Patent Description:
Planters are used for planting seeds of crops (e.g., corn, soybeans) in a field. Some planters include a display monitor within a cab for displaying a coverage map that shows regions of the field that have been planted. The coverage map of the planter is generated based on planting data collected by the planter.

A combine harvester or combine is a machine that harvests crops. A coverage map of a combine displays regions of the field that have been harvested by that combine. A coverage map allows the operator of the combine know that a region of the field has already been harvested by the same combine. Yield data for a field can then be generated after harvesting the field. The yield data can be analyzed in order to potentially improve agricultural operations for a subsequent growing season.

<CIT> discloses a system and method for creating <NUM>-dimensional agricultural field scene maps comprising producing a pair of images using a stereo camera and creating a disparity images based on the pair of images, the disparity image being a <NUM>-dimensional representation of the stereo images. In particular, the stereo images are two overlapping images taken simultaneously and the disparity images can be used to analyze a variety of agricultural features.

In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural image data including images of at least one stage of crop development that are captured with at least one of an apparatus and a remote sensor moving through a field. At least one processing unit is coupled to the database. The at least one processing unit is configured to execute instructions to analyze the captured images, to determine relevant images that indicate a change in at least one condition of the crop development, and to generate a localized view map layer for viewing the field at the at least one stage of crop development based on at least the relevant captured images.

The present disclosure is illustrated by way of example, and not by way of limitation, in the FIG. s of the accompanying drawings and in which:.

Described herein are systems and methods for capturing images of a field and performing agricultural data analysis of the images. In one embodiment, a method includes moving at least one of an apparatus and a remote sensor through a field at a stage of crop development and capturing images of the field including a crop (e.g., corn, soybeans). The method further includes analyzing the captured images and determining relevant images that indicate a change in at least one condition of the crop development. The method further includes generating a localized view map layer for viewing the field at the stage of crop development based on at least the relevant captured images.

A computer system includes at least one processing unit that is configured to execute instructions to analyze the captured images, to determine relevant images that indicate a change in at least one condition of the crop development, and to generate a localized view map layer for viewing the field at the at least one stage of crop development based on at least the relevant captured images. A user can view the localized view map layer in order to have a better understanding of actual current field conditions for the selected region. The user can identify any potential issues and take corrective action or different action during a current growing season of the crop to improve crop yield for the current growing season of the crop.

In the following description, numerous details are set forth. It will be apparent, however, to one skilled in the art, that embodiments of the present disclosure may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present disclosure.

<FIG> illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate. In one embodiment, a user <NUM> owns, operates or possesses a field manager computing device <NUM> in a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more agricultural fields. The field manager computer device <NUM> is programmed or configured to provide field data <NUM> to an agricultural intelligence computer system <NUM> via one or more networks <NUM>.

Examples of field data <NUM> include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, tract number, field number, section, township, and/or range), (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, commodity price information (e.g., crop price, crop revenue), grain moisture, tillage practice, and previous growing season information (c) soil data (for example, type, composition, pH, organic matter (OM), cation exchange capacity (CEC)), (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population, input cost information (e.g., cost of seed)), and proprietary indices (e.g., ratio of seed population to a soil parameter), etc.) for the fields that are being monitored), (e) fertilizer data (for example, nutrient type (Nitrogen, Phosphorous, Potassium), application type, application date, amount, source, method, cost of nutrients), (f) pesticide data (for example, pesticide, herbicide, fungicide, other substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant, application date, amount, source, method), (g) irrigation data (for example, application date, amount, source, method), (h) weather data (for example, precipitation, rainfall rate, predicted rainfall, water runoff rate region, temperature, wind, forecast, pressure, visibility, clouds, heat index, dew point, humidity, snow depth, air quality, sunrise, sunset), (i) imagery data (for example, imagery and light spectrum information from an agricultural apparatus sensor, camera, computer, smartphone, tablet, unmanned aerial vehicle, drone, self-guided device, self-propelled device, planes or satellite), (j) scouting observations (photos, videos, free form notes, voice recordings, voice transcriptions, weather conditions (temperature, precipitation (current and over time), soil moisture, crop growth stage, wind velocity, relative humidity, dew point, black layer)), and (k) soil, seed, crop phenology, pest and disease reporting, and predictions sources and databases.

A data server computer <NUM> is communicatively coupled to agricultural intelligence computer system <NUM> and is programmed or configured to send external data <NUM> to agricultural intelligence computer system <NUM> via the network(s) <NUM>. The external data server computer <NUM> may be owned or operated by the same legal person or entity as the agricultural intelligence computer system <NUM>, or by a different person or entity such as a government agency, non-governmental organization (NGO), and/or a private data service provider. Examples of external data include weather data, imagery data, soil data, field conditions, or statistical data relating to crop yields, among others. External data <NUM> may consist of the same type of information as field data <NUM>. In some embodiments, the external data <NUM> is provided by an external data server <NUM> owned by the same entity that owns and/or operates the agricultural intelligence computer system <NUM>. For example, the agricultural intelligence computer system <NUM> may include a data server focused exclusively on a type of data that might otherwise be obtained from third party sources, such as weather data. In some embodiments, an external data server <NUM> may actually be incorporated within the system <NUM>.

An agricultural apparatus <NUM> may have one or more remote sensors <NUM> fixed thereon, which sensors are communicatively coupled either directly or indirectly via agricultural apparatus <NUM> to the agricultural intelligence computer system <NUM> and are programmed or configured to send sensor data to agricultural intelligence computer system <NUM>. Examples of agricultural apparatus <NUM> include tractors, combines, harvesters, planters, trucks, fertilizer equipment, unmanned aerial vehicles, drone, self-guided device, self-propelled device, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture. In some embodiments, a single unit of apparatus <NUM> may comprise a plurality of sensors <NUM> that are coupled locally in a network on the apparatus; controller area network (CAN) is example of such a network that can be installed in combines or harvesters. Application controller <NUM> is communicatively coupled to agricultural intelligence computer system <NUM> via the network(s) <NUM> and is programmed or configured to receive one or more scripts to control an operating parameter of an agricultural vehicle or implement from the agricultural intelligence computer system <NUM>. For instance, a controller area network (CAN) bus interface may be used to enable communications from the agricultural intelligence computer system <NUM> to the agricultural apparatus <NUM>, such as how the CLIMATE FIELDVIEW DRIVE, available from The Climate Corporation, San Francisco, California, is used. Sensor data may consist of the same type of information as field data <NUM>. In some embodiments, remote sensors <NUM> may not be fixed to an agricultural apparatus <NUM> but may be remotely located in the field and may communicate with network <NUM>.

The apparatus <NUM> may optionally comprise a cab computer <NUM> that is programmed with a cab application, which may comprise a version or variant of the mobile application for device <NUM> that is further described in other sections herein. In an embodiment, cab computer <NUM> comprises a compact computer, often a tablet-sized computer or smartphone, with a graphical screen display, such as a color display, that is mounted within an operator's cab of the apparatus <NUM>. Cab computer <NUM> may implement some or all of the operations and functions that are described further herein for the mobile computer device <NUM>.

The network(s) <NUM> broadly represent any combination of one or more data communication networks including local area networks, wide area networks, internetworks or internets, using any of wireline or wireless links, including terrestrial or satellite links. The network(s) may be implemented by any medium or mechanism that provides for the exchange of data between the various elements of <FIG>. The various elements of <FIG> may also have direct (wired or wireless) communications links. The sensors <NUM>, controller <NUM>, external data server computer <NUM>, and other elements of the system each comprise an interface compatible with the network(s) <NUM> and are programmed or configured to use standardized protocols for communication across the networks such as TCP/IP, Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS, and the like.

Agricultural intelligence computer system <NUM> is programmed or configured to receive agricultural data including field data <NUM> from field manager computing device <NUM>, external data <NUM> from external data server computer <NUM>, and sensor data from remote sensor <NUM>. Agricultural intelligence computer system <NUM> may be further configured to host, use or execute one or more computer programs, other software elements, digitally programmed logic such as FPGAs or ASICs, or any combination thereof to perform translation and storage of data values, construction of digital models of one or more crops on one or more fields, generation of recommendations and notifications, and generation and sending of scripts to application controller <NUM>, in the manner described further in other sections of this disclosure.

In an embodiment, agricultural intelligence computer system <NUM> is programmed with or comprises a communication layer <NUM>, instructions <NUM>, presentation layer <NUM>, data management layer <NUM>, hardware/virtualization layer <NUM>, and model and field data repository <NUM>. "Layer," in this context, refers to any combination of electronic digital interface circuits, microcontrollers, firmware such as drivers, and/or computer programs or other software elements.

Communication layer <NUM> may be programmed or configured to perform input/output interfacing functions including sending requests to field manager computing device <NUM>, external data server computer <NUM>, and remote sensor <NUM> for field data, external data, and sensor data respectively. Communication layer <NUM> may be programmed or configured to send the received data to model and field data repository <NUM> to be stored as field data <NUM>.

Presentation layer <NUM> may be programmed or configured to generate a graphical user interface (GUI) to be displayed on field manager computing device <NUM>, cab computer <NUM> or other computers that are coupled to the system <NUM> through the network <NUM>. The GUI may comprise controls for inputting data to be sent to agricultural intelligence computer system <NUM>, generating requests for models and/or recommendations, and/or displaying recommendations, notifications, models, and other field data.

Data management layer <NUM> may be programmed or configured to manage read operations and write operations involving the repository <NUM> 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 data management layer <NUM> include JDBC, SQL server interface code, and/or HADOOP interface code, among others. Repository <NUM> may comprise a database. As used herein, the term "database" may refer to either a body of data, a relational database management system (RDBMS), or to both. As used herein, a database may comprise any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, and any other structured collection of records or data that is stored in a computer system. Examples of RDBMS's include, but are not limited to including, ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, any database may be used that enables the systems and methods described herein.

When field data <NUM> is not provided directly to the agricultural intelligence computer system via one or more agricultural machines or agricultural machine devices that interacts with the agricultural intelligence computer system, the user may be prompted via one or more user interfaces on the user device (served by the agricultural intelligence computer system) to input such information. In an example embodiment, the user may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system) and selecting specific CLUs that have been graphically shown on the map. In an alternative embodiment, the user <NUM> may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system <NUM>) and drawing boundaries of the field over the map. Such CLU selection or map drawings represent geographic identifiers. In alternative embodiments, the user may specify identification data by accessing field identification data (provided as shape files or in a similar format) from the U. Department of Agriculture Farm Service Agency or other source via the user device and providing such field identification data to the agricultural intelligence computer system.

In an example embodiment, the agricultural intelligence computer system <NUM> is programmed to generate and cause displaying 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 may provide one or more graphical user interface widgets which when selected can identify changes to the field, soil, crops, tillage, or nutrient practices. The data manager may include a timeline view, a spreadsheet view, and/or one or more editable programs.

<FIG> depicts an example embodiment of a timeline view <NUM> for data entry. Using the display depicted in <FIG>, a user computer can input a selection of a particular field and a particular date for the addition of event. Events depicted at the top of the timeline may include Nitrogen, Planting, Practices, and Soil. To add a nitrogen application event, a user computer may provide input to select the nitrogen tab. The user computer may then select a location on the timeline for a particular field in order to indicate an application of nitrogen on the selected field. In response to receiving a selection of a location on the timeline for a particular field, the data manager may display a data entry overlay, allowing the user computer to input data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information relating to the particular field. For example, if a user computer selects a portion of the timeline and indicates an application of nitrogen, then the data entry overlay may include fields for inputting an amount of nitrogen applied, a date of application, a type of fertilizer used, and any other information related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creating one or more programs. "Program," in this context, refers to a set of data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information that may be related to one or more fields, and that can be stored in digital data storage for reuse as a set in other operations. After a program has been created, it may be conceptually applied to one or more fields and references to the program may be stored in digital storage in association with data identifying the fields. Thus, instead of manually entering identical data relating to the same nitrogen applications for multiple different fields, a user computer may create a program that indicates a particular application of nitrogen and then apply the program to multiple different fields. For example, in the timeline view of <FIG>, the top two timelines have the "Fall applied" program selected, which includes an application of <NUM> lbs N/ac in early April. The data manager may provide an interface for editing a program. In an embodiment, when a particular program is edited, each field that has selected the particular program is edited. For example, in <FIG>, if the "Fall applied" program is edited to reduce the application of nitrogen to <NUM> lbs N/ac, the top two fields may be updated with a reduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has a program selected, the data manager removes the correspondence of the field to the selected program. For example, if a nitrogen application is added to the top field in <FIG>, the interface may update to indicate that the "Fall applied" program is no longer being applied to the top field. While the nitrogen application in early April may remain, updates to the "Fall applied" program would not alter the April application of nitrogen.

<FIG> depicts an example embodiment of a spreadsheet view <NUM> for data entry. Using the display depicted in <FIG>, a user can create and edit information for one or more fields. The data manager may include spreadsheets for inputting information with respect to Nitrogen, Planting, Practices, and Soil as depicted in <FIG>. To edit a particular entry, a user computer may select the particular entry in the spreadsheet and update the values. For example, <FIG> depicts an in-progress update to a target yield value for the second field. Additionally, a user computer may select one or more fields in order to apply one or more programs. In response to receiving a selection of a program for a particular field, the data manager may automatically complete the entries for the particular field based on the selected program. As with the timeline view, the data manager may update the entries for each field associated with a particular program in response to receiving an update to the program. Additionally, the data manager may remove the correspondence of the selected program to the field in response to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field data repository <NUM>. Model data comprises data models created for one or more fields. For example, a crop model may include a digitally constructed model of the development of a crop on the one or more fields. "Model," in this context, refers to an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more stored output values that can serve as the basis of computer-implemented recommendations, output data displays, or machine control, among other things. Persons of skill in the field find it convenient to express models using mathematical equations, but that form of expression does not confine the models disclosed herein to abstract concepts; instead, each model herein has a practical application in a computer in the form of stored executable instructions and data that implement the model using the computer. The model data may include a model of past events on the one or more fields, a model of the current status of the one or more fields, and/or a model of predicted events on the one or more fields. Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data.

Hardware/virtualization layer <NUM> comprises one or more central processing units (CPUs), memory controllers, and other devices, components, or elements of a computer system such as volatile or non-volatile memory, non-volatile storage such as disk, and I/O devices or interfaces as illustrated and described, for example, in connection with <FIG>. The layer <NUM> also may comprise programmed instructions that are configured to support virtualization, containerization, or other technologies. In one example, instructions <NUM> include different types of instructions for monitoring field operations, capturing images of crop development and field operations, and performing agricultural data analysis based on the captured images. The instructions <NUM> may include agricultural data analysis instructions including instructions for performing the operations of the methods described herein. The instructions <NUM> can be included with the programmed instructions of the layer <NUM>.

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

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

In an embodiment, user <NUM> interacts with agricultural intelligence computer system <NUM> using field manager computing device <NUM> configured with an operating system and one or more application programs or apps; the field manager computing device <NUM> also may interoperate with the agricultural intelligence computer system independently and automatically under program control or logical control and direct user interaction is not always required. Field manager computing device <NUM> broadly represents one or more of a smart phone, PDA, tablet computing device, laptop computer, desktop computer, workstation, or any other computing device capable of transmitting and receiving information and performing the functions described herein. Field manager computing device <NUM> may communicate via a network using a mobile application stored on field manager computing device <NUM>, and in some embodiments, the device may be coupled using a cable <NUM> or connector to the sensor <NUM> and/or controller <NUM>. A particular user <NUM> may own, operate or possess and use, in connection with system <NUM>, more than one field manager computing device <NUM> at a time.

The mobile application may provide client-side functionality, via the network to one or more mobile computing devices. In an example embodiment, field manager computing device <NUM> may access the mobile application via a web browser or a local client application or app. Field manager computing device <NUM> may transmit data to, and receive data from, one or more front-end servers, using web-based protocols or formats such as HTTP, XML and/or JSON, or app-specific protocols. In an example embodiment, the data may take the form of requests and user information input, such as field data, into the mobile computing device. In some embodiments, the mobile application interacts with location tracking hardware and software on field manager computing device <NUM> which determines the location of field manager computing device <NUM> using standard tracking techniques such as multilateration of radio signals, the global positioning system (GPS), WiFi positioning systems, or other methods of mobile positioning. In some cases, location data or other data associated with the device <NUM>, user <NUM>, and/or user account(s) may be obtained by queries to an operating system of the device or by requesting an app on the device to obtain data from the operating system.

In an embodiment, field manager computing device <NUM> sends field data <NUM> to agricultural intelligence computer system <NUM> comprising or including, but not limited to, data values representing one or more of: a geographical location of the one or more fields, tillage information for the one or more fields, crops planted in the one or more fields, and soil data extracted from the one or more fields. Field manager computing device <NUM> may send field data <NUM> in response to user input from user <NUM> specifying the data values for the one or more fields. Additionally, field manager computing device <NUM> may automatically send field data <NUM> when one or more of the data values becomes available to field manager computing device <NUM>. For example, field manager computing device <NUM> may be communicatively coupled to remote sensor <NUM> and/or application controller <NUM>. In response to receiving data indicating that application controller <NUM> released water onto the one or more fields, field manager computing device <NUM> may send field data <NUM> to agricultural intelligence computer system <NUM> indicating that water was released on the one or more fields. Field data <NUM> identified in this disclosure may be input and communicated using electronic digital data that is communicated 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, commercially available from The Climate Corporation, San Francisco, California. The CLIMATE FIELDVIEW application, or other applications, may be modified, extended, or adapted to include features, functions, and programming that have not been disclosed earlier than the filing date of this disclosure. In one embodiment, the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare. The combinations and comparisons may be performed in real time and are based upon scientific models that provide potential scenarios to permit the grower to make better, more informed decisions.

<FIG> illustrates two views of an example logical organization of sets of instructions in main memory when an example mobile application is loaded for execution. In <FIG>, each named element represents a region of one or more pages of RAM or other main memory, or one or more blocks of disk storage or other non-volatile storage, and the programmed instructions within those regions. In one embodiment, in view (a), a mobile computer application <NUM> comprises account-fields-data ingestion-sharing instructions <NUM>, overview and alert instructions <NUM>, digital map book instructions <NUM>, seeds and planting instructions <NUM>, nitrogen instructions <NUM>, weather instructions <NUM>, field health instructions <NUM>, and performance instructions <NUM>.

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

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

In one embodiment, script generation instructions <NUM> are programmed to provide an interface for generating scripts, including variable rate (VR) fertility scripts. The interface enables growers to create scripts for field implements, such as nutrient applications, planting, and irrigation. For example, a planting script interface may comprise tools for identifying a type of seed for planting. Upon receiving a selection of the seed type, mobile computer application <NUM> may display one or more fields broken into management zones, such as the field map data layers created as part of digital map book instructions <NUM>. In one embodiment, the management zones comprise soil zones along with a panel identifying each soil zone and a soil name, texture, drainage for each zone, or other field data. Mobile computer application <NUM> may also display tools for editing or creating such, such as graphical tools for drawing management zones, such as soil zones, over a map of one or more fields. Planting procedures may be applied to all management zones or different planting procedures may be applied to different subsets of management zones. When a script is created, mobile computer application <NUM> may make the script available for download in a format readable by an application controller, such as an archived or compressed format. Additionally and/or alternatively, a script may be sent directly to cab computer <NUM> from mobile computer application <NUM> and/or uploaded to one or more data servers and stored for further use. In one embodiment, nitrogen instructions <NUM> are programmed to provide tools to inform nitrogen decisions by visualizing the availability of nitrogen to crops. This enables growers to maximize yield or return on investment through optimized nitrogen application during the season. Example programmed functions include displaying images such as SSURGO images to enable drawing of application zones and/or images generated from subfield soil data, such as data obtained from sensors, at a high spatial resolution (as fine as <NUM> meters or smaller because of their proximity to the soil); upload of existing grower-defined zones; providing an application graph and/or a map to enable tuning application(s) of nitrogen across multiple zones; output of scripts to drive machinery; tools for mass data entry and adjustment; and/or maps for data visualization, among others. "Mass data entry," in this context, may mean entering data once and then applying the same data to multiple fields that have been defined in the system; example data may include nitrogen application data that is the same for many fields of the same grower, but such mass data entry applies to the entry of any type of field data into the mobile computer application <NUM>. For example, nitrogen instructions <NUM> may be programmed to accept definitions of nitrogen planting and practices programs and to accept user input specifying to apply those programs across multiple fields. "Nitrogen planting programs," in this context, refers to a stored, named set of data that associates: a name, color code or other identifier, one or more dates of application, types of material or product for each of the dates and amounts, method of application or incorporation such as injected or knifed in, and/or amounts or rates of application for each of the dates, crop or hybrid that is the subject of the application, among others. "Nitrogen practices programs," in this context, refers to a stored, named set of data that associates: a practices name; a previous crop; a tillage system; a date of primarily tillage; one or more previous tillage systems that were used; one or more indicators of application type, such as manure, that were used. Nitrogen instructions <NUM> also may be programmed to generate and cause displaying a nitrogen graph, which indicates projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted; in some embodiments, different color indicators may signal a magnitude of surplus or magnitude of shortfall. In one embodiment, a nitrogen graph comprises a graphical display in a computer display device comprising a plurality of rows, each row associated with and identifying a field; data specifying what crop is planted in the field, the field size, the field location, and a graphic representation of the field perimeter; in each row, a timeline by month with graphic indicators specifying each nitrogen application and amount at points correlated to month names; and numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude.

In one embodiment, the nitrogen graph may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen graph. The user may then use his optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. Nitrogen instructions <NUM> also may be programmed to generate and cause displaying a nitrogen map, which indicates projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted; in some embodiments, different color indicators may signal a magnitude of surplus or magnitude of shortfall. The nitrogen map may display projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted for different times in the past and the future (such as daily, weekly, monthly or yearly) using numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude. In one embodiment, the nitrogen map may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen map, such as to obtain a preferred amount of surplus to shortfall. The user may then use his optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts. In other embodiments, similar instructions to the nitrogen instructions <NUM> could be used for application of other nutrients (such as phosphorus and potassium) application of pesticide, and irrigation programs.

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

In one embodiment, field health instructions <NUM> are programmed to provide timely remote sensing images highlighting in-season crop variation for at least one stage of crop development and potential concerns. Example programmed functions include cloud checking, to identify possible clouds or cloud shadows; determining nitrogen indices based on field images; graphical visualization of scouting layers, including, for example, those related to field health, and viewing and/or sharing of scouting notes; and/or downloading satellite images from multiple sources and prioritizing the images for the grower, among others.

In one embodiment, performance instructions <NUM> are programmed to provide reports, analysis, and insight tools using on-farm data for evaluation, insights and decisions. This enables the grower to seek improved outcomes for the next year through fact-based conclusions about why return on investment was at prior levels, and insight into yield-limiting factors. The performance instructions <NUM> may be programmed to communicate via the network(s) <NUM> to back-end analytics programs executed at agricultural intelligence computer system <NUM> and/or external data server computer <NUM> and configured to analyze metrics such as yield, hybrid, population, SSURGO, soil tests, or elevation, among others. Programmed reports and analysis may include correlations between yield and another parameter or variable of agricultural data, yield variability analysis, benchmarking of yield and other metrics against other growers based on anonymized data collected from many growers, or data for seeds and planting, among others.

Applications having instructions configured in this way may be implemented for different computing device platforms while retaining the same general user interface appearance. For example, the mobile application may be programmed for execution on tablets, smartphones, or server computers that are accessed using browsers at client computers. Further, the mobile application as configured for tablet computers or smartphones may provide a full app experience or a cab app experience that is suitable for the display and processing capabilities of cab computer <NUM>. For example, referring now to view (b) of <FIG>, in one embodiment a cab computer application <NUM> may comprise maps-cab instructions <NUM>, remote view instructions <NUM>, data collect and transfer instructions <NUM>, machine alerts instructions <NUM>, script transfer instructions <NUM>, and scouting-cab instructions <NUM>. The code base for the instructions of view (b) may be the same as for view (a) and executables implementing the code may be programmed to detect the type of platform on which they are executing and to expose, through a graphical user interface, only those functions that are appropriate to a cab platform or full platform. This approach enables the system to recognize the distinctly different user experience that is appropriate for an in-cab environment and the different technology environment of the cab. The maps-cab instructions <NUM> may be programmed to provide map views of fields, farms or regions that are useful in directing machine operation. The remote view instructions <NUM> may be programmed to turn on, manage, and provide views of machine activity in real-time or near real-time to other computing devices connected to the system <NUM> via wireless networks, wired connectors or adapters, and the like. The data collect and transfer instructions <NUM> may be programmed to turn on, manage, and provide transfer of data collected at machine sensors and controllers to the system <NUM> via wireless networks, wired connectors or adapters, and the like. The machine alerts instructions <NUM> may be programmed to detect issues with operations of the machine or tools that are associated with the cab and generate operator alerts. The script transfer instructions <NUM> may be configured to transfer in scripts of instructions that are configured to direct machine operations or the collection of data. The scouting-cab instructions <NUM> may be programmed to display location-based alerts and information received from the system <NUM> based on the location of the agricultural apparatus <NUM> or sensors <NUM> in the field and ingest, manage, and provide transfer of location-based scouting observations to the system <NUM> based on the location of the agricultural apparatus <NUM> or sensors <NUM> in the field.

In an embodiment, external data server computer <NUM> stores external data <NUM>, including soil data representing soil composition for the one or more fields and weather data representing temperature and precipitation on the one or more fields. The weather data may include past and present weather data as well as forecasts for future weather data. In an embodiment, external data server computer <NUM> comprises a plurality of servers hosted by different entities. For example, a first server may contain soil composition data while a second server may include weather data. Additionally, soil composition data may be stored in multiple servers. For example, one server may store data representing percentage of sand, silt, and clay in the soil while a second server may store data representing percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor <NUM> comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor <NUM> may be aerial sensors, such as satellites, vehicle sensors, image sensors (e.g., image capturing device for capturing images of crops or soil conditions), planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields. In an embodiment, application controller <NUM> is programmed or configured to receive instructions from agricultural intelligence computer system <NUM>. Application controller <NUM> may also be programmed or configured to control an operating parameter of an agricultural vehicle or implement. For example, an application controller may be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, 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.

The system <NUM> may obtain or ingest data under user <NUM> control, on a mass basis from a large number of growers who have contributed data to a shared database system. This form of obtaining data may be termed "manual data ingest" as one or more user-controlled computer operations are requested or triggered to obtain data for use by the system <NUM>. As an example, the CLIMATE FIELDVIEW application, commercially available from The Climate Corporation, San Francisco, California, may be operated to export data to system <NUM> for storing in the repository <NUM>.

For example, seed monitor systems can both control planter apparatus components and obtain planting data, including signals from seed sensors via a signal harness that comprises a CAN backbone and point-to-point connections for registration and/or diagnostics. Seed monitor systems can be programmed or configured to display seed spacing, population and other information to the user via the cab computer <NUM> or other devices within the system <NUM>. Examples are disclosed in <CIT> and <CIT>, and the present disclosure assumes knowledge of those other patent disclosures.

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

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

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

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

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

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

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

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

In an embodiment, examples of sensors <NUM> and controllers <NUM> may be installed in unmanned aerial vehicle (UAV) apparatus or "drones. " Such sensors may include image capturing devices or cameras with detectors effective for any range of the electromagnetic spectrum including visible light, infrared, ultraviolet, near-infrared (NIR), and the like; accelerometers; altimeters; temperature sensors; humidity sensors; pitot tube sensors or other airspeed or wind velocity sensors; battery life sensors; or radar emitters and reflected radar energy detection apparatus. Such controllers may include guidance or motor control apparatus, control surface controllers, camera controllers, or controllers programmed to turn on, operate, obtain data from, manage and configure any of the foregoing sensors. Examples are disclosed in <CIT> and the present disclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors <NUM> and controllers <NUM> may be affixed to soil sampling and measurement apparatus that is configured or programmed to sample soil and perform soil chemistry tests, soil moisture tests, and other tests pertaining to soil. For example, the apparatus disclosed in <CIT> and <CIT> may be used, and the present disclosure assumes knowledge of those patent disclosures.

In another embodiment, sensors <NUM> and controllers <NUM> may comprise weather devices for monitoring weather conditions of fields. For example, the apparatus disclosed in International Pat. Application No. <CIT> may be used, and the present disclosure assumes knowledge of those patent disclosures.

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

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

<FIG> illustrates a programmed process by which the agricultural intelligence computer system generates one or more preconfigured agronomic models using field data provided by one or more data sources. <FIG> may serve as an algorithm or instructions for programming the functional elements of the agricultural intelligence computer system <NUM> to perform the operations that are now described.

At block <NUM>, the agricultural intelligence computer system <NUM> is configured or programmed to implement agronomic data preprocessing of field data received from one or more data sources. The field data received from one or more data sources may be preprocessed for the purpose of removing noise and distorting effects within the agronomic data including measured outliers that would bias received field data values. Embodiments of agronomic data preprocessing may include, but are not limited to, removing data values commonly associated with outlier data values, specific measured data points that are known to unnecessarily skew other data values, data smoothing techniques used to remove or reduce additive or multiplicative effects from noise, and other filtering or data derivation techniques used to provide clear distinctions between positive and negative data inputs.

At block <NUM>, the agricultural intelligence computer system <NUM> is configured or programmed to perform data subset selection using the preprocessed field data in order to identify datasets useful for initial agronomic model generation. The agricultural intelligence computer system <NUM> may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method. For example, a genetic algorithm selection technique uses an adaptive heuristic search algorithm, based on evolutionary principles of natural selection and genetics, to determine and evaluate datasets within the preprocessed agronomic data.

At block <NUM>, the agricultural intelligence computer system <NUM> is configured or programmed to implement field dataset evaluation. In an embodiment, a specific field dataset is evaluated by creating an agronomic model and using specific quality thresholds for the created agronomic model. Agronomic models may be compared using cross validation techniques including, but not limited to, root mean square error of leave-one-out cross validation (RMSECV), mean absolute error, and mean percentage error. For example, RMSECV can cross validate agronomic models by comparing predicted agronomic property values created by the agronomic model against historical agronomic property values collected and analyzed. In an embodiment, the agronomic dataset evaluation logic is used as a feedback loop where agronomic datasets that do not meet configured quality thresholds are used during future data subset selection steps (block <NUM>).

At block <NUM>, the agricultural intelligence computer system <NUM> is configured or programmed to implement agronomic model creation based upon the cross validated agronomic datasets. In an embodiment, agronomic model creation may implement multivariate regression techniques to create preconfigured agronomic data models.

At block <NUM>, the agricultural intelligence computer system <NUM> is configured or programmed to store the preconfigured agronomic data models for future field data evaluation.

<FIG> illustrates a flow diagram of one embodiment for a method <NUM> of capturing images and creating a local view map layer for an application pass of an agricultural crop. The method <NUM> is performed by processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine or a device), or a combination of both. In one embodiment, the method <NUM> is performed by processing logic of at least one computer system (e.g., computer system <NUM>, computer system <NUM>, field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, remote sensor <NUM>, drone, self-guided device, self-propelled device, etc). The computer system executes instructions of a software application or program with processing logic. The software application or program can be initiated by the computer system, an apparatus, or remote sensor. In one example, a computer system, a field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>, remote sensor <NUM>, drone, self-guided device, or self-propelled device performs some or all of the operations of the method <NUM>. In another example, a computer system <NUM> in combination with the field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>, remote sensor <NUM>, drone, self-guided device, or self-propelled device perform the operations of the method <NUM>.

At block <NUM>, at least one of an apparatus (e.g., field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>) and a remote sensor (e.g., remote sensor <NUM>, image sensor, image capturing device, drone, self-guided device, self-propelled device, etc) move through a field to capture images of the field including crops if visible. An initiated software application may control operations of an image capturing device of the apparatus or remote sensor. The remote sensor may be integrated with or coupled to the apparatus (e.g., agricultural apparatus <NUM>) that performs an application pass (e.g., planting, tillage, fertilization). The source of images during any pass could be a drone with a camera that is instructed to track (e.g., lead or follow) the machine (e.g., agricultural apparatus <NUM>) making the field pass and capture images of standing crop in front of the machine, processed crop (e.g., corn ears) entering the machine, or soil and crop residue in soil over which the machine has already traveled. In another example, a user walks through a field and captures images with a mobile device or tablet device having an image capture device (e.g., camera) and the software application. In another example, a user guides an apparatus (e.g., apparatus with wheels and support frame for positioning image capture devices) having at least one image capture device (e.g., remote sensor <NUM>) through a field for capturing images. In another example, a self-guided or self-propelled device moves through a field for capturing images with the software application. The software application controls whether images are captured continuously or during time periods of more stable movement as opposed to unstable movement.

At block <NUM>, a computer system (e.g., computer system <NUM>, computer system <NUM>, field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, remote sensor <NUM>, drone, self-guided device, self-propelled device, etc) analyzes the captured images and determines relevant images that indicate a change in at least one condition of the crop development (e.g., images relevant for further analysis, images showing a change in crop development, change in ear potential, change in yield, change in weed coverage, images showing a lower predicted yield, etc.). At block <NUM>, the computer system generates a localized view map layer (e.g., image based map layer, time-lapse video, <NUM> degree view) for viewing the field (e.g., at a particular stage of crop development, during the application pass) based on at least the relevant captured images (or a subset of all captured images). An image based map layer may comprise a map layer of image capture locations (e.g., locations along a travel path of a vehicle or implement traversing the field. ) In this manner, fewer images and more relevant images may be saved to reduce memory resources needed for saving these images and a localized view map layer.

At block <NUM>, the computer system generates and causes a graphical user interface to display yield data including a yield map in response to a user input. At block <NUM>, the computer system receives a user selection of a region of the yield map. At block <NUM>, the computer system generates and causes a graphical user interface to display a localized view map layer that is geographically associated with the selected region of the field map in response to the user selection. The localized view map layer may be superimposed with a second map layer (e.g., an agronomic information layer such as a yield map, a planting population map, a seed spacing map, a planting downforce map, or a field health map such as an NDVI map). The user can then identify a region of interest in the second map layer and select the region of interest in the superimposed localized view map layer in order to view images and/or video captured for the region of interest and have a better understanding of actual field conditions for the selected region. If the selected region has lower yield than other regions, then a user may be able to identify any issues (e.g., weed coverage, shorter crops in comparison to crops in other regions, smaller ear size for corn, crops with fewer leaves in comparison to crops in other regions) that cause the lower yield. If the selected region has higher yield than other regions, then a user may be able to identify certain crop characteristics or parameters (e.g., lack of weed coverage, taller crops in comparison to crops in other regions, larger ear size for corn, crops with more leaves in comparison to crops in other regions) that cause the higher yield.

In another embodiment, blocks <NUM> and <NUM> are optional. A user may want to view the view map layer early in a growing season or prior to yield data being available. In this case, at block <NUM>, the computer system identifies regions that are predicted to have a higher yield potential (e.g., higher corn ear potential) and regions predicted to have a lower yield potential (e.g., lower corn ear potential). Due to the predictions of regions with higher and lower yield potential, a user may be able to take action to increase yield in the lower yield potential regions. For example, a user can increase or decrease fertilization, spraying, etc. as appropriate. A user can also remove crops that are predicted to have a lower yield potential and this may increase yield for neighboring crops.

<FIG> illustrates a flow diagram of one embodiment for a method <NUM> of capturing images and analysis for an application pass of an agricultural crop. The method <NUM> is performed by processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine or a device), or a combination of both. In one embodiment, the method <NUM> is performed by processing logic of at least one computer system (e.g., computer system <NUM>, computer system <NUM>, field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, remote sensor <NUM>, drone, self-guided device, self-propelled device, etc). The computer system executes instructions of a software application or program with processing logic. The software application or program can be initiated by the computer system, an apparatus, or remote sensor. In one example, a computer system, a field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>, remote sensor <NUM>, drone, self-guided device, or self-propelled device performs some or all of the operations of the method <NUM>. In another example, a computer system <NUM> in combination with the field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>, remote sensor <NUM>, drone, self-guided device, or self-propelled device perform the operations of the method <NUM>.

At block <NUM>, an apparatus (e.g., apparatus <NUM>, vehicle, planter, tractor, combine, sprayer, implement, etc.) performs an application pass (e.g., planting, tillage, fertilization, etc.) for a field and at the same time captures images of the field including crops if visible during the application pass. Alternatively, a remote sensor (e.g., remote sensor <NUM>, drone, image capture device) associated with the apparatus captures images during the application pass. The source of images during any pass could be a drone with a camera that is instructed to track (e.g., lead or follow) the machine making the field pass.

At block <NUM>, the apparatus (or computer system in communication with the apparatus) generates a localized view (e.g., image based map layer, time-lapse video, <NUM> degree view) for viewing the field during the application pass based on the captured images. At block <NUM>, the apparatus (or computer system in communication with the apparatus) automatically analyzes (e.g., planting analysis, fertilizer analysis, harvesting analysis, tillage analysis) the images captured during the application pass. The apparatus (or computer system in communication with the apparatus) automatically performs the analysis in real-time during the application pass or alternatively if necessary communicates with an agricultural system for analysis of the images. At block <NUM>, the apparatus (or computer system in communication with the apparatus) adjusts settings of the application pass if appropriate based on the analysis of the images captured during the application pass.

For example, in a planting application pass, the planting analysis may include determining current field conditions (e.g., wet soil, dry soil, weather conditions, etc.) from the captured images and this analysis may cause an adjustment to parameters (e.g., speed of the planter, downforce, etc.) of the planter during the planting pass. In another example, in a fertilizer application pass, a remote sensor (e.g., drone camera) could lead the apparatus, machine, or implement, gather images of the plants ahead of the apparatus, machine or implement, determine a crop health criterion (e.g., crop growth stage, percentage or amount of weed cover) based on the images as part of the fertilizer analysis at block <NUM>, and then adjust settings automatically at block <NUM> or by transmitting the criterion to the apparatus, machine or implement which could adjust an application rate for the fertilizer based on the crop health criterion. In one example, a lower amount of weed cover in a certain region or strip of a field would result in less fertilizer being applied in this certain region or strip of the field. Conversely, a higher amount of weed cover in a certain region or strip of a field would result in more fertilizer being applied in this certain region or strip of the field.

In another example, in a harvesting application pass, the analysis at block <NUM> includes identifying crop components (e.g., corn ears) in a crop processing device (e.g., corn head) and identify size and health of the crop component (e.g., corn ears). The analysis may also include determining a delay between chopping a crop stalk (e.g., corn stalk) and identification of a crop component (e.g., corn ear). A device or structure could be added to a header of a combine to orient crop components (e.g., corn ears) in a proper position for an image capture device of the combine. A light source (e.g., halogen lamp, infrared LED) may be installed to the external portion of the harvesting equipment (e.g., to the combine head) or to an interior region of the harvesting equipment (e.g., in the feeder house) in order to illuminate the crops about to be harvested and/or the harvested crop components (e.g., corn ears) for better analysis. Analysis of the stalks or ears would then be used for adjusting settings of the combine at block <NUM>.

In another example, in a tillage application pass, the analysis at block <NUM> includes residue analysis of soil after the tillage pass. The analysis can be used for adjusting settings of the apparatus or implement during the tillage application pass.

<FIG> illustrates a flow diagram of one embodiment for a method <NUM> of capturing images of an agricultural crop in a field and determines crop information of the agricultural crop in the field. The method <NUM> is performed by processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine or a device), or a combination of both. In one embodiment, the method <NUM> is performed by processing logic of at least one computer system (e.g., computer system <NUM>, computer system <NUM>, field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>, remote sensor <NUM>, drone, self-guided device, self-propelled device, etc). The computer system executes instructions of a software application or program with processing logic. The software application or program can be initiated by the computer system, an apparatus, or remote sensor. In one example, a computer system, a field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, remote sensor <NUM>, drone, self-guided device, or self-propelled device performs some or all of the operations of the method <NUM>. In another example, a computer system <NUM> in combination with the field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>, remote sensor <NUM>, drone, self-guided device, or self-propelled device perform the operations of the method <NUM>.

At block <NUM>, at least one of an apparatus (e.g., field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>) and a remote sensor (e.g., remote sensor <NUM>, image sensor, image capturing device, drone, self-guided device, self-propelled device, etc) move through a field to capture images of the field including crops if visible. An initiated software application (e.g., image capture software application, field application) may control operations of an image capturing device or may control operations of multiple image capturing devices that are associated with at least one of the apparatus and the remote sensor. In one example, two rows of a crop are captured by the images. The remote sensor may be integrated with or coupled to the apparatus (e.g., agricultural apparatus <NUM>) that performs an application pass (e.g., planting, tillage, fertilization) or moves through the field.

The source of images during any pass could be a remote sensor (e.g., drone with a camera) that is instructed to track (e.g., lead or follow) the apparatus making the field pass. In another example, a user walks through a field and captures images with a mobile device or tablet device having an image capture device (e.g., camera) and the software application. In another example, a user guides an apparatus (e.g., apparatus with wheels and support frame for positioning image capture devices) having at least one image capture device through a field for capturing images. In another example, a self-guided or self-propelled device or robot moves through a field for capturing images with the software application. The software application controls whether images are captured continuously or during time periods of more stable movement as opposed to unstable movement.

At block <NUM>, a computer system (e.g., computer system <NUM>, computer system <NUM>, field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, remote sensor <NUM> (e.g., image sensor, image capturing device), drone, self-guided device, self-propelled device, etc) determines location information (e.g., GPS data), orientation information (e.g., gyroscope, accelerometer), time information (e.g., time of day, day, position of sun), and crop row information (e.g., crop spacing between rows) and associates this information with each captured image. In one example, crop row spacing for corn is set at a fixed spacing (e.g., <NUM> inches) which may be input by the user and can act as a reference for determining characteristics of a crop. At block <NUM>, the computer system analyzes the captured images and determines relevant images (e.g., images relevant for further analysis) for saving. At block <NUM>, the computer system generates a localized view map layer (e.g., image based map layer, time-lapse video, <NUM> degree view) for viewing the field (e.g., at a particular stage of crop development, during the application pass) based on at least the relevant captured images (or a subset of all captured images). In this manner, fewer images and more relevant images may be saved to reduce memory resources needed for saving these images and a localized view map layer. At block <NUM>, the computer system generates and causes a graphical user interface to display yield data including a yield map in response to a user input. At block <NUM>, the computer system receives a user selection of a region of the yield map. At block <NUM>, the computer system generates and causes the graphical user interface to display a localized view map layer that is geographically associated with the selected region of the field map in response to the user selection. The localized view map layer may be superimposed with the yield map. The user can view the localized view map layer in order to have a better understanding of actual field conditions for the selected region. If the selected region has lower yield than other regions, then a user may be able to identify any issues (e.g., weed coverage) that cause the lower yield or identify crop characteristics or parameters (shorter crops in comparison to crops in other regions, smaller ear size for corn, crops with fewer leaves in comparison to crops in other regions) that correlate with the lower yield. If the selected region has higher yield than other regions, then a user may be able to identify any issues (e.g., lack of weed coverage) that cause the higher yield or identify certain crop characteristics or parameters (e.g., lack of weed coverage, taller crops in comparison to crops in other regions, larger ear size for corn, crops with more leaves in comparison to crops in other regions) that correlate with the higher yield.

<FIG> illustrates a flow diagram of one embodiment for a method <NUM> of determining characteristics of an agricultural crop (e.g., corn) in a field based on capturing images of the crop in the field. The method <NUM> is performed by processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine or a device), or a combination of both. In one embodiment, the method <NUM> is performed by processing logic of at least one computer system (e.g., computer system <NUM>, computer system <NUM>, field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, remote sensor <NUM>, drone, self-guided device, self-propelled device, etc). The computer system executes instructions of a software application or program with processing logic. The software application or program can be initiated by the computer system, an apparatus, or remote sensor. In one example, a computer system, a field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>, remote sensor <NUM>, drone, self-guided device, or self-propelled device performs some or all of the operations of the method <NUM>. In another example, a computer system <NUM> in combination with the field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>, remote sensor <NUM>, drone, self-guided device, or self-propelled device perform the operations of the method <NUM>.

At block <NUM>, at least one of an apparatus (e.g., field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, apparatus <NUM>) and a remote sensor (e.g., remote sensor <NUM>, image sensor, image capturing device, drone, self-guided device, self-propelled device, etc) move through a field to capture images of the field including crops if visible. An initiated software application (e.g., image capture software application, field application) may control operations of an image capturing device or may control operations of multiple image capturing devices that are associated with at least one of the apparatus and the remote sensor. In one example, two rows of a crop are captured by the images. The images can be captured from different viewpoints (e.g., top view image above a crop, side view image from side of a crop). An initiated software application may control operations of an image capturing device of the apparatus or remote sensor. The remote sensor may be integrated with or coupled to the apparatus (e.g., agricultural apparatus <NUM>) that performs an application pass (e.g., planting, tillage, fertilization). The source of images during any pass could be a remote sensor (e.g., drone with a camera) that is instructed to track (e.g., lead or follow) the machine making the field pass. In another example, a user walks through a field and captures images with a mobile device or tablet device having an image capture device (e.g., camera) and the software application. In another example, a user guides an apparatus (e.g., apparatus with wheels and support frame for positioning image capture devices) having at least one image capture device through a field for capturing images. In another example, a self-guided or self-propelled device or robot moves through a field for capturing images with the software application. The software application controls whether images are captured continuously or during time periods of more stable movement as opposed to unstable movement.

At block <NUM>, a computer system (e.g., computer system <NUM>, computer system <NUM>, field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, remote sensor <NUM> (e.g., image sensor, image capturing device), drone, self-guided device, self-propelled device, etc) determines location information (e.g., GPS data), orientation information (e.g., gyroscope provides orientation of apparatus or remote sensor with respect to a reference, accelerometer provides orientation of apparatus or remote sensor with respect to a reference), time information (e.g., time of day, day, position of sun), shadow information (e.g., shadow regions as extracted from images), and crop row information (e.g., crop spacing between rows) and associates this information with each captured image. In one example, crop row spacing for corn is set at a fixed spacing (e.g., <NUM> inches) which may be input by the user and can act as a reference for determining characteristics of a crop. At block <NUM>, the computer system analyzes the captured images and the information associated with the images for determining a plant area (e.g., green plant area), a shadow area, and different characteristics of the crop including location of the stalk or stem in the field, a relative size of a crop in comparison to neighboring crops to determine a relative growth stage (e.g., ear potential of corn plant), leaf size, leaf length, number of leaves for each stalk or stem, and a location of a top of a stalk or stem (e.g., sworl for corn). The plant area and shadow area may be used in determining the different characteristics of the crop. At block <NUM>, the computer system generates and causes a graphical user interface to display different characteristics of the crop. If the crop characteristics (e.g., ear potential) indicates or predicts a lower yield than other regions, then a user may be able to identify any issues (e.g., weed coverage, shorter crops in comparison to crops in other regions, smaller ear size for corn, crops with fewer leaves in comparison to crops in other regions) that cause the lower yield and take a corrective action (e.g., fertilization application, spraying application) or different action (e.g., replant). If the selected region has higher yield than other regions, then a user may be able to identify certain crop characteristics or parameters (e.g., lack of weed coverage, taller crops in comparison to crops in other regions, larger ear size for corn, crops with more leaves in comparison to crops in other regions) that cause the higher yield.

The computer system may be integrated with or coupled to an apparatus that performs an application pass (e.g., planting, tillage, fertilization). Alternatively, the computer system may be integrated with a remote sensor (e.g., drone, image capture device) associated with the apparatus that captures images during the application pass.

In some embodiments, the operations of the method(s) disclosed herein can be altered, modified, combined, or deleted. The methods in embodiments of the present disclosure may be performed with a device, an apparatus, or computer system as described herein. The device, apparatus, or computer system may be a conventional, general-purpose computer system or special purpose computers, which are designed or programmed to perform only one function, may also be used.

<FIG> illustrates a diagram <NUM> for capturing images of a crop from multiple view points in accordance with one embodiment. Images are captured with processing logic that may comprise hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine or a device), or a combination of both. In one embodiment, the processing logic of at least one computer system (e.g., computer system <NUM>, computer system <NUM>, field manager computing device <NUM>, cab computer <NUM>, application controller <NUM>, remote sensor <NUM> (e.g., image sensor, image capturing device), drone, self-guided device, robot, self-propelled device, etc) captures images while moving along a direction <NUM> from that is substantially parallel with a row of a crop having stalks <NUM>-<NUM>. The computer system executes instructions of a software application or program with processing logic. The software application or program can be initiated by the computer system.

An initiated software application (e.g., image capture software application, field application) of the computer system captures images of the field including stalks <NUM>-<NUM>. In one example, images can be captured from different viewpoints (e.g., for each stalk of a row <NUM>). For the stalk <NUM>, one or more images are captured at each view point <NUM>, <NUM>, and <NUM>. Images captured at each view point have a respective angle of view <NUM>, <NUM>, and <NUM>. In one example, each angle of view is approximately <NUM>-<NUM> degrees (e.g., <NUM>-<NUM> degrees). An image of the stalk <NUM> that is captured at view point <NUM> will capture a larger portion of the stalk <NUM> than images of the stalk <NUM> that are captured at view points <NUM> and <NUM> because the view point <NUM> captures a larger portion (or all) of the stalk <NUM>. The stalk <NUM> is approximately centered within the angle of view <NUM>. A line drawn from the view point <NUM> to a center of the stalk <NUM> is approximately perpendicular with respect to the path <NUM>. The stalk width measured using such methods may be used, for example, to estimate a growth stage, relative growth stage, yield potential or ear potential for each plant.

A high frequency of images are captured from different side view points in order to obtain an estimate of stalk orientation (e.g., along an oblong length (e.g., major axis <NUM>) of the stalk, along a shorter length (e.g., minor axis <NUM>)) and dimensions along major and minor axes of the cross-sectional area of a stalk. In one example, the stalk <NUM> has a major axis along a y-axis of a coordinate system <NUM>, a minor axis along an x-axis, and grows vertical along a z-axis.

The computer system may be integrated with or coupled to an apparatus that performs an application pass (e.g., planting, tillage, fertilization). Alternatively, the computer system may be integrated with a remote sensor (e.g., remote sensor <NUM>, drone, image capture device) associated with the apparatus that captures images during the application pass. The source of images during any pass could be a remote sensor (e.g., drone with a camera) that is instructed to track (e.g., lead or follow) the apparatus making the field pass. In another example, a user walks through a field and captures images with a mobile device or tablet device having an image capture device (e.g., camera) and the software application. In another example, a user guides an apparatus (e.g., apparatus with wheels and support frame for positioning image capture devices) having at least one image capture device through a field for capturing images. In another example, a self-guided or self-propelled device or robot moves through a field for capturing images with the software application. The software application controls whether images are captured continuously or during time periods of more stable movement as opposed to unstable movement.

Claim 1:
A computer system for monitoring field operations, comprising:
a database for storing agricultural image data including images of at least one stage of crop development that have been captured with at least one of an apparatus (<NUM>) and a remote sensor (<NUM>) while the at least one of the apparatus (<NUM>) and the remote sensor (<NUM>) are moving along a path (<NUM>) of a field along a direction substantially parallel with a row of crops having stalks (<NUM>-<NUM>), the captured images including for each stalk of a row at least a first image of the crop stalk (<NUM>) captured with the at least one of the apparatus (<NUM>) and the remote sensor (<NUM>) at a first angle of view of <NUM> to <NUM> degrees at a first viewpoint (<NUM>) along the path of the field, a second image of the crop stalk (<NUM>) captured at a second angle of view of <NUM> to <NUM> degrees with the at least one of the apparatus (<NUM>) and the remote sensor(<NUM>) at a second viewpoint (<NUM>) along the path of the field, wherein a line drawn from one of the view point to a center of the crop stalk (<NUM>) is approximately perpendicular with respect to the path (<NUM>); and
at least one processing unit coupled to the database, the at least one processing unit is configured to execute instructions (<NUM>) to analyze the captured images, to determine relevant images that indicate a change in at least one condition of the crop development, to determine crop stalk width, or crop stalk width and orientation based on the relevant images, to generate a localized view map layer for viewing the field at the at least one stage of crop development based on at least the relevant captured images, and to save the relevant captured images and the localized view map layer.