupled to the database. This processing unit is configured to execute instructions for monitoring field operations, storing agricultural data, automatically determining whether at least one correlation between different variables or parameters of the agricultural data exceeds a threshold, and performing analysis of the agricultural data to identify a category of human-caused or other problems that may have caused the correlation when at least one correlation occurs between different variables or parameters of the agricultural data. In one example, at least one processing unit is further configured to execute instructions to check for a potential irrigation problem for a particular field by determining whether the crop yield for the field has a geometric pattern that includes a circular pattern or a linear pattern, and to determine if the irrigation can be identified as corresponding to a geometric pattern if a geometric pattern is determined. In another example, at least one processing unit is further configured to execute instructions to send a communication to a user device when irrigation is detected to match the geometric pattern. In another example, at least one processing unit is further configured to execute instructions to check for a potential application pass problem for a particular field by determining if the crop yield for the field has a geometric pattern and to determine if an application pass can be identified as matching the geometric pattern. In another example, at least one processing unit is also configured to execute instructions to send a communication to a device 238326 1647602 of 57 from a user when it is identified that the application pass corresponds to the geometric pattern. In one embodiment, a method for analyzing agricultural data includes monitoring, with a system, agricultural data that includes field and yield data. The method further includes automatically determining, with the system, whether at least one correlation between different variables or parameters of the agricultural data exceeds a threshold, and performing, with the system, an analysis of the agricultural data to identify a category of human-caused or other problems that may have caused the correlation when at least one correlation occurs between different variables or parameters of the agricultural data. In one example, the method also includes verifying, with a system, a potential irrigation problem for a particular field by determining whether the crop yield for the field has a geometric pattern that includes a circular pattern or a linear pattern and determining if the irrigation can be identified as corresponding to the geometric pattern if a geometric pattern is determined. In another example, the method also includes sending a communication to a user's device when the irrigation is identified as matching the geometric pattern. In another example, the method also includes checking for a potential problem in the application pass for a particular field by determining if the crop yield for the field has a geometric pattern and determining if an application pass can be identified as corresponding to the geometric pattern. In another example, the method also includes sending a communication to a user's device when the application pass is identified as matching the geometric pattern. In another embodiment, a computer system for agricultural data analysis includes a database for storing agricultural data, including field and yield data, and at least one processing unit coupled to the database. This at least one processing unit is configured to execute instructions to create at least one trial that potentially establishes one or more correlations between different parameters or variables of the agricultural data in response to receiving communication from a device and assigns yield data based on different regions or strips. 238326 1,647,602 of 57 created with at least one trial. At least one parameter or variable is varied in different regions or strips of a field to cause a correlation. In one example, at least one processing unit is configured to run instructions to analyze at least one trial created to determine if the at least one trial causes at least one correlation between yield data and a variable or parameter of agricultural data for different regions or strips of a field. In another example, at least one processing unit is configured to execute instructions to receive communication from a device in response to at least one user input that varies a parameter or variable of agricultural data in different regions or strips of the field to create at least one trial that causes a correlation between yield data and the parameter or variable. In one example, at least one processing unit is configured to execute instructions to receive communication from a device in response to at least one user input received in real time during a farm operation that varies a parameter or variable of the farm data in different regions or strips of the field to create at least one trial that causes a correlation between yield data and the parameter or variable. In another example, at least one processing unit is configured to execute instructions to generate and send data to a device to be displayed to the user by at least one test. The data exhibits at least one correlation for different regions or strips of the field of at least one test, or an absence of at least one correlation. In another embodiment, a method of analyzing agricultural data includes receiving, with a device, one or more user inputs after conducting a farming operation or during the farming operation to create at least one trial that potentially causes one or more correlations between different parameters or variables of agricultural data, creating, with the device, at least one trial to potentially cause one or more correlations between different variables or parameters of agricultural data for field operations in response to the one or more user inputs, and assigning yield data based on different regions created with the at least one trial. 238326 1647602 of 57 In another example, the method also includes analyzing at least one trial created to determine if the trial causes at least one correlation between performance data and a variable or parameter of the field data for different regions of the field. In another example, the method also includes generating and displaying data to the user for at least one trial. In another example, the device displays data that includes at least one correlation for different regions of the field from at least one trial or shows an absence of at least one correlation. In another example, the device displays the data and includes a Return On Investment (ROI) tool that allows the user to determine an optimal region or optimal set of conditions to maximize ROI. BRIEF DESCRIPTION OF THE FIGURES The present invention is illustrated by way of example and not in a limiting manner in the accompanying figures, in which: FIG. 1 illustrates an exemplary computing system that is configured to perform the functions described herein, presented in a field environment with other devices with which the system operationally interacts. FIG. 2 illustrates two views of an example of logical organization of instruction sets in main memory when an exemplary mobile application is loaded for execution. FIG. 3 illustrates a programmed process by which the agricultural intelligence computing system generates one or more preconfigured agronomic models using agronomic data supplied from one or more data sources. FIG. 4 is a block diagram illustrating a computer system 400 on which a form of embodiment of the invention can be implemented. FIG. 5 presents an exemplary way of implementing a timeline for data entry. 238326 1647602 of 57 FIG.6 presents an exemplary implementation in the form of a spreadsheet for data entry. FIG. 7 illustrates a flowchart of one embodiment for a 700 method of automatic identification of one or more correlations for field operations. FIG. 8 illustrates a flowchart of one embodiment for a method 800 for creating trials to cause one or more correlations between different variables or parameters of agricultural data; FIG. 9 illustrates a flowchart of one embodiment for a 900 method of creating trials to cause one or more correlations between different variables or parameters of agricultural data; FIG. 10 illustrates an exemplary comparison center 1000 interface according to one embodiment; FIG. 11 illustrates an exemplary interface of a comparison center 1100 according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION This section describes systems and methods for analyzing agricultural data. In one embodiment, a method for analyzing agricultural data includes monitoring, with a system, agricultural data that includes field and yield data (e.g., weather data, harvest data, planting data, fertilizer data, pesticide data, irrigation data, information on farming practices, cost input information, and information on commodity prices, etc.). The method further includes automatically determining, with the system, whether at least one correlation between different variables or parameters of the agricultural data exceeds a threshold, and performing, with the system, an analysis of the agricultural data to identify a category of human-caused or other problems that may have caused the correlation when at least one correlation occurs between different variables or parameters of the agricultural data. The system can then send a communication (e.g., email, text message, map, etc.) to a user's machine or device. This communication indicates that at least one correlation exceeds a certain threshold. The system can also send a fault comparison center when, for example, 238326 1647602 out of 57 correlations exceed a threshold. The system can also send a recommendation to decide on an action in response to at least one correlation that exceeds a threshold. The user can then make better decisions for farms (e.g., planting decisions, hybrid type selection, planting date, nutrient application, etc.). The following description sets forth numerous details. However, it will be evident to a person skilled in the art that embodiments of the present invention can be practiced without such specific details. In some instances, well-known structures such as block diagrams are shown, rather than detailed representations, for the sake of clarity. Figure 1 illustrates an exemplary computing system configured to perform the functions described herein, shown in a field environment with another device with which the system can operationally interact. In one embodiment, a user 102 owns, operates, or possesses a field management computing device 104, located in or associated with a field location, such as a field intended for agricultural activities or a location for managing one or more agricultural fields. The field management computing device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computing system 130 via one or more networks 109. Examples of data in field 106 include (a) identification data (e.g., hectares, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and other suitable data that can be used to identify farmland, such as a Common Land Unit (CLU), lot and block number, parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, road number, field number, section, township, and / or district), (b) crop data (e.g., crop type, crop variety, crop rotation, whether the crop is organic, harvest date, Actual Production History (APH), expected production, production, commodity information (e.g., crop price,crop income), grain moisture, tillage practices and information from the previous growing season), (c) soil data (e.g., type, 238326 1647602 of 57 composition, pH, organic matter (OM), cation exchange capacity (CEC)), d) planting data (e.g. sowing date, seed types, relative maturity (RM) of the seed(s) planted, seed population, information on input costs (e.g., cost of seed)), and ownership indices (e.g., seed-to-soil ratio parameter), etc.) for the fields being monitored), (e) fertilizer data (e.g., nutrient type (nitrogen, phosphorus, potassium), application type, application date, quantity, origin, method, nutrient costs), (f) pesticide data (e.g. pesticides, herbicides, fungicides, other substances or mixtures of substances intended for use as plant regulators, defoliants or desiccants, application date, quantity, origin, method), (g) irrigation data (e.g., application date, quantity, origin, method),(h) meteorological data (e.g. precipitation, precipitation rate, forecast rainfall, regional water runoff rate, temperature, wind, forecast, pressure, visibility, cloud cover, heat index, dew point, humidity, snow depth, air quality, sunrise, sunset), (i) image data (e.g., images and light spectrum information from a sensor device, camera, computer, smartphone, tablet, unmanned aerial vehicle, aircraft, or agricultural satellite), (j) reconnaissance survey (photos, videos, free-format notes, voice recordings, voice transcripts, weather conditions (temperature, precipitation (current and historical), soil moisture, crop growth stage, wind speed, relative humidity, dew point, black layer)), and (k) soil, seed, crop phenology, pest and disease reports,and prediction sources and databases. A data server 108 is communicatively coupled with the agricultural intelligence computing system 130 and is programmed or configured to send external data 110 to the agricultural intelligence computing system 130 via network(s) 109. The external data server 108 may be owned or operated by the same person or entity as the agricultural intelligence computing system 130, or by a different person or entity, such as a government agency, a non-governmental organization (NGO), and / or a private data service provider. Examples of external data include, but are not limited to, weather data, imagery data, soil data, field conditions, input cost information, commodity information, or statistical data relating to, 238326 1647602 of 57 crop yield. External data 110 may consist of the same type of information as field data 106. In some embodiments, external data 110 is supplied by an external data server 108 owned by the same entity that owns and / or operates the agricultural intelligence computing system 130. For example, the agricultural intelligence computing system 130 may include a data server focused exclusively on a type of data that might otherwise be obtained from third-party sources, such as weather / climate data. In some embodiments, in fact, an external data server 108 may be incorporated within the system 130. An agricultural apparatus 111 may have one or more remote sensors 112 fixed to it, sensors that are communicatively coupled, directly or indirectly, through an agricultural apparatus 111 with the agricultural intelligence computing system 130 and are programmed or configured to send sensor data to the agricultural intelligence computing system 130. Examples of agricultural apparatus 111 include tractors, mower-threshers, harvesters, seeders, trucks, fertilizer equipment, unmanned aerial vehicles, as well as any other element of physical machinery or hardware, typically mobile machinery that can be used in tasks related to agriculture.In some embodiments, a single unit of the apparatus 111 may comprise a plurality of sensors 112 that are locally coupled to a network within the apparatus; the Controller Area Network (CAN) is an example of such a network that can be installed in combine harvesters or combines. The application controller 114 is communicatively coupled to the agricultural intelligence computing system 130 via the network(s) 109 and is programmed or configured to receive one or more sets of commands or scripts to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computing system 130.For example, a controller zone network (CAN) bus interface can be used to enable communication from the agricultural intelligence computing system 130 to the agricultural appliance 111, for example, as used by CLIMATE FIELDVIEW DRIVE. 238326 1647602 of 57 marketed by “The Climate Corporation” of San Francisco, California. The sensor data may consist of the same type of information as the field data 106. In some embodiments, the remote sensors 112 may not be fixed to an agricultural apparatus 111 but may be located remotely in the field and may communicate with the network 109. The apparatus 111 may comprise a cockpit computer 115 programmed with a cockpit application, which may comprise a version or variant of the mobile application for the apparatus 104 described in more detail elsewhere herein. In one embodiment, the cockpit computer 115 comprises a compact computer, often the size of a tablet or smartphone, with a graphic display screen, such as a color screen, mounted inside the operator's cab of the apparatus 111. The cockpit computer 115 may implement some or all of the operations and functions described in more detail herein for the handheld computer 104. The network(s) 109 broadly represent any combination of one or more data communication networks, including local area networks, wide area networks, interconnected networks, or intranets, using wired or wireless links, including terrestrial or satellite links. The network(s) may be implemented by any means or mechanism that allows data exchange between the different elements of FIG. 1. The various elements of FIG. 1 may also have direct communication links (wired or wireless). The sensor(s) 112, the controller 114, the external data server 108, and other system elements each comprise an interface compatible with the network(s) 109 and are programmed or configured to use standardized protocols for communication across networks, such as TCP / IP, Bluetooth, CAN, and upper-layer protocols such as HTTP, TLS, and the like. The agricultural intelligence computing system 130 is programmed or configured to 238326 1647602 of 57 receive agricultural data including field data 106 from the field management computing device 104, external data 110 from external data server 108, and sensed data from a remote sensor 112. The agricultural intelligence computing system 130 may be further configured to host, use, or run one or more computer programs, other software elements, digitally programmed logic such as FPGAs and ASICs, or any combination thereof to perform the translation and storage of data values, the construction of digital models of one or more crops in one or more fields, the generation of recommendations and notifications, and the generation and submission of command sets to the application controller 114, in the manner described in more detail in other sections of this disclosure. In one embodiment, the agricultural intelligence computing system 130 is programmed or comprises a communication layer 132, instructions 136, a presentation layer 134, a data management layer 140, a hardware / virtualization layer 150, and a repository of model and field data 160. Layer, in this context, refers to any combination of digital interface electronic circuits, microcontrollers, firmware such as drivers and / or computer programs, or other software elements. The communication layer 132 can be programmed or configured to perform input / output interface functions, including sending requests to the field management computing device 104, the external data server 108, and the remote sensor 112 for field data, external data, and sensor data, respectively. The communication layer 132 can also be programmed or configured to send the received data to the model and field data repository 160 for storage as field data 106. The presentation layer 134 can be programmed or configured to generate a graphical user interface (GUI) to be displayed on the field management computing device 104, the computer of 238326 1647602 of 57 booth 115 or other computers that couple with system 130 through network 109. The GUI may comprise controls for entering data to be sent to the agricultural intelligence computing system 130, generating requests for models and / or recommendations, and / or for displaying recommendations, notifications, models, and other field data. The data management layer 140 can be programmed or configured to manage read and write operations involving the repository 160 and other functional elements of the system, including queries and result sets communicated between the functional elements of the system and the repository. Examples of the data management layer 140 include JDBC, SQL Server interface code, and / or HADOOP interface code. The repository 160 can comprise a database. As used herein, the term database can refer to a dataset, a relational database management system (RDBMS), or both.In this document, a database may encompass any collection of data, including hierarchical databases, relational databases, flat-file databases, object-relational databases, object-oriented databases, and any other structured collection of records or data stored on a computer system. Examples of relational database management systems (RDBMS) include, but are not limited to, Oracle®, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase, and PostgreSQL. However, any database that enables the systems and methods described herein may be used. When field 106 data is not supplied directly to the agricultural intelligence computing system through one or more agricultural machines or agricultural machine devices that interact with the agricultural intelligence computing system, the user may be required, through one or more user interfaces on the user device (served by the agricultural intelligence computing system), to enter such information. 238326 1647602 of 57 Example of one embodiment, the user can specify identification data by accessing a map on the user's device (served by the agricultural intelligence computing system) and selecting specific CLUs that are graphically displayed on the map. In an alternative embodiment, the user can specify identification data by accessing a map on the user's device (served by the agricultural intelligence computing system) and drawing the field boundaries on the map. Such selection of CLUs or map drawings represent geographic identifiers. In alternative embodiments, the user can specify identification data by accessing field identification data (provided as form files or in a similar format) from the U.S. Department of Agriculture's Farm Services Agency.or other source through the user device and provide such field identification data to the agricultural intelligence computing system. In one example of the embodiment, the agricultural intelligence computing system 130 is programmed to generate and display a graphical user interface comprising a data manager for data input. After one or more fields have been identified using the methods described above, the data manager can provide one or more graphical user interface programs that, when selected, can identify changes in the field, soil, crops, tillage, and nutrients. The data manager can include a timeline view, a spreadsheet view, and / or one or more editable programs. Figure 5 illustrates an example of one way to implement a 501 timeline view for data entry. Using the screen shown in Figure 5, a user computer can enter a selection from a particular field and a specific date to add an event. The events displayed at the top of the timeline can include Nitrogen, Planting, Practices, and Soil. To add a nitrogen application event, a user computer can input data to select the nitrogen tab. 238326 1647602 of 57 The user computer can then select a location on the timeline for a particular field in order to indicate a nitrogen application in the selected field. In response to receiving a selection of a location on the timeline for a particular field, the data manager can display a data entry overlay, allowing the user computer to enter data pertaining to nitrogen applications, planting procedures, aboveground applications, tillage procedures, irrigation practices, or other information related to the particular field.For example, if a user computer selects a portion of the timeline and indicates a nitrogen application, then the data entry overlay can include fields to enter the amount of nitrogen applied, the application date, the type of fertilizer used, and any other information related to the nitrogen application. In one implementation, the data manager provides an interface for creating one or more programs. In this context, "program" refers to a set of data relating to nitrogen applications, planting procedures, soil applications, tillage procedures, irrigation practices, or other information that may be related to one or more fields, and which can be stored in a digital data repository for reuse as a set in other operations. Once a program is created, it can be conceptually applied to one or more fields, and references to the program can be stored in a digital repository associated with data that identifies the fields.Therefore, instead of manually entering identical data related to the same nitrogen applications for several different fields, a user computer can create a program that specifies a particular nitrogen application and then apply that program to several different fields. For example, in the timeline view of Figure 5, the first two timelines have the "Applied in Autumn" program selected, which includes an application of 150. 238326 1647602 of 57 lb N / ac in early April. The data manager may provide an interface for editing a program. In one embodiment, when a particular program is edited, each field that has that particular program selected is edited. For example, in Figure 5, if the Fall Applied program is edited to reduce the nitrogen application to 130 lb N / ac, the first two fields can be updated with a reduced nitrogen application based on the edited program (namely, 1 lb = 0.453592 kg, and 1 acre = 4046.86 m²). In one implementation, in response to receiving edits to a field that has a selected schedule, the data manager removes the field's association with the selected schedule. For example, if a nitrogen application is added to the first field in Figure 5, the interface might update to indicate that the Fall Applied schedule no longer applies to the first field. While the nitrogen application in early April might be retained, updates to the Fall Applied schedule will not affect the nitrogen application in April. Figure 6 illustrates an example of a spreadsheet view for data entry. Using the screen shown in Figure 6, a user can create and edit information in one or more fields. The data manager can include spreadsheets for entering information regarding Nitrogen, Planting, Practices, and Soil, as shown in Figure 6. To edit a specific entry, a user can select it in the spreadsheet and update the values. For example, Figure 6 depicts an ongoing update of a target yield value for the second field. Additionally, a user can select one or more fields to apply one or more programs.In response to receiving a program selection for a particular field, the data manager can automatically populate the entries for that field based on the selected program. Similar to the chronological view, the data manager can update the entries for each field associated with a program. 238326 1647602 of 57 in particular in response to receiving a program update. In addition, the data manager may remove the correspondence of the selected program with the field in response to receiving an edit of one of the entries for the field. In one embodiment, the model and field data are stored in the model and field data repository 160. The model data comprises data models created for one or more fields. For example, a crop model might include a digitally constructed model of crop development in one or more fields. In this context, "model" refers to data values ​​and a set of executable instructions stored electronically and associated with each other. This model can receive and respond to programmatic calls or other digital calls, invocations, or requests for resolution based on specified input values, producing one or more stored output values ​​that can serve as the basis for recommendations implemented on computers, output data displays, or machine control systems, among other things.Those experienced in the field find it convenient to express models using mathematical equations, but this form of expression does not limit the models described here to abstract concepts. Instead, each model here has a practical application on a computer in the form of stored executable instructions and the data that implement the model used by the computer. The model data may include a model of events that have already occurred in one or more fields, a model of the current state of one or more fields, and / or a model of events predicted in one or more fields. The model data and fields may be stored in data structures in memory, in the rows of a database table, in flat files or spreadsheets, or in other forms of digital data storage. The hardware / virtualization layer 150 comprises one or more central processing units (CPUs), memory controllers, and other devices, 238326 Layer 150 comprises 57 components or elements of a computer system, such as volatile or non-volatile memory, non-volatile storage such as disks, and / or I / O devices or interfaces, as illustrated and described, for example, in relation to Figure 4. Layer 150 may also comprise programmed instructions configured to support virtualization, containerization, or other technologies. In one example, instructions 136 include different types of instructions for monitoring field operations and performing agricultural data analysis. Instructions 136 may include agricultural data analysis instructions, including instructions for performing operations using the methods described here. Instructions 136 may be included within the programmed instructions of layer 150. To illustrate a clear example, Figure 1 shows a limited number of instances of certain functional elements. However, in other embodiments, there can be any number of such elements. For example, embodiments may utilize thousands or millions of different mobile computing devices 104 associated with different users. Furthermore, the system 130 and / or the external data server may be deployed using two or more processors, cores, pools, or instances of physical or virtual machines, configured in a discrete location or located together with other elements in a data center, shared computing facility, or cloud computing facility. In one embodiment, the implementation of the functions described herein using one or more computer programs or other software elements that are loaded and executed using one or more general-purpose computers will require 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. Furthermore, each of the flowcharts also described in this documentation may serve, alone or in combination with the process and function descriptions in the text herein, as algorithms, plans, or instructions that may be used to program a computer or 238326 1647602 of 57 logic to implement the functions described. In other words, all the text in this document, and all the figures in the drawings, taken together, are intended to provide disclosure of algorithms, plans, or instructions that are sufficient to enable a person skilled in the art to program a computer to perform the functions described herein, in combination with such person's skill and knowledge given the level of skill appropriate for inventions and disclosures of this kind. In one embodiment, user 102 interacts with the agricultural intelligence computing system 130 using the field management computing device 104 configured with an operating system and one or more application programs. The field management computing device 104 can also interact operationally with the agricultural intelligence computing system independently and automatically under program or logic control, and direct user interaction is not always required. The field management computing device 104 generally represents one or more smartphones, PDAs, electronic digital tablets, laptops, desktop computers, workstations, or any other computing device capable of transmitting and receiving information and performing the functions described herein.The field management computing device 104 can communicate via a network using a mobile application stored on the field management computing device 104, and in some embodiments, the device can be coupled by means of a cable 113 or connector to the sensor 112 and / or the controller 114. A particular user 102 may own, operate or possess and use, in connection with the system 130, more than one field management computing device 104 at a time. The mobile application can provide client-side functionality over the network to one or more mobile computing devices. In one example of an embodiment, the 104 field management computing device can access the mobile application through a web browser or a local client application. 238326 The 1647602 of 57 field management computing device 104 can transmit and receive data from one or more front-end servers, using web-based protocols and formats such as HTTP, XML, and / or JSON, or application-specific protocols. In one embodiment, the data can take the form of user requests and input, such as field data, into the mobile computing device. In some embodiments, the mobile application interacts with the location tracking hardware and software on the field management computing device 104, which determines the location of the field management computing device 104 using standard tracking techniques, such as radio signal multi-point, the Global Positioning System (GPS), WiFi positioning systems, or other mobile positioning methods.In some cases, location data or other data associated with device 104, user 102 and / or the user account(s) can be obtained by querying a device operating system or by requesting an application on the device to obtain the data from the operating system. In one embodiment, the field management computing device 104 sends data from field 106 to the agricultural intelligence computing system 130, comprising or including, but not limited to, data values ​​representing one or more of the following: the geographic location of one or more fields, tillage information for one or more fields, the crops planted in one or more fields, and soil data extracted from one or more fields. The field management computing device 104 can send the data from field 106 in response to user input from user 102 specifying the data values ​​for one or more fields. Furthermore, the field management computing device 104 can automatically send the data from field 106 when one or more of the data values ​​become available to the field management computing device 104.For example, the field manager computing device 104 can be communicatively coupled with the remote sensor 112 and / or the application controller 114. In response to receiving. 238326 1647602 of 57 data indicating that application controller 114 released water in one or more fields, the field management computing device 104 can send data from field 106 to the agricultural intelligence computing system 130 indicating that water was released in one or more fields. The data from field 106 identified in this disclosure can be entered and communicated using digital electronic data that communicates between computing devices using parameterized URLs over HTTP or another suitable communication or messaging protocol. A commercial example of the mobile application is CLIMATE FIELDVIEW, marketed by The Climate Corporation of San Francisco, California. The CLIMATE FIELDVIEW application, or other applications, may be modified, extended, or adapted to include features, functions, and programming not disclosed prior to the date of this disclosure. In one embodiment, the mobile application comprises an integrated software platform that enables a grower to make fact-based decisions for their operations by combining historical data about the grower's fields with any other data the grower wishes to compare. These combinations and comparisons can be made in real time and are based on scientific models that provide possible hypotheses to enable the grower to make better, more informed decisions. Figure 2 illustrates two views of an example of the logical organization of instruction sets in main memory when a mobile application is loaded for execution. In Figure 2, each defined element represents a region of one or more pages of RAM or other main memory, or one or more storage blocks on a disk or other non-volatile storage, and the instructions programmed within those regions. In one embodiment, in view (a), a mobile computing application 200 comprises count-fields-data-feed-sharing instructions 202, description and alert instructions 204, digital map book instructions 206, seed and sowing instructions 208, nitrogen instructions 210, weather instructions 212, and instructions for 238326 1647602 of 57 field health conditions 214 and performance instructions 216. In one embodiment, a mobile computer application 200 comprises account-field-data-feed-sharing instructions 202 that are programmed to receive, translate, and feed field data from third-party systems via manual upload or application programming interface (API). Data types may include field boundaries, yield maps, planting maps, soil analysis results, application maps, and / or management zones, among others. Data formats may include form files, third-party native data formats, and / or farm management information system (FMIS) exports, among others. Data reception may occur via manual upload, email with attachment, external APIs that receive the data in the mobile application, or instructions that call external system APIs to extract data from the mobile application.In one embodiment, the Mobile Computing Application 200 comprises a data input tray. Upon receiving a selection from the data input tray, the Mobile Computing Application 200 can display a graphical user interface for manually loading data files and importing loaded files into a data manager. In one embodiment, the Digital Map Book Instructions 206 comprise field map data layers stored in the device's memory and are programmed with data visualization tools and geospatial field notes. This provides growers with convenient and immediate information for reference, record-keeping, and visual insights into field performance. In another embodiment, the Alert and Overview Instructions 204 are programmed to provide an overview of the entire operation, highlighting what is important to the grower, and timely recommendations for taking action or focusing on particular problems. This allows the grower to concentrate on what needs attention, saving time and maintaining performance throughout the operation. 238326 1647602 of 57 seasons. In one implementation, the seed and planting instructions 208 are designed to provide tools for seed selection, hybrid placement, and command set creation, including the creation of variable rate (VR) command sets, based on scientific models and empirical data. This allows growers to maximize yield or return on investment through optimized seed purchase, placement, and population. In one embodiment, the command set generation instructions 205 are programmed to provide an interface for generating command sets (i.e., scripts, command files, or command sequences), including variable rate (VR) fertility command sets. The interface allows growers to create command sets for field tools, such as nutrient applications, seeding, and irrigation. For example, a seeding command set interface might include tools for identifying a seed type for planting. Upon receiving a seed type selection, the mobile computer application 200 can display one or more fields divided into management zones, such as the field map data layers created as part of the digital map book instructions 206.In one embodiment, management zones comprise soil areas along with an identification panel for each soil area and a soil name, texture, drainage for each area, or other field data. Mobile Computing Application 200 can also display tools for editing or creating these areas, such as graphical tools for drawing management zones, such as soil areas, on a map of one or more fields. Planting procedures can be applied to all management zones, or different planting procedures can be applied to different subsets of management zones. When a command set is created, Mobile Computing Application 200 can allow the command set to be downloaded in a format readable by an application driver. 238326 1647602 of 57 as an archived or compressed format. Additionally and / or alternatively, a set of commands can be sent directly to the cockpit computer 115 from a mobile computer application 200 and / or uploaded to one or more data servers and stored for later use. In one embodiment, the nitrogen instructions 210 are programmed to provide tools for informing nitrogen decisions by visualizing nitrogen availability to crops. This enables growers to maximize yield or return on investment through optimized nitrogen application during the growing season.Examples of programmed functions include displaying images such as SSURGO images to allow drawing of application zones and / or images generated from subfield soil data, such as data obtained with sensors, at high spatial resolution (e.g., 10 meters or less due to their proximity to the ground); loading existing zones defined by the producer; providing an application graph and / or a map to allow one or more nitrogen adjustment applications across multiple zones; outputting command sets to drive machinery; tools for bulk data entry and adjustment; and / or maps for data visualization, among others.In this context, bulk data entry can mean entering data once and then applying that same data to multiple fields defined in the system. Examples of such data might include nitrogen application data that is the same for many fields of the same grower, but this bulk data entry applies to the entry of any type of field data in the Mobile Computer Application 200. For example, Nitrogen Instructions 210 can be programmed to accept definitions of nitrogen seeding programs and practices and accept user input specifying the application of those programs to multiple fields. In this context, nitrogen seeding programs refer to a defined set of data that associates: a name, color code or other identifier, one or more application dates, and application types. 238326 1647602 of 57 materials or products for each of the dates and quantities, method of application or incorporation, for example, injected or inserted, and / or application quantities or rates for each of the dates, crop or hybrid to which the application is being made, among others. In this context, nitrogen practice programs refer to a specific data set that associates: a name of the practices; a previous crop; a tillage system; a main tillage date; one or more tillage systems previously used; one or more indicators of the type of application, such as manure, that were used.The Nitrogen 210 instructions can also be programmed to generate and cause the display of a nitrogen chart, which indicates projections of the specified nitrogen implementation usage and whether a surplus or shortage is anticipated; in some embodiments, different colored indicators may indicate a magnitude of surplus or a magnitude of shortage.In one embodiment, a nitrogen chart comprises a graphic display on a computer display device comprising a plurality of rows, each row associated with a field and the field identification; data specifying what crop is planted in the field, the size of the field, the location of the field, and a graphical representation of the field perimeter; in each row, a timeline for each month with graphical indicators specifying each application and the amount of nitrogen at points correlated with the names of the months; and numerical and / or color indicators of surplus or shortage, wherein the color indicates its magnitude. In one embodiment, the nitrogen chart may include one or more user input features, such as dials or sliders, to dynamically change seeding programs and nitrogen practices so that a user can optimize their nitrogen chart. The user can then use their optimized nitrogen chart and the related seeding programs and nitrogen practices to implement one or more command sets, including variable-rate (VR) fertility command sets. 238326 The 1647602 of 57 nitrogen 210 can also be programmed to generate and display a nitrogen map, indicating projections of the specified nitrogen application use and whether a surplus or shortage is anticipated. In some implementations, different colored indicators can indicate the magnitude of the surplus or shortage. The nitrogen map can show projections of the specified nitrogen application use and whether a surplus or shortage is anticipated for different times in the past and future (e.g., daily, weekly, monthly, or annually) using numerical and / or color indicators of surpluses or shortages, where the color indicates the magnitude.In one embodiment, the nitrogen map may include one or more user input features, such as dials or sliders, to dynamically change planting schedules and nitrogen practices so that a user can optimize their nitrogen map, such as to achieve a preferred amount of surplus or deficit. The user can then use their optimized nitrogen map and the related planting schedules and nitrogen practices to implement one or more command sets, including variable-rate (VR) fertility command sets. In other embodiments, instructions similar to the nitrogen 210 instructions could be used for the application of other nutrients (e.g., phosphorus and potassium), pesticide application, and irrigation schedules. In one implementation, weather instructions 212 are programmed to provide recent field-specific weather data and forecast information. This allows growers to save time and has an efficient, integrated display for daily operational decisions. In one implementation, the health condition instructions for field 214 are programmed to provide timely remote sensing images to establish seasonal crop variation and potential problems. Examples of programmed functions include cloud verification to identify 238326 1647602 of 57 possible clouds or cloud shadows; the determination of nitrogen indices from field images; the graphical visualization of scan layers, including, for example, those related to field conditions, and the visualization and / or exchange of scan notes; and / or the downloading of satellite images from multiple sources and the prioritization of images for the producer, among others. In one implementation, the 216 performance instructions are programmed to provide reports, analyses, and visual perception tools using farm data for evaluation, insights, and decision-making. This allows the grower to aim for better results for the coming year through fact-based conclusions about why the return on investment was at previous levels and an understanding of the factors limiting performance. The 216 performance instructions can be programmed to communicate through the 109 network of end-user analysis programs running on the 130 farm intelligence computer system and / or the 108 external data server and configured to analyze metrics such as yield, hybrid, population, SSURGO, soil analysis, or elevation, among others.Scheduled reports and analyses may include correlations between yield and another agricultural data parameter or variable, analysis of yield variability, benchmarking of yield and other measures compared with other producers based on anonymously collected data from many producers, or seed and planting data, among others. Applications with instructions configured in this way can be deployed on different computing device platforms while maintaining the same overall user interface appearance. For example, a mobile application can be programmed to run on tablets, smartphones, or servers accessed via web browsers on client computers. Furthermore, a mobile application configured for tablets or smartphones can provide a 238326 1647602 of 57 complete application experience or a cockpit application experience suitable for the visualization and processing of cockpit computer capabilities 115. For example, with reference now to view (b) of Figure 2, in one embodiment a cockpit computer application 220 may comprise cockpit map instructions 222, remote view instructions 224, data collection and transfer instructions 226, machine alert instructions 228, script set transfer instructions 230, and cockpit scan instructions 232.The code base for view instructions (b) can be the same as for view (a), and the executables implementing the code can be programmed to detect the type of platform on which they are running and display, through a graphical user interface, only those functions appropriate for a cab platform or a full platform. This approach allows the system to recognize the distinctly different user experience appropriate for a cab environment and the different cab technology environment. Cab map instructions 222 can be programmed to provide map views of fields, farms, or regions that are useful for directing machine operation.Remote View Instructions 224 can be programmed to activate, manage, and provide real-time or near-real-time views of machine activity to other computing devices connected to the system 130 via wireless networks, wired connectors or adapters, etc. Data Collection and Transfer Instructions 226 can be programmed to activate, manage, and provide the transfer of data collected by machine sensors and controllers to the system 130 via wireless networks, wired connectors or adapters, and similar means. Machine Alert Instructions 228 can be programmed to detect problems in the operation of the machine or associated tools in the cab and generate operator alerts. Command Set Transfer Instructions 230 can be configured to transfer command sets. 238326 1647602 of 57 instructions that are configured to direct machine operations or data collection. The 232 cab-scan instructions can be programmed to display location-based alerts and information received from system 130 based on the location of the agricultural implement 111 or sensors 112 in the field, and to feed, manage, and provide the transfer of location-based scan observations to system 130 based on the location of the agricultural implement 111 or sensors 112 in the field. In one embodiment, the external data server 108 stores external data 110, including soil data representing soil composition for one or more fields and meteorological data (climate data) representing temperature and precipitation in one or more fields. The meteorological data may include past and present weather data, as well as forecasts of future weather. In one embodiment, the external data server 108 comprises a plurality of servers hosted by different entities. For example, a first server may contain soil composition data, while a second server may contain meteorological data. Furthermore, the soil composition data may be stored on multiple servers.For example, one server might store data representing the percentage of sand, silt, and clay in the soil, while a second server might store data representing the percentage of organic matter (OM) in the soil. In one embodiment, the remote sensor 112 comprises one or more sensors programmed or configured to produce one or more observations. The remote sensor 112 can be an aerial sensor, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other tool capable of receiving data from one or more fields. In one embodiment, the application controller 114 is programmed or configured to receive instructions from the agricultural intelligence computing system 130. The controller of. 238326 Application 114, section 1647602 of 57, can also be programmed or configured to control an operating parameter of a vehicle or agricultural implement. For example, an application controller can be programmed or configured to control an operating parameter of a vehicle, such as a tractor, seeding equipment, tillage equipment, fertilizer or insecticide application equipment, combine harvester equipment, or other farm implements, such as a water valve. Other embodiments may use any combination of sensors and controllers, of which the following are merely selected examples. System 130 can obtain and feed data under the control of user 102, in bulk, from a large number of producers who have contributed data to a shared database system. This method of obtaining data can be called manual data feeding because one or more user-controlled computer operations are requested or activated to obtain data for use by System 130. As an example, the CLIMATE FIELDVIEW application, marketed by The Climate Corporation of San Francisco, California, can be used to export data to System 130 for storage in repository 160. For example, seed monitoring systems can both control components of the planting apparatus and obtain planting data, including signals from seed sensors, through a signal harness comprising a CAN bus hub and point-to-point connections for logging and / or diagnostics. Seed monitoring systems can be programmed or configured to display seed spacing, population, and other information to the user via the cab computer 115 or other devices within the system 130. Examples are disclosed in U.S. Patent No. 8,738,243 and U.S. Patent Publication 20150094916, and this disclosure assumes knowledge of those other patent disclosures. Similarly, yield monitoring systems may contain 238326 1647602 of 57 performance sensors for combine harvester implements that send performance measurement data to the cab computer 115 or other devices within the system 130. Performance monitoring systems may use one or more remote sensors 112 to obtain grain moisture measurements on a combine harvester or other combine and transmit these measurements to the user via the cab computer 115 or other devices within the system 130. In one embodiment, examples of sensors 112 that may be used with any moving vehicle or implement of the type described herein include kinematic sensors and position sensors. Kinematic sensors may comprise speed sensors such as radar or wheel speed sensors, accelerometers, or gyroscopes.Position sensors can include GPS receivers or transceivers, WiFi-based positioning, or mapping applications that are programmed to determine a location based on nearby WiFi access points, among others. In one embodiment, examples of sensors 112 that can be used with tractors or other moving vehicles include engine speed sensors, fuel consumption sensors, surface counters or distance counters that interface with GPS or radar signals, PTO (power take-off) speed sensors, hydraulic sensors for tractors configured to detect hydraulic parameters such as pressure or flow and / or hydraulic pump speed, wheel speed sensors, or wheel slip sensors. In one embodiment, examples of controllers 114 that can be used with tractors include hydraulic directional controllers, pressure and / or flow controllers; hydraulic pump speed controllers; speed controllers or regulators; linkage position controllers; or wheel position controllers that provide automatic steering. In one embodiment, examples of the 112 sensors that can be used with seed planting equipment such as planters, plows, or seeders 238326 1647602 of 57 pneumatics include seed sensors, which may be optical, electromagnetic or impact; aerodynamic load 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 performance criteria sensors such as sowing depth sensors, aerodynamic load cylinder pressure sensors, seed disc speed sensors, seed drive motor encoders, seed transport system speed sensors or vacuum level sensors;or pesticide application sensors, such as optical or other electromagnetic sensors or impact sensors. In one embodiment, examples of controllers 114 that can be used with these seed-planting equipment include: toolbar folding controllers, such as valve controllers associated with hydraulic cylinders; aerodynamic load controllers, such as valve controllers associated with pneumatic cylinders, air bags, or hydraulic cylinders, and programmed for applying aerodynamic load to individual row units or an entire planter frame; seeding depth controllers, such as linear actuators; metering controllers, such as electric seed metering drive motors, hydraulic seed metering drive motors, or residue control wheels;hybrid selection controllers, such as seed metering drive motors, or other actuators programmed to allow or prevent seeds or an air-seed mixture from entering or leaving seed regulators or central bulk distributors; metering controllers, such as electric seed metering drive motors, or hydraulic seed metering drive motors; seed conveying system controllers, such as conveyor motor controllers for a seed delivery belt; 238326 1647602 of 57 marker controllers, such as a controller for a pneumatic or hydraulic actuator; pesticide application rate controllers, such as meter drive controllers, orifice size or position controllers. In one embodiment, examples of sensors 112 that can be used with tillage equipment include tool position sensors such as for shanks or discs; tool position sensors for such tools configured to detect depth, disc set angle, or side clearance; aerodynamic load sensors; or draft force sensors. In one embodiment, examples of controllers 114 that can be used with tillage equipment include aerodynamic load controllers or tool position controllers, such as controllers configured to control tool depth, disc set angle, or side clearance. In one embodiment, examples of sensor 112 that can be used in connection with an apparatus for applying fertilizers, insecticides, fungicides and the like, such as 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 sprayer head valves or liquid line valves are open; sensors associated with tanks, such as fill level sensors; section or whole system feed line sensors, or row-specific feed line sensors; or kinematic sensors such as accelerometers arranged on spray arms.In one embodiment, examples of 114 controllers that can be used with such devices include pump speed controllers; valve controllers programmed to control pressure, flow, direction, pulse-width modulation (PWM), and the like; or position actuators. 238326 1647602 of 57 such as for arm height, subsoiler depth or arm position. In one embodiment, examples of 112 sensors that can be used with combine harvesters include performance monitors, such as strain impact plate gauges or position sensors, capacitive flow sensors, load sensors, weight sensors or torque sensors associated with elevators or screw conveyors, 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 performance criteria sensors such as header height, header type, cover plate clearance, feeder speed and cylinder speed sensors; separator performance criteria sensors, such as concave clearance, rotor speed, shoe clearance or screen clearance sensors;Screw conveyor sensors for position, operation, or speed; or motor speed sensors. In one embodiment, examples of controllers 114 that can be used with combine harvesters include header performance criteria controllers for elements such as header height, header type, cover plate clearance, feed rate, or cylinder speed; separator performance criteria controllers for features such as concave clearance, rotor speed, shoe clearance, or screen clearance; or controllers for screw conveyor position, operation, or speed. In one embodiment, examples of sensors 112 that can be used with grain carts include weight sensors or sensors for position, operation, or speed of the screw conveyor. In one embodiment, examples of controllers 114 that can be used with grain carts include controllers for position, operation, or speed of the screw conveyor. 238326 1647602 of 57 In one embodiment, the examples of sensors 112 and controllers 114 can be installed on unmanned aerial vehicles (UAVs) or drones. Such sensors may include cameras with detectors effective for any range of the electromagnetic spectrum, for example, visible light, infrared, ultraviolet, near-infrared (NIR), and the like; accelerometers; altimeters; temperature sensors; humidity sensors; pitot tube sensors or other airspeed or wind speed sensors; battery life sensors; or radar emitters and radar energy detection devices. Such controllers may include guidance or motor control devices, control surface controllers, camera controllers, and controllers programmed to activate, operate, acquire data from, manage, and configure any of the above sensors.Examples are disclosed in US Patent Application No. 14 / 831,165 and this disclosure assumes knowledge of that other patent disclosure. In one embodiment, the sensors 112 and controllers 114 can be attached to soil sampling and measuring apparatus that are configured or programmed to take soil samples and perform soil chemistry tests, soil moisture tests, and other soil-related tests. For example, the apparatus disclosed in U.S. Patent No. 8,767,194 and U.S. Patent No. 8,712,148 can be used, and this disclosure assumes knowledge of those other patent disclosures. In another embodiment, the sensors 112 and controllers 114 may comprise meteorological devices for monitoring the climatic / meteorological conditions of the fields. For example, the apparatus described in International Patent Application No. PCT / US2016 / 029609 may be used, and the present invention assumes knowledge from those patent disclosures. In one embodiment, the agricultural intelligence computing system 130 is programmed or configured to create an agronomic model. In this context, a 238326 1647602 of 57 An agronomic model is a data structure in the memory of the agricultural intelligence computing system 130 that comprises field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties that describe the conditions that can affect the growth of one or more crops in a field, or the properties of one or more crops, or both. In addition, an agronomic model may comprise recommendations based on agronomic factors such as crop recommendations, irrigation recommendations, planting recommendations, and harvesting recommendations. Agronomic factors can also be used to estimate one or more related crop outcomes, such as agronomic yield.The agronomic yield of a crop is an estimate of the amount of the harvest produced, or in some examples the income or profit obtained from the harvest produced. In one embodiment, the 130 agricultural intelligence computing system can use a preconfigured agronomic model to calculate agronomic properties related to the currently received location and crop information for one or more fields. The preconfigured agronomic model is based on 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 its accuracy.Cross-validation can include a comparison with field verification that compares predicted results with actual results in a field, such as comparing the precipitation estimate with a rain gauge or sensor that provides meteorological data in or near the same location, or comparing an estimate of nitrogen content with a measurement of the soil sample. Figure 3 illustrates a programmed process by which the agricultural intelligence computing system generates one or more pre-configured agronomic models. 238326 1647602 of 57 using field data provided by one or more data sources. FIG. 3 can serve as an algorithm or instructions for programming functional elements of the agricultural intelligence computing system 130 to perform the operations described. In block 305, the agricultural intelligence computing system 130 is configured or programmed to implement the preprocessing of agronomic field data received from one or more data sources. The field data received from one or more data sources can be preprocessed to remove noise and distorting effects within the agronomic data, including measured outliers that would skew the received field data values.Methods of performing preprocessing of agronomic data may include, but are not limited to, removing data values ​​commonly associated with outliers, specific measured data points known to unnecessarily bias other data values, data normalization techniques used to remove or reduce additive or multiplicative effects of noise, and other data filtering or derivation techniques used to provide a clear distinction between positive and negative data inputs. In block 310, the agricultural intelligence computing system 130 is configured or programmed to perform data subset selection using pre-processed field data to identify datasets useful for the initial generation of the agronomic model. The agricultural intelligence computing system 130 can implement data subset selection techniques, including, but not limited to, a genetic algorithm method, an all-models subset method, a sequential search method, a stepwise regression method, a particle cloud optimization method, and an ant colony optimization method. For example, a genetic algorithm selection technique uses an adaptive heuristic search algorithm, based on 238326 1647602 of 57 evolutionary principles of natural and genetic selection, to determine and evaluate the datasets within the previously processed agronomic data. In block 315, the agricultural intelligence computing system 130 is configured or programmed to implement the evaluation of the field dataset. In one embodiment, a field-specific dataset is evaluated by creating an agronomic model and using quality thresholds specific to that model. The agronomic models can be compared using cross-validation techniques, including, but not limited to, the root mean square error of cross-validation (RMSECV), the mean absolute error, and the mean percentage error. For example, RMSECV can cross-validate agronomic models by comparing the predicted agronomic property values ​​created by the model against collected and analyzed values ​​of historical agronomic properties.In one embodiment, the logic of the evaluation of the agronomic dataset is used as a feedback loop, where agronomic datasets that do not meet the configured quality thresholds are used during the subsequent data subset selection steps (block 310). In block 320, the agricultural intelligence computing system 130 is configured or programmed to implement the creation of the agronomic model based on cross-validated agronomic datasets. In one embodiment, the creation of the agronomic model can implement multivariate regression techniques to create models from pre-configured agronomic data. In block 325, the agricultural intelligence computing system 130 is configured or programmed to store the previously configured agronomic data models for future evaluation of field data. According to one form of implementation, the techniques described herein are 238326 1647602 of 57 implemented by one or more special-purpose computing devices. Special-purpose computing devices may be hardwired to carry out the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs) that are constantly programmed to carry out the techniques, or may include one or more general-purpose hardware processors programmed to carry out the techniques in accordance with program instructions in firmware, memory, other storage, or a combination thereof.These special-purpose computing devices can also combine custom hardwired logic, ASICs, and FPGAs with custom programming to perform the techniques. Custom special-purpose devices can be desktop and laptop computer systems, handheld devices, network devices, or any other device that incorporates hardwired and / or program logic to implement the techniques. For example, FIG. 4 is a block diagram illustrating a computer system 400 on which an embodiment of the invention can be implemented. The computer system 400 includes a bus 402 or other communication mechanism for transmitting information, and a hardware processor 404 along with a bus 402 for processing information. The hardware processor 404 can be, for example, a general-purpose microprocessor. The computer system also includes a main memory, such as random access memory (RAM) or another dynamic storage device, coupled with the bus to store information and instructions to be executed by the processor. The main memory can also be used to store temporary variables or other intermediate information during the execution of instructions. 238326 1647602 of 57 processor 404. These instructions, when stored on non-transient storage media accessible to the processor 404, convert the 400 computer system into a special-purpose machine customized to perform the operations specified in the instructions. The 400 computer system also includes a read-only memory (ROM) 408 or other static storage device coupled to the bus 402 to store static information and processor instructions 404. A storage device 410, such as a magnetic disk, optical disk, or solid-state drive, is provided and coupled to the bus 402 to store information and instructions. The computer system 400 can be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT), to show information to a computer user. An input device 414, including alphanumeric and other keys, is coupled via bus 402 to communicate information and instruction selections to the processor 404. Another type of user input device is the cursor control 416, such as a mouse, trackball, or cursor direction keys, to communicate direction information and instruction selections to the processor 404 and to control the movement of the cursor on the display 412. This input device generally has two degrees of freedom on two axes, a first axis (e.g., x) and a second axis (e.g., y), which allows the device to specify positions in a plane. The Computing System 400 can implement the techniques described herein using custom-hardwired logic, one or more ASICs and FPGAs, firmware, and / or program logic that, in combination with the Computing System, enables the programming of the Computing System 400 into a special-purpose machine. According to one embodiment, the techniques of the present invention are implemented by the Computing System 400 in response to the Processor 404 executing one or more sequences of one or more instructions. 238326 1647602 of 57 contained in main memory 406. These instructions can be read into main memory 406 from another storage medium, such as storage device 410. Executing the instruction sequences contained in the processor's main memory 406 causes the processor 404 to carry out the process steps described herein. In alternative embodiments, hardwired circuits may be used instead of, or in combination with, software instructions. As used herein, the term storage media refers to any non-transient medium that stores data and / or instructions that cause a machine to operate in a specific manner. Such storage media may comprise non-volatile and / or volatile media. Non-volatile media include, for example, optical discs, magnetic discs, or solid-state drives, such as the 410 storage device. Volatile media include dynamic memory, such as the 406 main memory. Common forms of storage media include, for example, a floppy disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, RAM, PROM and EPROM, FLASH-EEPROM, NVRAM, and any other memory chip or cartridge. Storage media are distinct from transmission media but can be used with them. Transmission media are involved in the transfer of information between storage media. For example, transmission media include coaxial cables, copper cables, and fiber optic cables, in addition to the cables that make up the 402 bus. Transmission media can also take the form of acoustic or light waves, such as those generated during infrared data communications and radio waves. Various forms of media can carry one or more 238326 1647602 of 57 sequences of one or more instructions to the processor 404 for execution. For example, the instructions may initially be transported on a magnetic disk or solid-state drive of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A local modem connected to the computer system 400 may receive the data on the telephone line and use an infrared transmitter to convert the data into an infrared signal. An infrared detector may receive the data carried in the infrared signal, and appropriate circuitry may place the data on the bus 402. The bus 402 carries the data to the main memory 406, from which the processor 404 retrieves and executes the instructions.Instructions received by main memory 406 can optionally be stored in storage device 410, either before or after execution by processor 404. The computer system 400 also includes a communication interface 418 coupled to bus 402. The communication interface 418 provides bidirectional data communication coupling to a network link 420 that is connected to a local area network 422. For example, the communication interface 418 can be an Integrated Services Digital Network (ISDN) card, a cable modem, a satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface 418 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links can also be implemented.In this implementation, the 418 communication interface sends and receives electrical, electromagnetic, or optical signals that carry continuous streams of digital data representing various types of information. Network link 420 typically provides data communication across one or more networks to other data devices. For example, network link 420 can provide a connection across local network 422 to a host computer 424. 238326 1647602 of 57 or with data equipment operated by an Internet Service Provider (ISP) 426. The ISP 426, in turn, provides data communication services through the worldwide packet data communication network now known as the Internet 428. Both the local network 422 and the Internet 428 use electrical, electromagnetic, or optical signals that carry continuous streams of digital data. Signals across the different networks and signals on the network link 420 and across the communication interface 418, which carry digital data to and from the computer system 400, are examples of forms of transmission media. The computer system 400 can send messages and receive data, including program code, across the network or networks, the network link 420 and the communication interface 418. In the Internet example, a server 430 could transmit a request code for an application program across the Internet 428, the ISP 426, the local network 422 and the communication interface 418. The received code can be executed by the processor 404 because it is received and / or stored in the storage device 410, or other non-volatile storage for later execution. Figure 7 illustrates a flowchart of one embodiment of a Method 700 for automatically identifying one or more correlations for field operations. Method 700 is carried out by processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (as executed on a general-purpose computer system or a dedicated machine or device), or a combination of both. In one embodiment, Method 700 is carried out by the processing logic of at least one data processing system (e.g., computer system 130, computer system 400, field manager computer device 104, cabin computer 115, application controller 114, apparatus 111, etc.). The system or device executes instructions from a software application or program with the processing logic. The software application or program may be initiated by a 238326 1647602 of 57 system or can notify an operator or user of a machine (e.g. tractor, seeder, mower-thresher) depending on whether one or more correlations were determined. In block 702, a system monitors agricultural data, including field and yield data (e.g., identification data, harvest data, planting data, fertilizer data, pesticide data, irrigation data, and climate data, information on farming practices, input cost information, raw material information, etc.). In block 704, the system (or device) automatically determines whether at least one correlation between different variables or parameters of the agricultural data exceeds a threshold. For example, a correlation between yield data and a variable of farming practice (e.g., planting data, applied nutrients) in the field data may have exceeded a threshold. The correlation can be defined by experimental data, a certain yield differential between different values ​​of a variable or parameter, or an R² value exceeding a threshold value.In this case, certain R² values ​​between 0 and 1 (e.g., 0.8, 0.9) indicate a strong correlation. If so, then the system (or device) in block 705 performs an analysis (e.g., geometric analysis) to identify a category of human-caused problems (e.g., crop application problems, etc.) or other problems (e.g., irrigation problems, pest problems near a waterway, etc.) that may have caused the correlation. If the correlation between different variables or parameters of the agricultural data does not exceed a threshold, then the method returns to block 702. In one example, in block 706, the system (or device) checks for a potential irrigation problem for one or more fields by determining whether the crop yield for the field(s) exhibits a geometric pattern (e.g., circular pattern, linear pattern). Irrigation problems can result from a damaged or defective pivot in an irrigation system. In block 708, if it was determined 238326 1647602 of 57 a geometric pattern, then the system (or device) determines that the irrigation can be identified as corresponding to the geometric pattern. In block 716, if the system determines a geometric pattern and identifies that the irrigation corresponds to the geometric pattern, then a communication (e.g., irrigation problem alert communication, email message, text message, map, etc.) is sent to a user's device. The communication indicates that at least one correlation exceeds a threshold. The system may also send a fault comparison center when at least one correlation exceeds a threshold. The system may also send a recommendation to take action in response to the at least one correlation that exceeds the threshold. The communication may include an alert, maps, a fault comparison center, and a recommendation.In block 724, the user's device then causes a device display to show at least one of the following: an alert (for example, an irrigation problem alert communication), a map, a fault comparison center, and a recommendation. A recommendation can be generated and displayed in response to a user selection of a subcategory or variable that is an agricultural practice parameter or input parameter. The system or device can receive user input in response to the communication, fault comparison center, or recommendation. If no geometric pattern was determined in block 706 or irrigation was not identified in block 708, then the method returns to block 702. In another example, in block 712, the system (or device) checks for a potential problem in the application pass for one or more fields by determining whether the crop yield for the field(s) has a geometric pattern (e.g., linear pattern). In block 714, the system (or device) then determines whether the identified geometric pattern corresponds to an application pass (e.g., planting, fertilization, etc.). In block 717, if the system determines a geometric pattern and identifies that the geometric pattern is 238326 Block 1647602 of 57 corresponds to an application pass, and a communication (e.g., application problem alert communication, email message, text message, map, etc.) is then sent to a user. The communication indicates that at least one correlation exceeds a threshold. The system may also send a fault comparison center when at least one correlation exceeds a threshold. The system may also send a recommendation to take action in response to the at least one correlation that exceeds the threshold. The communication may include an alert, maps, a fault comparison center, and a recommendation. In block 726, the user's device then causes a device display to show at least one of the following: an alert (e.g., application problem communication), a map, a fault comparison center, and a recommendation.The system or device can receive user input in response to communication, fault comparison center, or recommendation. A recommendation can be generated and displayed in response to a user selection of a subcategory or variable that is an input parameter or a field data farming practice parameter. If a geometric pattern is not determined in block 706 or a geometric pattern corresponding to an application pass is not identified in block 714, then the method returns to block 702. An example of a pass-through application problem is a mechanical issue during seed planting. The system can determine a correlation between yield and a planting variable from the planting data. This correlation can help identify that a planter over-planted certain regions of a field, likely due to a mechanical error during planting. The over-planted regions can be correlated with other variables or parameters, such as yield. This correlation indicates a mechanical problem, such as a faulty clutch, that causes the loss of a certain number of seed bags. In another example, a seed population deviation across different 238326 1647602 of 57 regions of a field may be linked or correlated with a curve-fit failure. In another example, in block 720, the system checks for a potential insect or pest problem for one or more fields by determining if a waterway is located near one or more fields on a map (e.g., map tile, user-drawn outline). In block 722, the system then determines if a yield data pattern corresponds to a location or channel of the waterway. In block 719, if the system identifies a waterway near one or more fields and determines that a pattern (e.g., yield data patterns) corresponds to a location or channel of the waterway, then a communication (e.g., pest problem alert communication, email, text message, map, etc.) is sent to a user device. The communication indicates that at least one correlation exceeds a threshold.The system can also send a fault comparison center when at least one correlation exceeds a threshold. The system can also send a recommendation to take action in response to the at least one correlation that exceeds the threshold. The communication can include an alert, maps, a fault comparison center, and a recommendation. In block 728, the user's device then causes a display device to show at least one of the following: an alert (for example, a pest problem communication), a map, a fault comparison center, and a recommendation. A recommendation can be generated and displayed in response to a user selection of a subcategory or variable that is an input parameter or an agricultural practice parameter. The system (or device) can receive user input in response to the communication, fault comparison center, or recommendation.If a waterway is not identified in block 720 or there is no pattern that matches a waterway location or channel in block 722, then the method reverts to block 702. 238326 1647602 of 57 Upon determining that at least one correlation between different variables or parameters of agricultural data exceeds a threshold, the system (or device) can perform the operations / functions of blocks 706, 712, and 720 simultaneously or sequentially. Method 700 returns to block 702 if no correlation exceeding a threshold is determined in block 704. Figure 8 illustrates a flowchart of one embodiment of Method 800 for creating trials to induce one or more correlations among different variables or parameters of agricultural data. Method 800 is carried out by processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (such as that running on a general-purpose computer system or a dedicated machine or device), or a combination of both. In one embodiment, Method 800 is carried out by the processing logic of at least one data processing system (e.g., computer system 130, computer system 400, field manager computer device 104, cabin computer 115, application controller 114, apparatus 111, etc.). The system or device executes instructions from a software application or program with processing logic. In block 802, a system monitors agricultural data, including field and yield data (e.g., harvest data, planting data, fertilizer data, weather data, input cost information, raw material information, etc.). In block 804, the system creates at least one trial that potentially causes one or more correlations between different parameters or variables of the agricultural data in response to receiving communication from a device (e.g., a software application or device program) triggered by one or more user inputs (e.g., user input(s) after conducting a farming operation, user input(s) during the farming operation). For example, a user might vary a parameter or variable. 238326 1647602 of 57 in different regions or strips of one or more fields to create a trial that causes a correlation between yield data and a parameter or variable in the agricultural data (e.g., a variable related to farming practices, planting information, application rate, products applied, planting depth study, nutrients applied, etc.). In one example, a user changes a variable (e.g., seed population, planting density, nitrogen applied) in different regions or strips of a field to cause the correlation. The user can create the trial after performing a farming operation (e.g., planting, tillage, nutrient application, etc.). In another example, a user draws a polygon on a map of a field to assign a region to the polygon and then changes a region for that polygon. A northern region of a field might be region 1, while a southern region of a field might be region 2.In another example, a user can label a waterlogged area that was sown in a certain month as a region. A user can create any custom region or configuration; configurations can be implemented by the 130 system (for example, a commanded seed population) or implemented by the user (for example, a closed-system downforce configuration or a seeding depth configuration). Alternatively, a user creates a live, real-time trial while farming is underway. In one example, a user selects a recording option from a device (e.g., a display on a machine, a tablet, etc.) to start recording a first region during an initial operation (e.g., planting) and then selects a recording or stop option at a later time to complete an area defined by the first region. The user can then define additional regions for a field or fields for the initial operation. The data associated with each different region is then provided to a system (e.g., a cloud-based system). At a later date or time, a second operation is performed (e.g., planting). 238326 1647602 of 57 harvest, fertilization, etc.) with a different machine (or the same machine) and the data (e.g., yield) are automatically partitioned into the predefined regions. In block 806, the system creates at least one trial to potentially generate one or more correlations between different variables or parameters of agricultural data for field operations in response to communication from the device. This communication is generated in response to user inputs (e.g., user input(s) after conducting a farm operation, user input(s) during the farm operation). In block 808, the system assigns data (e.g., yield data) based on the regions created by the at least one trial. For example, yield data for a subsequent farm operation (e.g., harvest) is assigned according to regions created by the trial.In block 810, the system analyzes at least one trial to determine if it produces at least one correlation (for example, a correlation between yield data and a farming practice variable) for different regions or strips within one or more fields. In block 812, the system generates and sends data to a device for display to the user for at least one trial. The data may show at least one correlation for the different regions or strips within the field(s) of the at least one trial, or it may show an absence of at least one correlation. The presented data can serve as a return on investment (ROI) tool, allowing the user to determine an optimal region or optimal set of conditions to maximize ROI. For example, a first region of a field has nutrients applied and exhibits a first yield.A second region of the field has a certain amount of nutrients applied and a second yield. The ROI tool allows the user to determine if the additional cost of the applied nutrients increases the yield sufficiently (e.g., the increase in yield equals the second yield minus the first yield) to justify the investment. 238326 1647602 of 57 justify future crops with the additional cost of the nutrients applied. Figure 9 illustrates a flowchart of one embodiment of a Method 900 for creating trials to induce one or more correlations among different variables or parameters of agricultural data. Method 900 is carried out by processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (as executed on a general-purpose computer system or a dedicated machine or device), or a combination of both. In one embodiment, Method 900 is carried out by the processing logic of at least one data processing system (e.g., computer system 130, computer system 400, field manager computer device 104, cabin computer 115, application controller 114, apparatus 111, etc.). The system or device executes instructions from a software application or program with processing logic. A system monitors agricultural data, including field and yield data (e.g., harvest data, planting data, fertilizer data, weather data, input cost information, and raw material information, etc.). In block 904, a user device receives one or more user inputs (e.g., user input(s) after conducting a farming operation, user input(s) during a farming operation) to create at least one trial that potentially establishes one or more correlations between different parameters or variables. For example, a user might vary a parameter or variable in different regions or strips of one or more fields to create a trial that establishes a correlation between yield data and a variable of the farming practice or field data (e.g., planting information, application rate, products applied, planting depth study, nutrients applied).In one example, a user changes a variable (e.g., seed population, seed density, nitrogen applied) in different regions or strips of a field to induce a correlation. The user can then create the trial after performing one. 238326 1647602 of 57 agricultural exploitation (e.g., sowing, tilling, nutrients applied, etc.). In one example, a user draws a polygon on a map of a field to assign a region to the polygon and then changes a variable for that region. Alternatively, a user creates a live, real-time trial while farming is underway. For example, a user selects a recording option from a device (e.g., a display on a machine, a tablet, etc.) to start recording a region during the first operation (e.g., planting) and then selects a recording or stop option at a later time to complete an area defined by the first region. The user can then define additional regions for a field or fields for the first operation. The data associated with each additional region is then provided to a system (e.g., a cloud-based system). At a later date or time, a second operation (e.g., harvesting, fertilizing, etc.) is performed with a different machine (or the same machine), and the data (e.g., yield) is automatically partitioned into the previously defined regions.In block 906, the device creates at least one trial to potentially elicit one or more correlations between different variables or parameters of agricultural data for field operations in response to user inputs (e.g., user input(s) after conducting a farming operation, user input(s) during a farming operation). In block 908, the device allocates data (e.g., yield data) based on the regions created by the at least one trial. For example, yield data for a subsequent farming operation (e.g., harvest) is allocated according to the regions created by the trial. In block 910, the device (or system) analyzes the at least one trial created to determine whether it elicits at least one correlation (e.g., a correlation between yield data and a farming practice variable) for different regions or strips of one or more fields. 238326 1647602 of 57 fields. In block 912, the device generates and displays data to the user for at least one trial. The device may present data that includes at least one correlation for different regions or strips of the field(s) of the at least one trial, or it may present an absence of at least one correlation. The presented data can be a return on investment (ROI) tool that allows the user to determine an optimal region or optimal set of conditions to maximize ROI. In one example, a first region of a field has no applied nutrients and has a certain yield. A second region of the field has a certain amount of applied nutrients and a second yield.ROI tools allow the user to determine whether the additional cost of applied nutrients sufficiently increases yield (e.g., the yield increase equals the second yield minus the first yield) to justify future crops with the added cost of applied nutrients. The data presented may also include a benchmark, as further described in this document. Some ways of implementing comparison center user interfaces are illustrated in Figures 10 and 11 and are described in more detail below. The comparison center user interface preferably includes an agronomic result (e.g., a yield value such as average yield in bushels per acre, economic yield in dollars per acre) corresponding to a plurality of criteria (e.g., seasons, fields, subfield management zones, soil types, etc.). Each comparison center user interface preferably includes categories (e.g., soil / environment, planting, fertility, harvest, climate) of data available for a plurality of criteria (e.g., seasons, fields, subfield management zones, soil types, etc.).Each category can preferably be expanded by the operator (e.g., by clicking or tapping) in order to display detailed information that falls within that category. The “floor / environment” category can preferably be expanded. 238326 1647602 of 57 to display relevant data, preferably including soil type, plotting / plotting practices, and tillage practices. The “harvest” category can preferably be expanded to display relevant data, preferably including harvest start date, harvest completion date, harvesting practices, and harvesting equipment. The “sowing” category can preferably be expanded to display relevant information, preferably including sowing data, hybrid (e.g., seed type), population, population fitness rating (e.g., a numerical score indicating whether the sown population was appropriate for the field or management area), and sowing soil temperature.The “Fertility” category can preferably be expanded to display relevant data, including, ideally, accumulated precipitation (total and by month), spring frost rating, and heat stress during pollination. For each data category (e.g., Soil / Environment, Planting, Fertility, Harvest, Climate), the comparison center user interface preferably displays a comparative summary (e.g., “similar,” “different,” or a similarity score legend or numerical score) indicating the similarity of the data within the category for each criterion (e.g., season, field, soil type, subfield management zone). The comparative summary is preferably determined based on the aggregate similarity of the data in the category and can be determined by comparing the aggregate similarity to a similarity threshold.As an example, the category “planting” may include a comparative summary of “different” that can be determined by the operations of (a) assigning a numeric value to each data item in the category according to a predetermined association of numeric values ​​to data ranges (e.g., assigning a numeric value to cumulative precipitation data equivalent to inches of rain accumulated through seasons / seasons, assigning a value of 100 to a spring frost rating of “A”, assigning a value of 200 to heat stress during the. 238326 1647602 of 57 pollination rating “B”; (b) aggregating the determined numeric values ​​(for example, summarizing or averaging determined numeric values) to obtain an aggregate numeric value; (c) comparing the aggregate numeric value to a predetermined numeric similarity threshold (for example, 300); and (d) if the aggregate numeric value exceeds the numeric similarity threshold, selecting and displaying a comparative summary of “different”. Figure 10 illustrates an exemplary interface for the Comparison Center 1000 according to one embodiment. The Comparison Center 1000 user interface is displayed on a monitor (e.g., cab computer 115, display device, OEM display device, computer / computing device, etc.) in a tractor cab or machine, or the Comparison Center 1000 map is displayed on a user device (e.g., device 104, tablet device, computing device, desktop computer, cell phone, smart TV) that can be located anywhere, enabling the operator to make a farming decision for one or more fields. A Seasonal option 510 can be selected to display seasonal comparison data, or a Field option 512 can be selected to display field comparison data.A field region 514 includes a selectable option (for example, place of origin 520) to display comparative data for a particular field or farm. A season A region includes a selectable option 530 (for example, 2013) to display agricultural data for a particular year in a 2013 column. A season B region includes a selectable option 540 (for example, 2014) to display agricultural data for a particular year in a 2014 column. In this example, the 2013 and 2014 columns include average yield (for example, in bushels / acre), soil / environment, and planting conditions, including planting date, hybrid(s), hybrid fitness rating, population, population fitness rating, and planting soil temperature. The system or device determines the conditions of. 238326 The soil / environment conditions for the 2013 and 2014 seasons are similar, while the planting conditions for those seasons are different. The operator can then correlate and / or compare the yield of 225 Bu / Acre in the 2013 season with the planting conditions for that season. In contrast, the lower yield of 205 Bu / Acre in the 2014 season can be correlated with the planting conditions for that season. To optimize yields in future seasons, the operator may decide to use planting conditions similar to those of the 2013 season. Figure 11 illustrates an exemplary interface for the 1100 comparison center according to one embodiment. The 1100 comparison center user interface is displayed on a monitor (e.g., display device, OEM display device, computer device, etc.) in a tractor cab, or the 600 comparison map is displayed on a user device (e.g., tablet, computer, desktop computer, mobile phone, smart TV) located anywhere, enabling the operator to make an agricultural decision for one or more fields. A season option (610) can be selected to display comparative seasonal data, or a field option (612) can be selected to display comparative field data.A field region 614 includes a selectable field option 620 (e.g., place of origin) to display comparative data for a particular farm or field. A season A region includes a first selectable season option 630 (e.g., 2013) to display agricultural information for a particular year in a column (e.g., as illustrated, 2013). A season B region includes a second selectable season option 640 (e.g., 2014) to display agricultural data for a particular year in a column (e.g., as illustrated, 2014). In this example, the 2013 and 2014 columns include fertility data, crop data, and climate data, including accumulated precipitation and rainfall. 238326 1647602 of 57 monthly, spring frost rating, and heat stress during pollination. The system or device determines that fertility and harvesting conditions are similar for the 2013 and 2014 seasons, while the climatic conditions for the 2013 and 2014 seasons are different. The operator can then correlate and / or compare the yield of 225 Bu / Acre in the 2013 season with the climatic and planting conditions for the 2013 season. In contrast, the lower yield of 205 Bu / Acre in the 2014 season can be correlated with the climatic and planting conditions of the 2014 season. In some embodiments, the operations of the method(s) described herein may be altered, modified, combined, or eliminated. The methods in the embodiment of the present invention may be implemented with a device, apparatus, or data processing system as described herein. The device, apparatus, or data processing system may be a conventional general-purpose computer system, or special-purpose computers, designed or programmed to perform only one function, may be used. It should be understood that the foregoing description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those skilled in the art upon reading and understanding the foregoing description.Therefore, the scope of the invention must be determined with reference to the appended claims together with the full scope of the equivalents to which such claims entitle.

Claims

1. A computer-implemented method for monitoring operations in agricultural regions, characterized in that it comprises: receiving, by means of a processor, a real-time identification of one or more agricultural regions from a first device traveling within the one or more agricultural regions; transmitting data to a first graphical user interface (GUI) to be displayed to a user, the first GUI enabling the user to input information for an agricultural test for the one or more agricultural regions; receiving, by means of the processor, test information from the user; sending, by means of the processor, a command to vary a value of a specific agricultural variable in each of the one or more agricultural regions based on the received test information; and receiving, after transmission, current yield data for a crop yield in each of the one or more agricultural regions.to determine a correlation between the specific agricultural variable and crop yield over one or more agricultural regions based on the command and current yield data; to generate a recommendation to improve crop yield in one or more agricultural regions based on the correlation; to send data related to the recommendation to a second device to implement the recommendation in one or more agricultural regions or to a user device coupled to the second device, wherein the data is for a second GUI that displays information corresponding to the recommendation for one or more agricultural regions. Seven claims follow;