Analysis and presentation of agricultural data.
Abstract
A computer-implemented method is described. The method comprises causing the display of a first map of one or more agricultural fields, the first map indicating a first type of agricultural data; receive cost data corresponding to a second type of agricultural data; receive income data; perform RoI analysis for one or more agricultural fields that have a plurality of components, including cost data associated with a third type of agricultural data, revenue data, and corresponding RoI data; causing the display of a second map of one or more agricultural fields simultaneously with the first map, the second map indicating a first component of the RoI analysis; receiving a selection of points from the second map, the selection corresponding to a boundary of a region within one or more agricultural fields; cause a report to be displayed indicating the first component of the region-specific RoI analysis.

Term
12.6 yearsleft in the term
Expires 8 May 2039.
- Priority
- Filed
- Granted
- Today
- Expires
20 claims: 4 independent, 16 dependent
- 1Un método implementado por computadora para gestionar datos relacionados con un proceso agrícola, caracterizado porque comprende:provocar, mediante un procesador, la visualización de un primer mapa de uno o más campos agrícolas, indicando el primer mapa un primer tipo de una pluralidad de tipos de datos agrícolas asociados con uno o más campos agrícolas;recibir, por parte del procesador, datos de costos correspondientes a un segundo tipo de la pluralidad de tipos de datos agrícolas asociados con uno o más campos agrícolas;recibir datos de ingresos asociados con uno o más campos agrícolas;realizar un análisis de retorno de la inversión (Rol) para uno o más campos agrícolas que tienen una pluralidad de componentes, incluidos los datos de costos asociados con un tercer tipo de la pluralidad de tipos de datos agrícolas, los datos de ingresos y los datos de Rol correspondientes;provocar la visualización de un segundo mapa de uno o más campos agrícolas simultáneamente con el primer mapa, indicando el segundo mapa un primer componente de la pluralidad de componentes del análisis de Rol;recibir una selección de puntos del segundo mapa, correspondiendo la selección a un límite de una región dentro de uno o más campos agrícolas;provocar la visualización de un informe que indica el primer componente del análisis de Rol específico de la región.
- 2El método implementado por computadora de conformidad con la reivindicación 1, caracterizado porque las pluralidades de tipos de datos agrícolas incluyen datos de híbridos de semillas, datos de plantación, datos de preparación del suelo, datos de aplicación de fertilizantes, datos de riego, datos de cosecha o datos de secado de granos.
- 3El método implementado por computadora de conformidad con la reivindicación 1, caracterizado además porque comprende:recibir una selección de datos de costos asociados con el tercer tipo de datos agrícolas de la pluralidad de tipos de datos agrícolas, realizar el análisis de Rol que comprende calcular una diferencia entre los montos de ingresos incluidos en los datos de ingresos y los costos incluidos en los datos de costos seleccionados.
- 4El método implementado por computadora de conformidad con la reivindicación 1, caracterizado porque realizar el análisis de Rol comprende comparar un valor de Rol para lA/a/zuzu/ui i auo cada ubicación en uno o más campos agrícolas con un valor de Rol agregado sobre uno o más campos agrícolas u otra región específica, con un valor de Rol para la ubicación correspondiente a un periodo de tiempo anterior, o con un valor de Rol diferente para la ubicación.
- 5El método implementado por computadora de conformidad con la reivindicación 1, caracterizado porque realizar el análisis de Rol comprende identificar una región dentro de uno o más campos que tienen un ROI más alto o un valor de Rol por encima de un cierto umbral.
- 6El método implementado por computadora de conformidad con la reivindicación 1, caracterizado porque realizar el análisis de Rol comprende identificar uno de la pluralidad de tipos de datos agrícolas de manera que los datos de costos asociados tengan una correlación más alta con los datos de ingresos.
- 7El método implementado por computadora de conformidad con la reivindicación 1, caracterizado porque el primer tipo y el segundo tipo de datos agrícolas son idénticos, y los datos de costos de recepción comprenden:recibir una selección de una ubicación en el primer mapa;recibir un costo asociado con el primer tipo de datos agrícolas indicado en la ubicación seleccionada.
- 8El método implementado por computadora de conformidad con la reivindicación 1, caracterizado porque el límite es una forma libre que cubre una parte de un campo agrícola.
- 9El método implementado por computadora de conformidad con la reivindicación 1, caracterizado porque provocar la visualización del informe comprende hacer que el informe se superponga en el segundo mapa.
- 10El método implementado por computadora de conformidad con la reivindicación 1, caracterizado porque el informe indica el primer componente del análisis de Rol para una pluralidad de subregiones de la región correspondiente a una clasificación de la región por uno o más de híbridos de semillas, suelo, población, o elevación.
- 11El método implementado por computadora de conformidad con la reivindicación 1, caracterizado además porque comprende provocar la visualización de una recomendación basada en el análisis de Rol.
- 12El método implementado por computadora de conformidad con la reivindicación 1, caracterizado además porque comprende provocar la nueva visualización del primer mapa, indicando ahora el primer mapa un segundo componente de la pluralidad de componentes del análisis de Rol.
- 13El método implementado por computadora de conformidad con la reivindicación 1, caracterizado además porque comprende:recibir una selección del primer tipo de datos agrícolas de la pluralidad de tipos de datos agrícolas, datos de costos asociados con la pluralidad de tipos de datos agrícolas, datos de ingresos y datos de Rol correspondientes;provocar la visualización de un primer mapa que responde a la selección del primer tipo de datos agrícolas.
- 14Un medio de almacenamiento no transitorio que almacena instrucciones que, cuando son ejecutadas por uno o más dispositivos informáticos, provocan la ejecución de un método de gestión de datos relacionados con un proceso agrícola, el método caracterizado porque comprende:provocar la visualización de un primer mapa de uno o más campos agrícolas, indicando el primer mapa un primer tipo de una pluralidad de tipos de datos agrícolas asociados con uno o más campos agrícolas;recibir datos de costos correspondientes a un segundo tipo de la pluralidad de tipos de datos agrícolas asociados con uno o más campos agrícolas;recibir datos de ingresos asociados con uno o más campos agrícolas;realizar un análisis de retorno de la inversión (Rol) para uno o más campos agrícolas que tienen una pluralidad de componentes, incluidos los datos de costos asociados con un tercer tipo de la pluralidad de tipos de datos agrícolas, los datos de ingresos y los datos de Rol correspondientes;provocar la visualización de un segundo mapa de uno o más campos agrícolas simultáneamente con el primer mapa, indicando el segundo mapa un primer componente de la pluralidad de componentes del análisis de Rol;recibir una selección de puntos del segundo mapa, correspondiendo la selección a un límite de una región dentro de uno o más campos agrícolas;provocar la visualización de un informe que indica el primer componente del análisis de Rol específico de la región.
- 15El medio de almacenamiento no transitorio de conformidad con la reivindicación 14, caracterizado además porque comprende:recibir una selección de datos de costos asociados con el tercer tipo de datos agrícolas de la pluralidad de tipos de datos agrícolas;realizar el análisis de Rol que comprende calcular una diferencia entre los montos de ingresos incluidos en los datos de ingresos y los costos incluidos en los datos de costos seleccionados.
- 16El medio de almacenamiento no transitorio de conformidad con la reivindicación 14, caracterizado porque realizar el análisis de Rol comprende comparar un valor de Rol para cada ubicación en uno o más campos agrícolas con un valor de Rol agregado sobre uno o más campos agrícolas u otra región específica, con un valor de Rol para la ubicación correspondiente a un periodo de tiempo anterior, o con un valor de Rol diferente para la 49 ubicación.
- 17El medio de almacenamiento no transitorio de conformidad con la reivindicación 14, caracterizado porque realizar el análisis de Rol comprende identificar uno de la pluralidad de tipos de datos agrícolas de manera que los datos de costos asociados tengan una correlación más alta con los datos de ingresos.
- 18El medio de almacenamiento no transitorio de conformidad con la reivindicación 14, caracterizado porque el límite es una forma libre que cubre una parte de un campo agrícola.
- 19El medio de almacenamiento no transitorio de conformidad con la reivindicación 14, caracterizado porque el informe indica el primer componente del análisis de Rol para una pluralidad de subregiones de la región correspondiente a una clasificación de la región por uno o más de híbridos de semillas, suelo, población, o elevación.
- 20El medio de almacenamiento no transitorio de conformidad con la reivindicación 14, caracterizado además porque comprende provocar la nueva visualización del primer mapa, indicando ahora el primer mapa un segundo componente de la pluralidad de componentes del análisis de Rol.
Independent claims20
147 paragraphs in 29 sections, as filed
FIELD OF THE INVENTION
The present description relates to the technical area of agricultural data management and graphical user interface and more specifically to the technical area of enabling efficient exploration, review, analysis and/or manipulation of agricultural and financial data associated with different levels of an agricultural process in near real time.
BACKGROUND OF THE INVENTION
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise stated, any of the approaches described in this section should not be assumed to qualify as prior art simply by virtue of their inclusion in this section.
An agricultural process can be long and complex. An example begins with the purchase of seeds and ends with the harvesting of crops, possibly with the application of fertilizers, pesticides or fungicides to the soil or crops, irrigation of the soil, drying of grain, etc. Various costs and revenues may be associated with different levels of an agricultural process. Additionally, different agricultural processes can be implemented on different fields, further complicating any cost-benefit or return-on-investment (“ROI”) analysis for a grower. It would be useful to allow growers to explore, review, analyze and/or manipulate cost and revenue data associated with agricultural processes implemented on individual fields or across multiple fields in near real time and ultimately better understand how to improve returns. investment overview.
BRIEF DESCRIPTION OF THE INVENTION
The appended claims may serve as a summary of the description.
BRIEF DESCRIPTION OF THE DRAWINGS
In the drawings:
FIGURE 1 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.
FIGURES 2A-2B illustrate two views of an example logical organization of instruction sets in main memory when an example mobile application is loaded for execution.
FIGURE 3 illustrates a programmed process whereby the agricultural intelligence computer system generates one or more pre-configured agronomic models using agronomic data provided by one or more data sources.
FIGURE 4 is a block diagram illustrating a computer system in which lA/a/zuzu/ui i auo an embodiment of the invention can be implemented.
FIGURE 5 depicts an example embodiment of a timeline view for data entry.
FIGURE 6 depicts an example embodiment of a spreadsheet view for data entry.
FIGURE 7 illustrates an example screen set up to receive cost data for different seed hybrids.
FIGURE 8 illustrates an example screen configured to display a map of one or more fields with hybrid seed information.
FIGURE 9 illustrates an example screen configured to display a map of one or more fields with planting cost information.
FIGURE 10 illustrates an example screen configured to display a map of one or more fields with performance information.
FIGURE 11 illustrates an example screen configured to display a map of one or more fields and receive a request to enter pricing data for yield in one or more fields.
FIGURE 12 illustrates an example screen configured to receive price data for performance in one or more fields.
FIGURE 13 illustrates an example screen configured to display a map of one or more fields with ROI information.
FIGURE 14 illustrates an example screen configured to display a map of one or more fields and receive a request to receive a field region report.
FIGURE 15 illustrates an example screen configured to display a map of one or more fields and receive a request to receive a specification of a region within one or more fields.
FIGURE 16 illustrates a sample screen configured to display summary data for a specific region, including size, performance, and ROI information.
FIGURE 17 illustrates a sample screen set up to display summary data for a seed hybrid grown in a specific region, including size, yield, and ROI information.
FIGURE 18 illustrates a sample screen set up to display cost and revenue data for one or more fields.
FIGURE 19 illustrates an example process performed by the agricultural data management server for managing data related to an agricultural process.
DETAILED DESCRIPTION OF THE INVENTION
In the following description, for purposes of explanation, numerous specific details are set forth to provide a complete understanding of the present description. However, it will be apparent that modalities can be implemented without these specific details. In other cases, well-known structures and devices are shown in block diagram form to avoid unnecessarily obscuring the present description. The modalities are described in sections according to the following scheme:
1. GENERAL DESCRIPTION
2. SAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM
2.1. STRUCTURAL GENERAL DESCRIPTION
2.2. APPLICATION PROGRAM OVERVIEW
23. DATA ENTRY TO THE COMPUTER SYSTEM
2.4. GENERAL DESCRIPTION OF THE PROCESS: TRAINING IN THE AGRONOMIC MODEL
2.5. IMPLEMENTATION EXAMPLE: HARDWARE OVERVIEW
3. FUNCTIONAL DESCRIPTION
3.1 DATA COLLECTION
3.2 ANALYSIS AND PRESENTATION OF DATA
3.3 EXAMPLE PROCESSES
4. EXTENSIONS AND ALTERNATIVES
1. GENERAL DESCRIPTION
Disclosed is an agricultural data management server computer ("server") for managing data related to an agricultural process. An agricultural process can include many stages or operations, such as preparing the soil, planting, adding manure and fertilizers, watering, harvesting, or storing. Existing computing platforms can capture different types of agricultural data for these operations, including yield data. Ultimately, however, a producer would like to make a profit and could benefit from exploring, reviewing, analyzing, and/or manipulating different types of financial data associated with these operations and the corresponding agricultural data, such as costs, income, and return on farm. investment (“ROI”) being the difference between income and costs.
In some embodiments, the server is programmed or configured with data structures and/or database records that are arranged to analyze financial data associated with different types of agricultural data and enable near real-time, easy-to-use data visualization. use and other exploration, review, further analysis and/or manipulation of the analysis results. A producer may own one or more fields, grow different types of crops, and implement different farming practices on one or more fields. For example, the grower may plant two different seed hybrids in alternate rows in two fields and apply insecticide to one of the fields. The server can be programmed to request information on the costs of buying or planting the two seed hybrids for the two fields as soon as those prices are available. Throughout the farming process, which can span multiple seasons, the server can be programmed to also request input for other types of costs, especially those that vary within one or more fields, to allow the grower to gain more comparative information. In this example, the other types of costs might include the cost of purchasing or applying pesticides. Towards the end of the farming process, the server can be programmed to then request input of the income produced by one or more fields.
In some embodiments, throughout the agricultural process, the server may be programmed to allow browsing, review, analysis, and/or manipulation of different types of agricultural data, associated financial data, and related environmental climate data. When revenue data is available, the server can be programmed to perform different types of Role analysis at different geographic granularities and allow display of analysis results on a user's computer via interactive maps. Each type of Role analysis typically involves calculating the difference between the amounts of revenue and one or more types of costs associated with one or more types of farm data. For example, the Role analysis might include comparing revenue to the cost of purchasing a hybrid seed or the sum of the cost of purchasing a hybrid seed and the cost of applying pesticides. Different types of Role analysis can lead to Role data being presented in different ways. For example, one type of Role analysis might involve presenting the Role value for each location relative to an aggregate value, and another type of Role analysis might involve presenting the Role value for each location relative to a period. of previous time. Yet another type of Role analysis may involve presenting ROI values that satisfy a criterion differently than those ROI values that do not satisfy the criterion. For example, a map can be presented that highlights those locations where the Role value is above a certain threshold.
In some modalities, the server can be programmed to allow the exploration, revision, analysis and/or manipulation of all the relevant data in a Role analysis in a flexible but optimized way through a pair of interactive maps corresponding to the same area. geographic. Each of the interactive maps can indicate any of a number of data types relevant to a Role analysis for a specific time period, including different types of agricultural data, cost data associated with different types of agricultural data , the input data and the corresponding Role data. The two maps can be displayed simultaneously on the screen, and each can be reused to display a new type of data relevant to a Role analysis.
This simultaneous and reusable visualization makes it easier to understand the variations of value in one of the maps and the progression of a Role analysis in general. For example, in response to a selection of purchased seed hybrids as indicated by data input to a user computer, a first map corresponding to one or more fields may be displayed indicating the seed hybrid planned or planted for each location. on the first map. In other modes, other relevant data types can be entered and accepted in a Role analysis. In response to a selection of cost data associated with purchased seed hybrids, a second map corresponding to one or more fields may be displayed adjacent to the first map indicating cost data for each location on the second map. In response to a selection of yields, the second map may be redisplayed indicating the yield data for each location on the second map. Also, in response to a selection of seed yields (eg, the difference between the amounts of income and the costs of purchasing hybrid seeds), the first map can be redisplayed indicating the seed yields. More interaction with the maps is possible. For example, a user computer may select a location on the first map indicating a particular seed hybrid and input a cost to purchase that particular seed hybrid, or a user computer may select a location on the second map indicating a particular seed hybrid. particular yield and enter a price to sell the crop produced. Similarly, a user computer can specify the boundary of an arbitrary region on the second map by indicating seed yields and obtain further data on seed yields specific to the specified region.
In some embodiments, the server may be programmed to further determine recommendations based on the Role analysis and present them to user computers. For example, the server can be configured to identify those regions within one or more fields that have produced the lowest ROIs and determine any costs associated with one of several different types of farm data that highly correlate with revenue from these regions. regions. Those regions can then be highlighted on the map with a screen overlay suggesting a cost reduction associated with a type of agricultural data. For example, the server can be configured to determine similar upward trends in Rol data in two regions with identical or similar seed hybrids planted and agricultural practices implemented, except that a first region is ahead of a second region. in causing and experiencing and the upward trend. An ROI forecast for the second region based on the current ROI of the first region can then be sent to a user device.
The server produces many technical benefits. When the effectiveness of an agricultural trial is evaluated along with the cost of the trial, the evaluation provides the most objective basis for comparing the usefulness and practical benefit of the trials. The server makes it easier for growers and distributors to evaluate the ROI values of product decisions and leads to the implementation of trials and more effective agricultural processes overall. More specifically, through optimized data management, the server enables different levels of cost and revenue data to be explored, reviewed, analyzed and/or manipulated individually or in combination so that a user computer can drill down into a generally complex cost structure associated with an agricultural process and, potentially, obtain additional information on how certain costs could affect the ROLE The server also allows the exploration, review, analysis and/or manipulation of such cost levels and revenue data as soon as possible. This allows a user's computer to perform a Relevant Role analysis in near real time (relative to the time the analysis request was submitted) based on benchmarking data for particular geographic regions or time periods and conforming to current practices. Current farms, as applicable. Historical data management also improves understanding of field and product performance over time, representing a variety of weather situations or management practices. In addition, the server allows the exploration, review, analysis and/or manipulation of said cost levels and efficient graphical representation of income data to visualize financial data in the geographic domain and receive correlations between different components of a Role analysis. In addition, through various complex Role analyses, the server enables user computers to make the best decisions on resource utilization, trial selection, or product placement in fields.
2. SAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM
2.1 STRUCTURAL GENERAL DESCRIPTION
FIGURE 1 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 102 operates or owns a field management computing device 104 at or associated with a field location such as a field dedicated to agricultural activities or a management location for one or more agricultural fields. Field management computing device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computing system 130 over one or more networks 109.
Examples of field data 106 include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that can be used to identify land such as a common land unit (CLU), lot and block number, parcel number, geographic coordinates and boundaries, farm serial number (FSN), farm number, stretch number, field number, section, municipality and/or range), (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is organically grown, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop income, grain moisture, tillage practice, and previous growing season information), (c) soil data (for example, type , composition, pH, organic matter (OM), cation exchange capacity (CIO)), (d) planting data (for example, planting date, seed type, relative maturity (RM) of planted seeds, seed population), (e ) fertilizer data (for example, nutrient type (nitrogen, phosphorus, potassium), application type, application date, amount, source, method), (f) chemical application 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) meteorological data (for example, precipitation, rainfall rate, forecast rainfall, water runoff rate region, temperature, wind, forecast, pressure, visibility, clouds, heat index, dew point, humidity, depth Of snow, air quality, sunrise, sunset), (i) imagery data (for example, images and light spectrum information from an agricultural appliance sensor, camera, computer, smartphone, tablet, UAV, aircraft, or satellite ), (j) scouting observations (photos, videos, free-form notes, voice recordings, voice transcripts, climatic conditions (temperature, precipitation (current and over time), soil moisture, crop growth stage, wind speed, relative humidity, dew point, black layer)) and (k) soil, seed, crop phenology, pest and disease reports and forecasts, sources and databases.
A data server computer 108 is communicatively coupled to agricultural intelligence computer system 130 and is programmed or configured to send external data 110 to agricultural intelligence computer system 130 via network 109. The external data server computer 108 may be owned or operated by the same legal person or entity as the agricultural intelligence computer system 130, 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, image data, soil data, or statistical data related to crop yields, among others. External data 110 may consist of the same type of information as field data 106. In some embodiments, external data 110 is provided by an external data server 108 owned by the same entity that owns and/or operates the computer system. agricultural intelligence 130. For example, agricultural intelligence computer system 130 may include a data server focused exclusively on one type of data that might otherwise be obtained from third party sources, such as weather data. In some embodiments, an external data server 108 may actually be incorporated within the system 130.
An agricultural appliance 111 may have one or more remote sensors 112 attached thereto, which sensors are communicatively coupled either directly or indirectly through an agricultural appliance 111 to the agricultural intelligence computer system 130 and are programmed or configured to send the data from the sensor to the agricultural intelligence computer system 130. Examples of 111 agricultural equipment include tractors, combines, planters, trucks, fertilizer equipment, aerial vehicles, including UAVs, and any other item of physical machinery or hardware, typically mobile machinery, and capable of being used in associated tasks. to agriculture. In some embodiments, a single apparatus unit 111 may comprise a plurality of sensors 112 that are locally coupled into a network in the apparatus; The Controller Area Network (CAN) is an example of such a network that can be installed on combines, sprayers, and cultivators. Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via network 109 and is programmed or configured to receive one or more Scripts that are used to control an operating parameter of an agricultural vehicle or implement from the computer system. agricultural intelligence 130. For example, a controller area network (CAN) bus interface can be used to enable communications from agricultural intelligence computer system 130 to agricultural apparatus 111, as used by the CLIMATE FIELDVIEW DRIVE, available from The Climate Corporation, San francisco California. The sensor data may consist of the same type of information as the field data 106. In some embodiments, the remote sensors 112 may not be attached to an agricultural appliance 111 but may be remotely located in the field and may communicate with the network 109.
Apparatus 111 may comprise a cockpit computer 115 that is programmed with a cockpit application, which may comprise a version or variant of the mobile application for device 104 described below in other sections herein. In one embodiment, the cockpit computer 115 comprises a compact computer, often a tablet or smartphone-sized computer, with a graphical display, such as a color display, that is mounted within the operator's cabin of the apparatus. 111. The cockpit computer 115 may implement some or all of the operations and functions described herein below for the mobile computer device 104.
Network 109 broadly represents any combination of one or more data communication networks including local area networks, wide area networks, Internetworks or the Internet, using any of the wired or wireless links, including terrestrial or satellite links. The network can be implemented by any means or mechanism that provides the exchange of data between the various elements of FIGURE 1. The various elements of FIGURE 1 can also have direct communication links (wired or wireless). Sensors 112, controller 114, external data server computer 108, and other system elements each comprise a network-compatible interface 109 and are programmed or configured to use standardized protocols for communication over networks such as TCP. /IP, Bluetooth, CAN protocol and higher level protocols like HTTP, TLS and the like.
Agricultural intelligence computer system 130 is programmed or configured to receive field data 106 from field management computer device 104, external data 110 from external data server computer 108, and sensor data from remote sensor 112. The agricultural intelligence computer system 130 may be further configured to host, use, or run one or more computer programs, other software items, digitally programmed logic such as FPGAs or ASICs, or any combination thereof to perform translation and storage of input values. data, construction of digital models of one or more crops in one or more fields, generation of recommendations and notifications, and generating and sending Scripts to application controller 114, as further described in other sections of this description.
In one embodiment, agricultural intelligence computer system 130 is programmed with or comprises a communication layer 132, presentation layer 134, data management layer 140, hardware/virtualization layer 150, and field data repository. and model 160. In this context, "layer" refers to any combination of electronic digital interface circuitry, microcontrollers, firmware such as controllers, and/or computer programs or other software items.
Communication layer 132 can be programmed or configured to perform input/output interface functions including sending requests to field management computing device 104, external data server computer 108, and remote sensor 112 for field data. , external data and sensor data, respectively. The communication layer 132 can be programmed or configured to send the received data to the field and model data repository 160 to be stored as field data 106.
Presentation layer 134 can be programmed or configured to generate a graphical user interface (GUI) to be displayed on field management computing device 104, cockpit computer 115, or other computers that are coupled to system 130 via the network 109. The GUI may comprise controls for entering data to be sent to the agricultural intelligence computer system 130, generating requests for models and/or recommendations, and/or displaying recommendations, notifications, models, and other field data.
The data management layer 140 can be programmed or configured to manage read and write operations involving the repository 160 and other functional elements of the system, including queries and result sets communicated between the functional elements of the system and the repository. . Examples of the data management layer 140 include JDBC, SQL server interface code, and/or HADOOP interface code, among others. The repository 160 may comprise a database. As used herein, the term "database" can refer to a body of data, a relational database management system (RDBMS), or both. As used herein, a database can comprise any collection of data, including hierarchical databases, relational databases, flat file databases, object-relational databases, object-oriented databases, distributed databases and any other structured collection of records or data that is stored in a computer system. Examples of RDBMS include, but are not limited to, ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, any database that enables the systems and methods described herein can be used.
When field data 106 is not provided directly to the agricultural intelligence computer system through one or more agricultural machines or agricultural machine devices that interact with the agricultural intelligence computer system, the user may receive prompts through one or more user interfaces on the user device (served by the agricultural intelligence computer system) for entering such information. In an exemplary embodiment, the user may specify identification data by accessing a map on the user's device (served by the agricultural intelligence computer system) and selecting specific CLUs that have been graphically displayed on the map. In an alternate embodiment, user 102 may specify identification data by accessing a map on the user device (served by agricultural intelligence computer system 130) and drawing field boundaries on the map. Said selection of CLUs 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 US Department of Agriculture, Farm Service Agency or other source through the user device and providing said field identification data to the agricultural intelligence computer system.
In an exemplary embodiment, agricultural intelligence computer system 130 is programmed to generate and display a graphical user interface comprising a data manager for data entry. After one or more fields have been identified using the methods described above, the data steward may provide one or more graphical user interface widgets that, when selected, can identify changes in 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.
FIGURE 5 depicts an example embodiment of a timeline view for data entry. Using the screen shown in FIGURE 5, a user computer can enter a selection of a particular field and a particular date for the event addition. Events represented at the top of the timeline may include nitrogen, planting, practices, and soil. To add a nitrogen application event, a user computer can provide information to select the nitrogen tab. The user computer can then select a location on the time line for a particular field in order to indicate a nitrogen application to the selected field. In response to receiving a selection of a timeline location for a particular field, the data manager may display a data entry overlay, allowing the user computer to enter data related to nitrogen applications, procedures planting times, soil application, tillage procedures, irrigation practices, or other information related to the particular field. For example, if a user computer selects a portion of the timeline and indicates a nitrogen application, then the data entry overlay may include fields to enter an amount of nitrogen applied, an application date, a fertilizer type used and any other information related to nitrogen application.
In one embodiment, the data manager provides an interface for creating one or more programs. In this context, "program" refers to a set of data related 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 it can be stored in digital data storage for reuse as a whole 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. Therefore, instead of manually entering identical data related to the same nitrogen applications for multiple different fields, a user computer can create a program indicating a particular nitrogen application and then apply the program to multiple different fields. For example, in the timeline view in FIGURE 5, the top two timelines have the “Spring Applied” schedule selected, which includes a 68.03 kg (150 Ibs) N/4,046 m application.<sup>2</sup> (ac) in early April. The data manager may provide an interface for editing a program. In one embodiment, when a particular program is edited, each field that the particular program has selected is edited. For example, in FIGURE 5, if the “Spring Applied” schedule is edited to reduce nitrogen application to 58.96 kg (130 Ibs) N/4,046 m<sup>2</sup> (ac), the two upper fields can be updated with a reduced nitrogen application according to the edited program.
In one embodiment, in response to receiving edits to a field having a selected program, the data manager unmaps the field for the selected program. For example, if a nitrogen application is added to the top field in FIGURE 5, the interface may update to indicate that the “Spring Applied” schedule no longer applies to the top field. While the early April nitrogen application may remain, updates to the “Spring Applied” schedule would not alter the April nitrogen application.
FIGURE 6 depicts an example embodiment of a spreadsheet view for data entry. Using the screen shown in FIGURE 6, a user can create and edit information for one or more fields. The data manager may include spreadsheets for entering information regarding nitrogen, planting, practices, and soil as shown in FIGURE 6. To edit a particular entry, the user computer can select the particular entry in the spreadsheet and update the values. For example, FIGURE 6 depicts an ongoing update of a target performance value for the second field. Furthermore, a user computer can select one or more fields 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 can update the entries for each field associated with a particular program in response to receiving an update from the program. In addition, the data manager may unmap the selected program to the field in response to receiving an edit to one of the field entries.
In one embodiment, model and field data is stored in model and field data repository 160. Model data comprises model data created for one or more fields. For example, a crop model may include a digitally constructed model of crop development in one or more fields. In this context, "model" means a digitally stored electronic set of executable instructions and data values, associated with one another, that is capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based on specified input values, to produce one or more stored or calculated output values that can serve as the basis of computer-implemented recommendations, output data screens or machine control, among other things. Persons skilled in the art find it convenient to express models using mathematical equations, but that form of expression does not limit the models described 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 may include a model of past events in one or more fields, a model of the current state of one or more fields, and/or a model of predicted events in one or more fields. Field and model 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.
In one embodiment, agricultural intelligence computer system 130 is programmed to comprise an agricultural data management server computer ("server") 170. Server 170 is further configured to comprise a data collection module 172, a data collection module 172, a data analysis 174, and a data presentation module 176. The data collection module 172 is configured to collect additional data related to an agricultural process, such as costs and revenues for agricultural operations or data associated with the agricultural process. The data collection module 172 can be configured to provide reminders for data entry or to facilitate data entry to expedite further data processing and analysis. The data analysis module 174 is configured to analyze the data collected by the data collection module 172 and related data. The analysis could be comparative in nature across time, location, or other dimensions and could focus on costs associated with certain farm data or operations, income associated with yields, or returns on investment based on a combination of the costs. The data presentation module 176 is configured to present the collected data or analysis results of the collected data through graphical user interfaces to facilitate exploration, review, analysis, manipulation, display, and/or understanding of the data. The data display module 176 can be configured to start with one or more maps of the agricultural fields of interest and display additional information, such as collected cost data, on top of the maps to allow for display of data on the map. family geographical domain of the producers.
Each server component 170 comprises a set of one or more pages of main memory, such as RAM, in the agricultural intelligence computer system 130 into which executable instructions have been loaded and which, when executed, cause the system to run. agricultural intelligence computer perform the functions or operations described herein with reference to those modules. For example, data collection module 172 may comprise a set of pages in RAM that contain instructions that, when executed, cause the location selection functions described herein to be performed. The instructions may be in executable machine code in the instruction set of a CPU and may have been compiled based on source code written in JAVA, C, C++, OBJECTIVE-C, or any other human-readable programming language or environment. , alone or in combination with JAVASCRIPT Scripts, other scripting languages and other programming source text. The term "pages" is intended to refer broadly to any region within main memory, and the specific terminology used in a system may vary depending on memory architecture or processor architecture. In another embodiment, each server component 170 may also represent one or more source code files or projects that are digitally stored in a mass storage device such as nonvolatile RAM or disk storage, in the agricultural intelligence computer system 130, or a separate repository system, which when compiled or interpreted, generates executable instructions which, when executed, cause the agricultural intelligence computer system to perform the functions or operations described herein with reference to those modules. In other words, the figure in the drawing can represent the way in which programmers or software developers organize and arrange the source code for its later compilation into an executable, or the interpretation in byte code or its equivalent, for its execution by the agricultural intelligence computer system 130.
The hardware/virtualization layer 150 comprises one or more central processing units (CPUs), memory controllers, and other devices, components, or elements of a computer system, such as volatile or nonvolatile memory, nonvolatile storage such as a disk, and devices or I/O interfaces as illustrated and described, for example, in connection with FIGURE 4. Layer 150 may also comprise scheduled instructions that are configured to support virtualization, storage, or other technologies.
For the purpose of illustrating a clear example, FIGURE 1 shows a limited number of instances of certain functional elements. However, in other embodiments, there may be any number of such elements. For example, the modalities may use thousands or millions of different mobile computing devices 104 associated with different users. In addition, system 130 and/or external data server computer 108 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 data center, a shared computing facility, or a cloud computing facility.
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2.2. APPLICATION PROGRAM OVERVIEW
In one embodiment, application of the functions described herein using one or more computer programs or other software items that are loaded and executed using one or more general-purpose computers will cause the general-purpose computers to be configured as one machine. in particular or as a computer that is specially adapted to perform the functions described herein. In addition, each of the flowcharts described hereinafter can serve, alone or in combination with the prose descriptions of processes and functions herein, as algorithms, plans, or instructions that can be used to program a computer or logic to implement the functions described. In other words, all of the prose text herein, and all of the drawing figures, taken together, are intended to provide the description of algorithms, plans, or instructions that are sufficient to enable a skilled person to program a computer to perform the functions described herein, in combination with the skill and knowledge of such person given the appropriate skill level for inventions and descriptions of this type.
In one embodiment, user 102 interacts with agricultural intelligence computer system 130 using a field management computer device 104 configured with an operating system and one or more applications or application programs; The field management computing device 104 can also interoperate with the agricultural intelligence computing system independently and automatically under logic or program control and direct user interaction is not always required. Field management computing device 104 broadly represents one or more of a smartphone, PDA, tablet computing device, laptop, desktop, workstation, or any other computing device capable of transmitting and receiving information and perform the functions described herein. The field management computing device 104 may communicate over a network using a mobile application stored on the field management computing device 104, and in some embodiments, the device may be attached using a cable 113 or connector to the sensor 112 and /or a controller 114. A particular user 102 may own, operate, or own and use, in connection with system 130, more than one field management computing device 104 at a time.
The mobile application may provide client-side functionality over the network to one or more mobile computing devices. In an exemplary embodiment, the field management computing device 104 may access the mobile application through a web browser, an application, or a local client application. Field management computing device 104 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 application-specific protocols. In an exemplary embodiment, the lA/a/zuzu/ui i auo data may take the form of user input and requests for information, such as field data, on the mobile computing device. In some embodiments, the mobile application interfaces with location tracking hardware and software on field management computing device 104 that determines the location of field management computing device 104 using standard tracking techniques such as signal multilateration. radio, the global positioning system (GPS), WiFi positioning systems or other mobile positioning methods. In some cases, location data or other data associated with the device 104, user 102, and/or user accounts may be obtained by querying a device operating system or by requesting an application on the device to obtain system data. operational.
In one embodiment, field management computing device 104 sends field data 106 to agricultural intelligence computing system 130 that comprises or includes, but is not limited to, data values that represent one or more of: a geographic location of one or more fields, tillage information for one or more fields, crops planted in one or more fields, and soil data extracted from one or more fields. Field management computing device 104 may send field data 106 in response to user input from user 102 specifying data values for one or more fields. In addition, field management computing device 104 may automatically send field data 106 when one or more of the data values are available to field management computing device 104. For example, field management computing device 104 may be communicatively coupled to remote sensor 112 and/or application controller 114 that includes an irrigation sensor and/or irrigation controller. In response to receiving data indicating that application controller 114 released water in one or more fields, field management computing device 104 may send field data 106 to agricultural intelligence computing system 130 indicating that water was released in one or more fields. Field data 106 identified in this description may be entered and communicated using electronic digital data that is communicated between computing devices using parameterized URLs over HTTP, or other suitable messaging or communication 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, expanded, or adapted to include features, functions, and programming not described prior to the filing date of this description. In one embodiment, the mobile application comprises an integrated software platform that enables a grower to make fact-based decisions for their operation by combining historical data about the grower's fields with any other data the grower wishes to compare. The combinations and comparisons can be done in real time and are based on scientific models that provide potential scenarios that allow the producer to make better and more informed decisions.
FIGURES 2A-2B illustrate two views of an example logical organization of instruction sets in main memory when an example mobile application is loaded for execution. In FIGS. 2A-2B, 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 instructions programmed within those regions. In one embodiment, in view (a), a mobile computing application 200 comprises account field data entry sharing instructions 202, general and alert instructions 204, digital map book instructions 206, seed instructions, and Planting Instructions 208, Nitrogen Instructions 210, Weather Instructions 212, Field Health Instructions 214, and Operating Instructions 216.
In one embodiment, a mobile computing application 200 comprises account field data entry sharing instructions 202 that are programmed to receive, translate and ingest field data from third party systems via manual upload or API. Data types may include field boundaries, yield maps, planting maps, soil test results, application maps, and/or management zones, among others. Data formats may include shape files, native third-party data formats, and/or farm management information system (SIAF) exports, among others. The receipt of data can occur through manual upload, email with attachment, external APIs that send data to the mobile application, or instructions that call APIs from external systems to extract data to the mobile application. In one embodiment, mobile computing application 200 comprises a data inbox. In response to receiving a selection from the data inbox, the mobile computing application 200 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 206 comprise layers of field map data stored in device memory and are programmed with geospatial field notes and data visualization tools. This provides growers with convenient information at their fingertips for reference, recording, and visual feedback on field performance. In one embodiment, the general and alert instructions 204 are programmed to provide an operation-wide view of what is important to the producer and timely recommendations for taking action or targeting particular problems. This allows the grower to focus time on what needs attention, saving time and preserving yield throughout the season.
In one embodiment, the seed and planting instructions 208 are programmed to provide tools for seed selection, hybrid placement, and Script creation, including Variable Rate (VR) Script creation, based on scientific models. and empirical data. This allows growers to maximize yield or return on investment through optimized seed purchase, placement, and population.
In one embodiment, the Script generation instructions 205 are programmed to provide an interface for the generation of Scripts, including Variable Rate (VR) Fertility Scripts. The interface allows growers to create scripts for field implements such as nutrient applications, planting, and irrigation. For example, a planting script interface may comprise tools to identify a type of seed to plant. Upon receipt of a seed type selection, the mobile computing application 200 may display one or more fields zoned for management, such as the field map data layers created as part of the digital map book instructions 206. In In one embodiment, the management zones comprise soil zones together with a panel identifying each soil zone and a soil name, texture, drainage for each zone, or other field data. The mobile computing application 200 may also display tools for editing or creating, such as graphical tools for drawing management zones, such as land zones, on a map of one or more fields. Planting procedures can be applied to all management zones or different planting procedures can be applied to different subsets of management zones. When a script is created, the mobile computing application 200 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 cockpit computer 115 from mobile computing application 200 and/or uploaded to one or more data servers and stored for later use.
In one embodiment, the nitrogen instructions 210 are programmed to provide tools to inform nitrogen decisions by displaying the availability of nitrogen to crops. This allows growers to maximize yield or return on investment through optimized in-season nitrogen application. Example programmed functions include displaying images as SSURGO images to allow drawing of fertilizer application zones and/or images generated from subfield soil data, such as sensor data, with high spatial resolution (so fine as millimeters or smaller depending on sensor proximity and resolution); loading zones defined by existing producers; provide a plot of plant nutrient availability and/or a map to allow tuning of nitrogen applications in multiple zones; output of Scripts to drive machinery; tools for massive data entry and adjustment; and/or maps for data visualization, among others. In this context, “mass data entry” can mean entering data once and then applying the same data to multiple fields and/or zones that have been defined in the system; Example data may include nitrogen application data that is the same for many fields and/or zones of the same producer, but such bulk data entry applies to the entry of any type of field data into the mobile computing application 200 . For example, the nitrogen instructions 210 can be programmed to accept nitrogen application definitions and practice programs and to accept user input specifying to apply those programs in multiple fields. In this context, "nitrogen application programs" refers to stored and named data sets that associate: a name, color code or other identifier, one or more application dates, material or product types for each of the dates and quantities, method of application or incorporation as injected or broadcast, and/or quantities or application rates for each of the dates, crop or hybrid that is the object of the application, among others. In this context, "nitrogen practice programs" refer to stored and named data sets that associate: a practice name; a previous harvest; a farming system; a primary tillage date; one or more previous tillage systems used; one or more indicators of the type of application, such as manure, that was used. Nitrogen commands 210 can also be programmed to generate and cause the display of a nitrogen graph, which indicates projections of plant use of the specified nitrogen and whether a surplus or deficit is predicted; In some modalities, different color indicators may indicate a magnitude of surplus or a magnitude of deficit. In one embodiment, a nitrogen graph comprises a graphic display on a computer display device comprising a plurality of rows, each row associated with identifying a field; data specifying which crop is planted in the field, the size of the field, the location of the field, and a graphical representation of the perimeter of the field; in each row, a time line by month with graphic indicators specifying each nitrogen application and the amount in points correlated with the names of the months; and numerical and/or colored surplus or deficit indicators, where color indicates magnitude.
In one embodiment, the nitrogen chart may include one or more user input features, such as sliders or dials, for dynamically changing nitrogen planting and practice programs so that a user can optimize their nitrogen chart. The user can then use their optimized nitrogen graph and related nitrogen planting and practice programs to implement one or more scñpts, including variable rate (VR) fertility scñpts. Nitrogen commands 210 can also be programmed to generate and cause the display of a nitrogen map, which indicates projections of plant use of the specified nitrogen and whether a surplus or deficit is predicted; In some modalities, different color indicators may indicate a magnitude of surplus or a magnitude of deficit. The nitrogen map can show projections of plant use of the specified nitrogen and whether a surplus or deficit is predicted for different times in the past and future (such as daily, weekly, monthly, or yearly) using numerical and/or rate indicators. colors of surplus or deficit, in which the color indicates the magnitude. In one embodiment, the nitrogen map may include one or more user input features, such as sliders or dials, to dynamically change nitrogen planting and practice schedules so that a user can optimize their nitrogen map, such as to obtain a preferred amount from surplus to deficit. The user can then use their optimized nitrogen map and related nitrogen planting and practice programs to implement one or more Scripts, including Variable Rate (VR) Fertility Scripts. In other embodiments, instructions similar to the nitrogen 210 instructions could be used for the application of other nutrients (such as phosphorus and potassium), pesticide application, and irrigation schedules.
In one embodiment, weather instructions 212 are programmed to provide recent field-specific weather data and forecast weather information. This allows producers to save time and have an efficient integrated screen regarding daily operational decisions.
In one embodiment, field health instructions 214 are programmed to provide timely remote sensing images that highlight in-season crop variation and potential concerns. Example programmed functions include Cloud Check, to identify potential clouds or cloud shadows; determination of nitrogen indices based on field images; graphical display of scan layers, including, for example, those related to field health and viewing and/or sharing of scan notes; and/or downloading satellite images from multiple sources and prioritizing images for the producer, among others.
In one embodiment, the operating instructions 216 are programmed to provide reporting, analysis, and reporting tools using farm data for evaluation, reporting, and decisions. This allows the producer to seek better results for the coming year through fact-based conclusions about why ROI was at previous levels, and understanding the factors limiting performance. The operating instructions 216 can be programmed to communicate over the network 109 with backend analysis programs running on the agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as performance. , yield differential, hybrids, population, SSURGO zone, soil or elevation test properties, among others. Scheduled reports and analyzes may include analysis of yield variability, estimation of treatment effect, benchmarking of yield, and other metrics with other growers based on anonymous data collected from many growers, or seed and planting data, among others.
Applications that have instructions configured in this way can be implemented for different computing device platforms while maintaining the same general user interface look and feel. For example, the mobile application can be scheduled to run on tablets, smartphones, or server computers that are accessed through browsers on client computers. In addition, the mobile application configured for tablets or smartphones can provide a full application experience or a cockpit application experience that is suitable for the display and processing capabilities of the cockpit computer 115. For example, referring now to view (b) of FIGS. 2A-2B, in one embodiment, a cockpit computer application 220 may comprise map booth commands 222, remote view commands 224, collect and transfer commands instructions 226, machine alert instructions 228, Scripts transfer instructions 230 and cabin instructions 232. The code base for the instructions in FIGURE 2B can be the same as for FIGURE 2A and the executables that implement the code can be programmed to detect the type of platform they are running on and expose, via an interface. user graph, only those functions that are appropriate for a cockpit platform or an entire platform. This approach allows the system to recognize distinctly different user experience that is appropriate for an in-cab environment and the different technological environment of the cabin. The map booth commands 222 can be programmed to provide map views of fields, farms, or regions that are useful in directing the operation of the machine. Remote display instructions 224 can be programmed to power on, manage, and provide real-time or near-real-time views of machine activity to other computing devices connected to system 130 via wireless networks, wired connectors or adapters, and the like. . Data collection and transfer instructions 226 can be programmed to power on, manage, and provide transfer of data collected from sensors and controllers to system 130 via wireless networks, wired connectors or adapters, and the like. Machine alert instructions 228 can be programmed to detect problems with the operations of the machine or tools that are associated with the cab and generate alerts for the operator. Script Transfer Instructions 230 may be configured to transfer Instruction Scripts that are configured to direct machine operations or data collection. The roam booth commands 232 can be programmed to display location-based alerts and information received from the system 130 based on the location of the field management computing device 104, farm appliance 111, or sensors 112 in the field and ingest, managing and providing the transfer of location-based scouting observations to the system 130 based on the location of the agricultural appliance 111 or sensors 112 in the field.
23. DATA ENTRY TO THE COMPUTER SYSTEM
In one embodiment, external data server computer 108 stores external data 110, including soil data representing soil composition for one or more fields and weather data representing temperature and precipitation for one or more fields. Weather data may include past and present weather data, as well as forecasts for future weather data. In one embodiment, the external data server computer 108 comprises a plurality of servers hosted by different entities. For example, a first server may contain soil composition data while a second server may include weather data. Additionally, soil composition data can be stored on multiple servers. For example, one server might store data representing the percentage of sand, silt, and clay in the soil, while a second server might store data representing the percentage of organic matter (OM) in the soil.
In one embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. The remote sensor 112 can be airborne sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from one or more fields. In one embodiment, application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130. Application controller 114 may also be programmed or configured to control an operating parameter of a vehicle or agricultural implement. For example, an application controller can be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvesting equipment, or other agricultural implements such as a water valve. . Other modalities may use any combination of sensors and controllers, of which the following are merely selected examples.
System 130 can obtain or ingest data under the control of user 102, on a mass basis from a large number of producers who have contributed data to a shared database system. This form of obtaining data may be referred to as "manual data entry" as one or more user-controlled computing operations are requested or activated to obtain data for use by the system 130. As an example, the CLIMATE FIELDVIEW application, commercially available from The Climate Corporation, San Francisco, California, can be operated to export data to system 130 for storage in repository 160.
For example, seed monitoring systems can control planter components and obtain planting data, including signals from seed sensors, via a signal harness comprising a CAN backbone and point-to-point connections for registration and/or diagnosis. Seed monitoring systems can be programmed or configured to display seed spacing, population, and other information to the user through the cockpit computer 115 or other devices within the system 130. Examples are described in US Patent No. 8,738,243 and US Patent Pub. 20150094916, and the present disclosure assumes acknowledgment of those other patent disclosures.
Similarly, yield monitoring systems may contain yield sensors for a harvesting apparatus that send yield measurement data to cockpit computer 115 or other devices within the system 130. Yield monitoring systems may use one or more remote sensors 112 to obtain grain moisture measurements in a combine and transmit these measurements to the user via the cab computer 115 or other devices within the system 130.
In one embodiment, examples of sensors 112 that can 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 gyroscopes. Position sensors may comprise GPS receivers or transceivers, or WiFi-based position or mapping applications that are programmed to determine location based on nearby WiFi access points, among others.
In one embodiment, examples of sensors 112 that can be used with tractors or other moving vehicles include engine speed sensors, fuel consumption sensors, area or distance counters that interact with radar or GPS signals, PTO (power take-off) speed, tractor hydraulic sensors configured to detect hydraulic parameters such as pressure or flow and/or hydraulic pump speed sensors, wheel speed sensors or wheel slip sensors. In one embodiment, examples of controllers 114 that can be used with tractors include hydraulic directional controllers, pressure controllers, and/or flow controllers; hydraulic pump speed controllers;
speed controllers or governors; hitch position controllers; or wheel position controllers provide automatic steering.
In one embodiment, examples of sensors 112 that can be used with seed planting equipment, such as planters, drills, or air seeders include seed sensors, which can 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 performance 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 sensors vacuum level; or pesticide application sensors such as optical or other electromagnetic sensors, or shock sensors. In one embodiment, examples of controllers 114 that can be used with such seed planting equipment include: toolbar folding 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 can be programmed to apply downforce to individual row units or the entire planter frame; planting depth controllers, such as linear actuators; metering controllers, such as electric seed meter drives, hydraulic seed meter drives or row control clutches; hybrid selection controllers, such as seed meter drive motors, or other actuators programmed to selectively allow or prevent 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 conveying system controllers, such as controllers for a seed supply conveyor motor; marker controllers, such as a controller for a pneumatic or hydraulic actuator; or pesticide application rate controllers such as metering push controllers, orifice size or position controllers.
In one embodiment, examples of sensors 112 that can be used with tillage equipment include position sensors for tools such as handles or discs; tool position sensors for such tools that are configured to detect depth, group angle, or side clearance; downforce sensors; or draft force sensors. In one mode, the controller examples
114 that can be used with tillage equipment include downforce controllers or tool position controllers, such as controllers configured to control tool depth, clump angle, or side spacing.
In one embodiment, examples of sensors 112 that may be used in connection with apparatus for applying fertilizers, insecticides, fungicides, and the like, such as in-planter feed fertilizer systems, subsoil fertilizer applicators, or fertilizer sprayers, include: fluid system criteria sensors, such as flow sensors or pressure sensors; sensors that indicate 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 arranged in spray arms. In one embodiment, examples of controllers 114 that can be used with such an apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM, and the like; or position actuators, such as boom height, subsoiler depth, or boom position.
In one embodiment, examples of sensors 112 that can be used with combines include performance monitors, such as strike plate strain gauges or position sensors, capacitive flow sensors, load sensors, weight sensors, or load sensors. torque associated with elevators or augers, or optical or other electromagnetic grain height sensors; grain moisture sensors, such as capacitive sensors; grain loss sensors, including shock, optical or capacitive sensors; header operating criteria sensors such as header height, header type, deck plate gap, feeder speed, and reel speed sensors; separator performance 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 one embodiment, examples of controllers 114 that can be used with combines include header performance criteria controllers for items such as header height, header type, header plate gap, feeder speed, or reel speed. ; separator performance criteria controllers for characteristics such as concave clearance, rotor speed, shoe clearance, or chaffer clearance; or controllers for bit position, operation, or speed.
In one embodiment, examples of sensors 112 that can be used with grain carts include weight sensors, or sensors for auger position, operation, or speed. In one embodiment, examples of controllers 114 that can be used with grain carts include controllers for auger position, operation, or speed.
In one embodiment, examples of sensors 112 and controllers 114 may be installed on unmanned aerial vehicle (UAV) apparatus or "drains." Such sensors may include cameras with detectors effective for any range of the electromagnetic spectrum including visible light, infrared, ultraviolet, near infrared (NIR) and the like; accelerometers; altimeters; thermometers; humidity sensors; Pitot tube sensors or other air or wind speed sensors; battery life sensors; or Radar emitters and apparatus for detecting reflected radar energy; other emitters of electromagnetic radiation and apparatus for detecting reflected electromagnetic radiation. Such controllers may include motor or guide control apparatus, control surface controllers, camera controllers, or controllers programmed to power up, operate, data, manage, and configure any of the foregoing sensors. Examples are disclosed in US Patent Application No. 14/831,165 and the present disclosure assumes cognizance of that other patent disclosure.
In one embodiment, sensors 112 and controllers 114 may be attached to soil sampling and measurement apparatus that is configured or programmed to sample soil and perform soil chemistry tests, soil moisture tests, and other soil-related tests. . For example, the apparatus described in US Patent No. 8,767,194 and US Patent No. 8,712,148 can be used, and the present disclosure assumes knowledge of those patent disclosures.
In one embodiment, sensors 112 and controllers 114 may comprise weather devices for monitoring field weather conditions. For example, the apparatus described in US Provisional Application No. 62/154,207, filed April 29, 2015, US Provisional Application No. 62/175,160, filed June 12, 2015, Provisional Application from the USA no. 62/198,060, filed July 28, 2015 and United States Provisional Application No.
62/220,852, filed September 18, 2015, may be used, and the present disclosure assumes knowledge of those patent disclosures.
2.4. GENERAL DESCRIPTION OF THE PROCESS: TRAINING OF THE AGRONOMIC MODEL
In one embodiment, agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in the memory of the agricultural intelligence computer system 130 comprising field data 106, such as identification data and harvest data for one or more fields. The agronomic model may also comprise calculated agronomic properties that describe conditions that may affect the growth of one or more crops in a field, or properties of one or more crops, or both. In addition, an agronomic model may comprise recommendations based on agronomic factors such as crop recommendations, irrigation recommendations, planting recommendations, fertilizer recommendations, fungicide recommendations, pesticide recommendations, harvest recommendations, and other crop management recommendations. Agronomic factors can also be used to estimate one or more crop-related outcomes, such as agronomic yield. The agronomic yield of a crop is an estimate of the amount of the crop that is produced or, in some examples, the income or profit earned from the crop produced.
In one embodiment, the agricultural intelligence computer system 130 may use a pre-configured agronomic model to calculate currently received location-related agronomic properties and crop information for one or more fields. The pre-configured agronomic model is based on pre-processed field data, including but not limited to identification data, harvest data, fertilizer data, and weather data. The pre-configured agronomic model may have been cross-validated to ensure model accuracy. Cross validation can include a comparison with field verification that compares predicted results with actual results in a field, such as a comparison of the precipitation estimate with a rain gauge or a sensor that provides weather data at the same location or at different locations. a nearby location or an estimate of nitrogen content with a soil sample measurement.
FIGURE 3 illustrates a programmed process whereby the agricultural intelligence computer system generates one or more pre-configured agronomic models using field data provided by one or more data sources. FIGURE 3 can serve as an algorithm or instructions for programming the functional elements of the agricultural intelligence computer system 130 to perform the operations now described.
At block 305, agricultural intelligence computer system 130 is configured or programmed to implement agronomic data pre-processing of field data received from one or more data sources. Field data received from one or more data sources can be pre-processed in order to remove noise, distorting effects and confounding factors within the agronomic data, including measured outliers that could negatively affect the values of the data. field data received. Modalities of pre-processing agronomic data may include, but are not limited to, removal of data values commonly associated with outliers, specific measured data points known to unnecessarily skew other data values, data smoothing, aggregation or sampling techniques used to remove or reduce the additive or multiplicative effects of noise and other data derivation or filtering techniques used to provide clear distinctions between positive and negative data inputs.
At block 310, the agricultural intelligence computer system 130 is configured or programmed to perform data subset selection using the previously processed field data to identify data sets useful for generating an initial agronomic model. The agricultural intelligence computer system 130 can implement data subset selection techniques including, but not limited to, a genetic algorithm method, a subset all models method, a sequential search method, a regression method in stages, 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 data sets within previously processed agronomic data.
At block 315, agricultural intelligence computer system 130 is configured or programmed to implement field data evaluation. In one embodiment, a specific field data set is evaluated by creating an agronomic model and using specific quality thresholds for the created agronomic model. Agronomic models can be compared and/or validated using one or more comparison techniques, such as, but not limited to, leave-one-out cross-validated root mean square error (RMSECV), mean absolute error, and mean percent error. For example, RMSECV can cross-validate agronomic models by comparing the predicted agronomic property values created by the agronomic model with the collected and analyzed historical agronomic property values. In one embodiment, the agronomic data set evaluation logic is used as a feedback loop where agronomic data sets that do not meet configured quality thresholds are used during future data subset selection steps (block 310 ).
In block 320, the agricultural intelligence computer system 130 is configured or programmed to implement the creation of the agronomic model based on the cross-validated agronomic data sets. In one embodiment, agronomic modeling can implement multivariate regression techniques to model pre-configured agronomic data.
At block 325, the agricultural intelligence computer system 130 is configured or programmed to store the pre-configured agronomic data models for future field data evaluation.
2.5. IMPLEMENTATION EXAMPLE: HARDWARE OVERVIEW
In one embodiment, the techniques described herein are implemented by one or more special purpose computing devices. Special-purpose computing devices may be hardwired to perform the techniques, or they may include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general-purpose hardware processors programmed to perform the techniques in accordance with the program's instructions in firmware, memory, other storage, or a combination. Such special purpose computing devices may also combine custom hardwired, ASIO, or FPGA logic with custom programming to achieve the techniques. Special purpose computing devices may be desktop computing systems, portable computing systems, handheld devices, network devices, or any other device that incorporates programmed and/or hardwired logic to implement the techniques.
For example, FIGURE 4 is a block diagram illustrating a computer system 400 in which an embodiment of the invention may be implemented. Computer system 400 includes a bus 402 or other communication mechanism for communicating information, and a hardware processor 404 coupled with the bus 402 for processing information. Hardware processor 404 may be, for example, a general purpose microprocessor.
Computer system 400 also includes main memory 406, such as random access memory (RAM) or other dynamic storage device, coupled to bus 402 to store information and instructions to be executed by processor 404. Main memory 406 may It can also be used to store temporary variables or other intermediate information during the execution of instructions that will be executed by the processor 404. Such instructions, when stored in non-transient storage media accessible to processor 404, turn computer system 400 into a special purpose machine that is customized to perform the operations specified in the instructions.
Computer system 400 further includes a read-only memory (ROM) 408 or other static storage device coupled to bus 402 for storing static information and instructions for processor 404. A storage device 410, such as a magnetic disk, is provided. an optical disk, or a solid state drive and is coupled to bus 402 to store information and instructions.
Computer system 400 may be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT), to display information to a computer user. An input device 414, including alphanumeric and other keys, is coupled to bus 402 to communicate information and command selections to processor 404. Another type of user input device is cursor control 416, such as a mouse, trackball, or cursor arrow keys to communicate direction information and command selections to processor 404 and to control cursor movement on screen 412. This input device typically has two degrees of freedom in two axes, a first axis (for example, x) and a second axis (for example, y), which allows the device to specify positions in a plane.
Computer system 400 may implement the techniques described herein using custom hardwired logic, one or more ASIO or FPGA, firmware, and/or program logic that, in combination with the computer system, causes or programs computer system 400 to be a special purpose machine. In accordance with one embodiment, the techniques herein are performed by computer system 400 in response to processor 404 executing one or more sequences of one or more instructions contained in main memory 406. Such instructions may be read from main memory 406 from another storage medium, such as a storage device 410. Execution of the instruction sequences contained in main memory 406 causes processor 404 to perform the processing steps described herein. In alternate embodiments, hardwired circuitry may be used instead of or in combination with software instructions.
The term "storage medium", as used herein, refers to any non-transient medium that stores data and/or instructions that make a machine work in a specific way. Said storage media may comprise non-volatile and/or volatile media. Non-volatile media include, for example, optical disks, magnetic disks, or solid-state drives, such as the 410 storage device. Volatile media include dynamic memory, such as 406 main memory. Common forms of storage media include, for example, a floppy disk, floppy disk, hard drive, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other data storage medium, optical data, any physical media with hole patterns, a RAM, a PROM and EPROM, FLASH-EPROM, NVRAM, any other memory chip or cartridge.
Storage media are different but can be used in conjunction with streaming media. Transmission media participate in the transfer of information between storage media. For example, transmission media include coaxial cables, copper wires, and fiber optics, including the wires that comprise bus 402. The transmission media can also take the form of acoustic or light waves, such as those generated during data communications over radio waves and infrared.
Various forms of means may be involved for carrying one or more sequences of one or more instructions to processor 404 for execution. For example, the instructions may initially be carried on a magnetic disk or solid-state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 400 can receive the data on the telephone line and use an infrared transmitter to convert the data into an infrared signal. An infrared detector can receive the data carried in the infrared signal, and appropriate circuitry can place the data on bus 402. Bus 402 carries the data to main memory 406, from which processor 404 retrieves and executes the instructions. . Instructions received by main memory 406 may optionally be stored in storage device 410 before or after execution by processor 404.
Computer system 400 also includes a communication interface 418 coupled to bus 402. Communication interface 418 provides bidirectional data communication that is coupled to a network link 420 that is connected to a local network 422. For example, communication interface 418 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem for providing a data communication connection to a corresponding type of telephone line. As another example, communication interface 418 may be a local area network (LAN) card for providing a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation, communication interface 418 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.
Network link 420 typically provides data communication over one or more networks to other data devices. For example, the network link 420 may provide a connection through the local network 422 to a host computer 424 or to data equipment operated by an Internet service provider (ISP) 426. The ISP 426 in turn provides data communication services over the worldwide packet data communication network, now commonly referred to as the "Internet" 428. The local network 422 and the Internet 428 use electrical, electromagnetic, or optical signals that carry of digital data. The signals across the various networks and the signals on the network link 420 and through the communication interface 418, which carry the digital data to and from the computing system 400, are exemplary forms of transmission media.
The computer system 400 can send messages and receive data, including program code, through the network, the network link 420 and the communication interface 418. In the lA/a/zuzu/ui i auo example of the Internet, a server 430 may transmit a requested code for an application program over the Internet 428, ISP 426, local network 422 and communication interface 418.
The received code may be executed by processor 404 as received, and/or stored in storage device 410, or other non-volatile storage for later execution.
3. FUNCTIONAL DESCRIPTION
3.1 DATA COLLECTION
In some embodiments, server 170 is programmed or configured to collect cost data from user devices, such as a grow device or provider computer. To allow comparative analysis between different agricultural regions, cost data may include costs that often vary between different agricultural regions, such as costs associated with purchasing and planting hybrids from seeds, applying fertilizers, pesticides, or fungicides to the soil. or crops, or grain drying. Cost data may also include other costs that are more or less constant or evenly distributed among different agricultural regions, such as costs related to storage or certain agricultural cartridges.
In some embodiments, server 170 is programmed to request input of cost data. The application can be submitted at various points during an agricultural process. Cost data related to an item can usually be requested when other data related to the item is requested or entered. This makes it easier to associate the costs incurred in implementing an agricultural process in an agricultural region with other data related to the agricultural region, such as soil data or environmental data. Cost data can also be requested at a specific point within a stage of an agricultural process or cycle, such as at the end of the planting stage or one month after the harvest stage. In addition, cost data may be requested when a request to perform a Role analysis is received. It is generally preferable to receive cost data as soon as possible during the current growing season or earlier to allow for accurate calculations related to costs throughout a farming process.
In some embodiments, server 170 is programmed to receive cost data from farmer devices or directly from material or labor supplier devices. For example, server 170 may be configured to receive quotes for different seed hybrids in a catalog from a vendor system and receive a specification of seed hybrids planted on a farm with possible price discounts from a farmer's device, and the server 170 can be configured to then derive the actual purchase costs of the seed hybrids planted on the farm. The server 170 can be programmed to further allow a specification of costs in specific units and perform any necessary conversions. For example, to purchase a specific seed hybrid, the cost data received can be expressed as a price per bag and converted to a standard unit, such as the price per 1,000 seeds. Server 170 is programmed to have cost data expressed in standard units for each location of the agricultural regions of interest.
In some embodiments, server 170 is programmed or configured to request input of login data from user devices. The income data generally includes the market prices of the yields and is available at the time the yield data is available. The application is typically filed during the harvest stage of an agricultural cycle. Alternatively, revenue data may be requested when a request to perform a Role analysis is received. In general, it is preferable to receive revenue data as early as possible in the current growing season to allow accurate calculations of costs and benefits as soon as possible.
In some embodiments, server 170 is programmed to receive income data from farmer devices. The server 170 can be further programmed to allow a specification of income amounts in specific units and perform any necessary conversions. For example, to sell a specific seed hybrid, the revenue data received can be expressed as a price per liter and can be converted to a standard unit, such as a bushel price. Server 170 is programmed to have income data expressed in standard units for each location of the agricultural regions of interest.
3.2 ANALYSIS AND PRESENTATION OF DATA
In some embodiments, server 170 is programmed or configured to receive from a user device a selection of a plurality of types of analysis to perform or a plurality of types of reports to generate. The selection usually includes a specification of a duration to limit the scope of the analysis. Examples of duration include one or more years or agricultural seasons. The different types of analysis or reports are discussed in more detail below.
In some embodiments, server 170 is programmed to allow browsing, review, analysis, and/or manipulation of different types of agricultural data corresponding to an agricultural process implemented in one or more fields or regions. A user computer may specify one or more criteria to identify one or more regions or fields directly or indirectly. For example, the criteria may include the name or a field, the boundary of a region, the types of soils present, the types of seed hybrids planted, the moisture content, temperature, or other attributes of individual fields or regions.
In some embodiments, the server 170 is programmed to allow further exploration, review, analysis, and/or manipulation of different components of a Role analysis, including individual pieces of data that contribute to a Role analysis or analytical data that correspond to various interactions between individual data. Individual data may include costs of purchasing or planting hybrid seeds, collecting or supplying water, purchasing or applying fertilizers, pesticides, or fungicides, harvesting or drying grain bodies, as well as income from yields. Individual components may also include sizes, quantities, or other attributes of seeds, fertilizer, labor, or other items that cost money or produce income. The various interactions may include comparisons of different instances of an individual component across time, location, population, or other dimensions, as discussed below. The various interactions may also include calculations of different types of ROL. Each type of ROI would be the difference between revenue and a specific combination of cost components. For example, one type of ROI might be the difference between revenue and simply the cost of purchasing a particular hybrid seed, while another type of ROI might be the difference between revenue and all applicable costs incurred during a farming process. .
In some embodiments, server 170 is programmed to display a digitized graphical map of one or more fields using a computer display device and to overlay additional information on the map, which typically corresponds to a component of a Role analysis. In this way, the Rol analysis described here is not merely mathematical, but rather serves to drive a more efficient presentation of data that can be used to drive specific decisions in the field, such as seed or nutrient application. The map can be interactive or manipulative, allowing the user's computer to zoom in or out, move around, or specify a particular region. The overlay can be in the form of specific colors or shadings within certain regions, popups or dialogs on top of maps, etc. Overlay information can be limited to a specific location or applied to an entire region on the map.
In some embodiments, server 170 is programmed to display two maps simultaneously on the same screen. The two maps may have identical or similar features, but indicate specific content in response to user selections. A user computer can specify or change the type of information displayed on any map, dismiss a map, or re-invoke a map at any time. By controlling the types of information displayed on the two maps, a user's computer receives data that is more efficient or better at illustrating a causal relationship or other correlations between different components of a Role analysis or the overall progression of Role analysis. Role. For example, for each location in one or more fields, one of the maps may indicate yield data for the current year, and the other map may indicate overall ROI data for the current year. The two maps together more efficiently specify the effectiveness of determining the utility and profitability of certain seed hybrids based on yield data alone or based on overall ROI data that takes financials into account.
In some embodiments, the server 170 is programmed to perform various types of comparative analysis and present the analysis results through one of the interactive maps or other means of displaying data or on plain media. In a first type, data with respect to one or more chosen regions is presented with respect to data with respect to a predetermined region or another region further specified by the user computer or with respect to certain aggregate data. For example, yield data for a grower's field may be presented relative to the average yield of all fields in a geographic region encompassed by the grower's field. In a second type, the data regarding several chosen regions is presented in original values but on the same screen to facilitate comparison. For example, Role data for several fields of a producer can be presented on the same screen. In a third type, data for one or more selected regions are presented in relation to a previous season or a previous stage of an agricultural cycle. For example, the costs incurred in a grower's field during the current season thus far may be presented relative to the costs incurred in the grower's field during the previous season. In a fourth type, cost data and revenue data for one or more chosen regions are presented on the same screen. In a fifth type, different types of cost data for one or more selected regions are presented on the same screen. For example, the costs incurred in preparing the soil of a farmer's field, in planting and in irrigating the field, respectively, can be presented on the same screen. In a sixth type, data is presented differently with respect to one or more chosen regions and with respect to all other regions in a field. For example, given a criteria specified by the user computer of an ROI of more than a certain amount, those regions that have an ROI above that certain amount are displayed in a hue, while those regions that have an ROI at or below a certain amount they are displayed in another hue. Other types of comparative analysis can be performed along specific dimensions or at certain granularities and the results can be presented in a similar way.
In some embodiments, the server 170 is programmed to perform additional types of trend or correlation analysis and present the analysis results through one of the interactive maps or other data display means or on plain media. Such trend or correlation analysis can be performed using known machine learning techniques such as decision trees, regression analysis, or neural networks. For any given data set, server 170 can be programmed to identify a stronger or weaker part and have them highlighted on the display. For example, the top 5% and bottom 5% of returns may be displayed differently than the rest of the performance data. Server 170 can also be programmed to detect patterns or trends and generate predictions accordingly. For example, given cost data and revenue data for a field, server 170 may be configured to determine what types of costs may be highly correlated with ROI and send those findings to a user device. As a further example, given yield data and crop or soil treatment data for a field over several seasons, server 170 can be configured to calculate a yield consistency and determine which of one or more types of treatments might have been a primary contributor to performance consistency.
In some embodiments, server 170 is programmed to use the results of the trend or correlation analysis to generate recommendations for user computers or adjust interaction with user computers. For example, based on a determination that the cost of a certain type of fungicide fluctuates greatly from year to year, server 170 may be configured to recommend reducing the use of that fungicide to reduce overall risk. For example, based on a determination of low variability in the cost of drying corn within a specific geographic area, the server 170 may be configured to reduce the number of requests from user devices for the cost of drying corn. for fields within that geographic area. Similarly, based on a detected growth rate of the cost of drying corn over the past few years, the server 170 can be configured to predict the cost for the next year and use it as the default value for the next year. For example, based on a determination that the ROI for a specific field remains stable under the same or similar soil or crop treatment over the years, the server 170 may be configured to recommend continued investment in those treatments.
In some embodiments, server 170 can be programmed to perform certain types of analysis or generate certain types of reports based on a specific time, such as the beginning of each stage of an agricultural cycle. The server 170 can be programmed to send further alerts or notifications to user devices when user- or system-defined trigger conditions are satisfied. An example of a trigger condition is a steady increase in a particular type of cost or a significant decrease in a certain ROI. Such alerts or notifications of certain problems may contain recommendations to remedy these problems and prompt user computers to adopt the recommendations or to explore, review, analyze and/or manipulate different components of the Role analysis before taking further action. For example, a recommendation might be to review the use of the type of agricultural data associated with the particular type of cost or consider certain alternatives on the market that typically cost less.
3.3 EXAMPLE OF PROCESSES
In some embodiments, the server 170 is programmed to cause the display of a graphical user interface on a computer display device, in which the digitized visual elements receive or represent financial and other data associated with the growers' fields and display various results of analysis of such data, including ROI information. As an example, each of FIGURE 7, FIGURE 8, FIGURE 9, FIGURE 10, FIGURE 11, FIGURE 12, FIGURE 13, FIGURE 14, FIGURE 15, FIGURE 16, FIGURE 17, and FIGURE 18 illustrate an example screen display of a graphical user interface that the server 170 can generate using modally arranged programs.
FIGURE 7 illustrates an example screen that is set up to receive cost data for different seed hybrids. The screen includes an option 708 that allows the addition of a new seed hybrid. This screen also includes a list of seed hybrids that have been added or submitted, with one row for each seed hybrid. For each hybrid, a name 702, a plant type 712, and a description 704 are shown. For example, the last seed hybrid on the list has a name of “DKC27-15”, a plant type of “corn”, and a description of “DEKALB”. The screen also includes an option 710 to remove each seed hybrid. In addition, the screen includes an option 706 to provide a price for each seed hybrid. The price can be entered for a specific unit, such as a certain dollar amount per 1,000 seeds. Other types of costs incurred in an agricultural process can be received in a similar way.
FIGURE 8 illustrates an example screen display that is configured to show a map of one or more fields with seed hybrid information. The screen includes an 808 option that allows the specification of one or more fields, such as by planting year and plant type. For example, option 808 can have a value or description of "corn 2016". The screen also includes an 802 option that allows you to specify a sorting of one or more fields. For example, option 802 can have a value of "hybrid", which causes one or more fields to display differently per seed hybrid. In addition, the display includes a rating legend 806 corresponding to the value of option 802. For example, the rating legend 806 may display the colors or shadings assigned to the two hybrids 213-26VT2PRIB ("213") and 21445DGVT2PRIB ("214 ”) that are present in one or more fields. The screen then includes a map 804 of one or more fields, with an overlay of the classification information. In this example, the two hybrids are planted alternately in one or more fields and are shown in different colors or shading based on the 806 grading legend. In certain embodiments, in response to a user selection of a location on the map, the display may display additional information, such as an actual value (the name of a seed hybrid in this example) associated with the selected location or a summary of all those actual values in the entire field encompassing the selected location.
FIGURE 9 illustrates a sample screen configured to display a map of one or more fields with planting cost information. This screen is similar to the screen illustrated in FIGURE 8 and can be displayed instead of or simultaneously with the screen illustrated in FIGURE 8, for example. The screen includes an option 908 that enables the specification of one or more fields, such as by planting year and plant type. For example, option 908 may have a value of "corn 2016". The screen also includes an option 902 that enables the specification of a classification of one or more fields. For example, option 902 can have a value of "seed cost", which causes one or more fields to display differently based on seed cost. In addition, the screen includes a rating legend 906 corresponding to the value of option 902. For example, the 906 rank legend can show the colors or shadings assigned to the six ranges of planting costs in dollars that may be present in one or more fields, i.e. >165.00, 146.25-165.00, 127.50-146.25, 108.75- 127.50, 90.00-108.75 and <90. The screen then includes a map 904 of one or more fields, with an overlay of the classification information. In this example, the two seed hybrids 213 and 214 are planted alternately as illustrated in FIGURE 8. These planting costs can be based on the prices entered through the screen illustrated in FIGURE 7. Specifically, the unit cost of the 213 seed hybrid is $260 and the unit cost of the 214 seed hybrid is $340. For each unit location on the map, the unit cost can be multiplied by the number of units used at the unit location to get the planting cost. The different planting costs for unit locations in one or more fields are then shown in different colors or shading according to the 906 classification legend. Other types of costs incurred in an agricultural process can be shown in a similar way.
FIGURE 10 illustrates an example screen configured to display a map of one or more fields with performance information. This screen is similar to the screen illustrated in FIGURE 8 and may be displayed instead of the screen illustrated in FIGURE 8 or FIGURE 9, for example. The screen includes two options 1008 and 1010 that allow a two-level specification of one or more fields. Option 1010 controls the first level, such as by geographic area, and option 1008 controls the second level below the first level, such as by year planted and a plant type within the geographic area. For example, option 1010 might have a value of "Dad's house west of house," and option 1008 might have a value of "corn 2016." The screen also includes an option 1002 that enables the specification of a classification of one or more fields. For example, option 1002 can have a value of "performance", which causes one or more fields to display differently for performance. In addition, the display includes a 1006 rating legend corresponding to the value of option 1002. For example, the 1006 rating legend can show the colors or shadings assigned to the nine bushels per 4,046 m yield ranges<sup>2</sup> (acre) that may be present in one or more fields. The screen then includes a map 1004 of one or more fields, with an overlay of the classification information. In this example, the two hybrid seeds 213 and 214 are planted alternately as illustrated in FIGURE 8. For each unit location on the map, the yield quantity can be converted to the specific unit of the number of bushels per 4,046m<sup>2</sup> (acre). The different yields for unit locations in one or more fields are shown in different colors or shading according to the 1006 classification legend.
FIGURE 11 illustrates an example screen configured to display a map of one or more fields and receive a request to enter pricing data for yield in one or more fields. The screen can be related to the screen illustrated in FIGURE 10 with respect to performances, using the information included on that screen as a background. This screen may include one or more options related to the returns, such as option 1102 that allows you to set a trading price for the returns. For example, the user computer can select a location on the map and the price can be specified for the seed hybrid used at the selected location.
FIGURE 12 illustrates an example screen configured to receive price data for performance in one or more fields. The screen can be related to the screen illustrated in FIGURE 10 with respect to performances, using the information included on that screen as a background. In addition, the screen may be presented in response to the selection of option 1102 illustrated in FIGURE 11. The display includes an option 1202 that allows you to specify a trading price for the yield at a specific unit, such as $3.25 per bushel.
FIGURE 13 illustrates an example screen configured to display a map of one or more fields with ROI information. This screen is similar to the screen illustrated in FIGURE 8 and can be simultaneously displayed as the screen illustrated in FIGURE 10, for example. The screen includes an option 1308 that allows the specification of one or more fields, such as by planting year and plant type. For example, option 1308 can have a value of "corn 2016". The screen also includes an option 1302 that allows the specification of a classification of one or more fields. For example, option 1302 can have a value of "seed return", which causes one or more fields to display differently for the cost of seed return. In addition, the display includes a rating legend 1306 corresponding to the value of option 1302. For example, the 1306 classification legend may show the colors or shadings assigned to the nine ranges of dollar return-to-plant costs that may be present in one or more fields. The screen then includes a map 1304 of one or more fields, with an overlay of the classification information. In this example, the two seed hybrids 213 and 214 are planted alternately as illustrated in FIGURE 8. These planting return costs can be based on the planting costs displayed on the screen illustrated in FIGURE 9, the yields displayed on the screen illustrated in FIGURE 10, and the marketing prices entered using the screen illustrated in FIGURE 9. FIGURE 12. Specifically, the return or benefit can be calculated as the product of the yield and the commercial price, and the return cost of planting can be calculated as the difference between the return and the planting cost. The different planting return costs for unit locations in one or more fields are shown in different colors or shading according to the 1306 classification legend. Such return cost of planting, which normally constitutes part of the total cost, indicates a relative return and can be especially useful for comparing the returns of different agricultural regions where the associated costs differ mainly in the cost of planting. Other types of returns corresponding to other types of costs can be visualized in a similar way.
FIGURE 14 illustrates an example screen configured to display a map of one or more fields and receive a request to receive a field region report. The screen can be related to the screen illustrated in FIGURE 10 with respect to performances, using the information included on that screen as a background. The display includes an option 1404 that allows selection of a field region report to focus on specific regions within one or more specified fields.
FIGURE 15 illustrates an example screen configured to display a map of one or more fields and receive a request to receive a specification of a region within one or more fields. The screen may be displayed in response to selecting option 1404 illustrated in FIGURE 14. The screen may be similar to the screen illustrated in FIGURE 8 and may be displayed in place of the screen illustrated in FIGURE 13 or FIGURE 14 , For example. The display allows the specification of a specific region within one or more specified fields by drawing a boundary 1502 of the specific region on the map, specifying the key coordinates of the specific region through separate graphical elements, etc. Specifying the specific region can be the result of touching the screen with your hand or a stylus, or interacting with the screen with a mouse. In response to the specification, the display may include a notification 1504 to generate a report for the specified region in real time.
FIGURE 16 illustrates a sample screen configured to display summary data for a specific region, including size, performance, and ROI information. The screen may be presented following the illustrated notification 1504 illustrated in FIGURE 15. The screen may be related to the screen illustrated in FIGURE 15, using the information included on that screen as a background. Summary data includes a general summary 1602 of size, yield, seed return, and moisture content for the specified region. The summary data also includes various statistics for the subregions of the specified region organized by different attributes. For example, the screen includes a section 1604 where statistics are displayed by hybrid, a section 1606 where statistics are displayed by soil, and a section 1608 where statistics are displayed by population. Within each section for each subregion, the display includes a size statistic 1610 in number of acres, an average yield statistic 1614 in number of bushels per 4,046 m<sup>2</sup> (acre) and a 1612 seed return cost statistic in a dollar amount. In addition, the display allows the user computer to drill down into each of the sub-regions. For example, the user's computer could focus more on the subregion where seed hybrid 214 is planted by clicking the 1616 link. In this example, while the average yield for seed hybrid 214 is greater than the average yield for seed hybrid 213, the total seed return for seed hybrid 214 is less than the total seed return for the 213 hybrid, showing that the 213 seed hybrid might be more desirable.
FIGURE 17 illustrates a sample screen set up to display summary data for a seed hybrid grown in a specific region, including size, yield, and ROI information. This screen is similar to the screen illustrated in FIGURE 16 but focuses on a specific sub-region. This screen may be presented in response to selecting link 1616 illustrated in FIGURE 16. The display includes a 1702 description of the subregion, such as the name of a selected seed hybrid when the subregion includes those locations in the specified region where the specific seed hybrid is planted.
FIGURE 18 illustrates a sample screen set up to display cost and revenue data for one or more fields. The screen includes two options 1816 and 1818 that allow one or more fields to be specified, such as by planting year and plant type, respectively. For example, option 1816 can have a value of “2016” and option 1818 can have a value of “corn”. The screen allows a selection 1804 of a plurality of cost levels of an agricultural process, such as the seed layer or the nitrogen layer. More generally, the plurality of cost levels can include any number of combinations of types of costs incurred in an agricultural process. The screen also offers a selection 1802 of a number of region classifications for input analysis, such as soil types, fields, or hybrids. For example, a selection of fields leads to an analysis of revenue or Role by field. The rest of the screen includes the results of the analysis of the selected cost levels against the revenue amounts for one or more specified fields. Specifically, the screen includes a summary 1820 of cost and revenue data for one or more fields, including a total number of fields, a total crop extent in number of acres, a total crop volume in number of bushels, and an average cost return as the difference between revenue and the selected planting cost level in a dollar amount. The screen also displays specific attributes for the subregions of one or more fields based on the classification of the selected region, such as by field. In this example, for each of the four fields, the display includes a row 1806 showing the name 1808, the average difference between revenue and cost level 1810 in a dollar amount, with an indicator of the average 1820 across all fields to make it easier to determine how the field average compares to the overall average, and an 1812 crop range across multiple acres.
FIGURE 19 illustrates an example process performed by the agricultural data management server for managing data related to an agricultural process. FIGURE 19 is intended to describe an algorithm, plan, or scheme that can be used to implement one or more computer programs or other software items that, when executed, cause the functional improvements and technical advances described in FIGURE 19 to be made. present. In addition, the flowcharts herein are described in the same level of detail that persons of ordinary skill in the art typically use to communicate with one another about algorithms, plans, or specifications that form a foundation for software programs they plan to code or implement using your accumulated skill and knowledge.
In step 1902, the server 170 is programmed or configured to cause the display of a first map of one or more agricultural fields. The first map may indicate for each location a first type of a plurality of agricultural data types associated with one or more agricultural fields. The plurality of types of agricultural data may include seed hybrid data, planting data, soil preparation data, fertilizer application data, irrigation data, harvest data, or grain drying data. While the user computer may initially request review of one of a number of types of agricultural data, such as finding out which seed hybrids were purchased and ultimately planted in one or more fields, the map may also indicate other types of data, such as as a component of a Role analysis, as discussed below.
At step 1904, server 170 is programmed or configured to receive cost data corresponding to a second of the plurality of agricultural data types associated with one or more agricultural fields. Cost data may be received as soon as a user device is capable of providing such data to server 170. The second type of farm data may be identical to the first type of farm data. In certain embodiments, the server 170 may be configured to allow the entry of multiple costs on the same screen, such as the costs of different seed hybrids or the costs of purchasing, planting, and harvesting a seed hybrid. In certain embodiments, server 170 may be configured to allow a user computer to select a location on the first map indicating that a particular seed hybrid is being purchased or planted for the selected location and provide a cost to purchase or plant the hybrid. of seeds.
At step 1906, server 170 is programmed or configured to receive income data associated with one or more agricultural fields. Income data is typically associated with harvested crops or yields, but represents a more precise “return” from the farming process than yields. In certain embodiments, server 170 may be programmed to cause the first map to be redisplayed to indicate performance data for each location in response to a user computer request. Then, similarly, server 170 may be configured to allow a user computer to select a location from the first map indicating that a certain crop is being harvested for the selected location and provide a trading price for selling the determined crop.
At step 1908, server 170 is programmed or configured to perform a Role analysis for one or more farm fields having a plurality of components, including cost data associated with a third type of the plurality of farm data types, the income data and the corresponding Role data. As noted above, the “return” in a Role analysis here is typically income rather than yield, investment could include costs associated with one or more types of agricultural data, and Role would be the difference between income and the investment. For example, when operations on two fields differ primarily in the seed hybrids being grown, comparative seed returns considering only the costs associated with purchasing or planting hybrid seeds may be useful. Other types of comparative analysis may highlight relevant advantages relative to specific benchmarks or aggregates that correspond to specific geographic areas or time periods.
At step 1910, server 170 is programmed or configured to cause a second map of one or more agricultural fields to be displayed on the screen simultaneously with the first map. The second map can indicate for each location a first component of the plurality of components of the Role analysis. For example, after reviewing yield data in one or more fields through the first map, a grower might be interested in knowing what the seed returns are in one or more fields. The second map can then indicate the return of the seed for each location. Simultaneous display of the first map and the second map make it easy to compare and contrast yields and returns to seeds and understand the impact of incorporating financial details.
At step 1912, server 170 is programmed or configured to receive a selection of points from the second map, the selection corresponding to a region boundary within one or more agricultural fields. The input from a user computer can freely specify the boundary, so the region can include any part of any of one or more fields. For example, the user computer can focus on a group of locations on the second map that indicate low seed yields to better understand what may have caused the low yields. For example, when the second map indicates a higher seed hybrid yield for one row in the middle of the field and a lower seed hybrid yield for another row near a field edge, the user's computer can You may want to focus on these rows to better understand what might have led to the different performances.
At step 1914, the server 170 is programmed or configured to cause the display of a report indicating the first component of the region-specific Role analysis. The server 170 can be configured to overlay the report on the second map to allow easy return to the second map. The server 170 may include in the report similar types of information as the second map but with different granularities. For example, where the second map indicates seed return data for each location, the report may indicate seed return data for the specific region according to certain classifications, such as soil type, seed hybrid, or population. Thus, the report allows the user computer to delve into different aspects of the specified region of interest.
In other embodiments, one or more of the steps illustrated in FIGURE 19 are performed by other computing devices, such as field management computing device 104 or cockpit computer 115. For example, field management computing device 104 it can be programmed to perform these steps or at least a Role analysis based on local data and subsequently communicate the results of performing these steps to the server 170.
4. EXTENSIONS AND ALTERNATIVES
In the above specification, the embodiments of the invention have been described with reference to many specific details that may vary from one implementation to another. Accordingly, the specification and figures are to be considered in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what the applicants intend the scope of the invention to be, is the literal and equivalent scope of the set of claims issued from this application, in the specific form in which said claims are issued, including any subsequent corrections.
Contents29
20 sheets
Sheet 1 Sheet 2 Sheet 3 Sheet 4 Sheet 5 Sheet 6 Sheet 7 Sheet 8 Sheet 9 Sheet 10 Sheet 11 Sheet 12 Sheet 13 Sheet 14 Sheet 15 Sheet 16 Sheet 17 Sheet 18 Sheet 19 Sheet 20
16 members in 10 offices
Priority claims3
| Document | Office | Kind | Date |
|---|---|---|---|
| 15976574 | United States of America | – | |
| 201815976574 | United States of America | A | |
| 2019031340 | United States of America | W |
Members16
| Document | Office | Kind | |
|---|---|---|---|
| CA3099552A1 | Canada | A1 | |
| US2019347745A1 | United States of America | A1 | |
| WO2019217568A1 | World Intellectual Property Organization (WIPO) | A1 | |
| US10755367B2 | United States of America | B2 | |
| AR114898A1 | Argentina | A1 | |
| AU2019265649A1 | Australia | A1 | |
| CN112106087A | China | A | |
| MX2020011908AThis record | Mexico | A | |
| BR112020022715A2 | Brazil | A2 | |
| US2021042858A1 | United States of America | A1 | |
| EP3791343A1 | European Patent Office (EPO) | A1 | |
| CA3099552C | Canada | C | |
| ZA202007133B | South Africa | B | |
| EP3791343A4 | European Patent Office (EPO) | A4 | |
| CN112106087B | China | B | |
| US12051121B2 | United States of America | B2 |
Numbers
- Publication
- 2020011908
- Application
- 2020011908
Titles2
- Spanish
- ANÁLISIS Y PRESENTACIÓN DE DATOS AGRÍCOLAS
- English
- ANALYSIS AND PRESENTATION OF AGRICULTURAL DATA
Classification
- CPC, 4
- G06Q40/12
- G06Q50/02
- A01B79/005
- G06F16/29
- IPC, 3
- G06Q10 06
- G06Q10 08
- G06Q50 02