Real-time agricultural recommendations using weather sensing on equipment

Abstract

One technical area of ​​the present disclosure is computer-implemented agricultural data processing. Another technical area is computer-implemented real-time localized weather data collection and its use in agricultural operations to provide continuous monitoring of spraying effectiveness, automatic creation of field buffers to prevent product application under conditions that would adversely affect adjacent crops or fields, automatic generation of regulatory reports, and automatic drift management.

Description

[0001] Copyright Notice

[0002] A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. The Climate Corporation. Technical Field

[0003] One technical area of ​​the present disclosure is computer-implemented agricultural data processing. Another technical area is computer-implemented collection of real-time localized weather data and the use of weather data in agricultural operations. Background Art

[0004] 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 indicated, it should not be assumed that any approach described in this section qualifies as prior art merely by virtue of its inclusion in this section.

[0005] Many agricultural operations are performed in outdoor fields and may be significantly affected by local weather conditions. Examples of operations include spraying, side dressing, and sowing. For these operations, the wind and precipitation conditions in the fields can determine whether the operation can be performed and whether adverse effects such as seed drift, spray drift, or insufficient application of materials to crops occur. Most weather data available to growers are collected on a regional basis and include forecasts. This data is not enough to determine whether an operation can be started, or whether an ongoing operation occurs under optimal conditions.

[0006] Manufacturers of certain spray products, such as pest control agents or herbicides, provide product labels that define recommended conditions for applying these products. Product labels often specify maximum allowable wind speeds to avoid drift or misapplication, as well as recommended nozzle pressures, spray volumes, heights above the crop canopy, or other factors that may affect product effectiveness. Summary of the Invention

[0007] The following claims may serve as a summary of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In the attached figure:

[0009] Figure 1 An example computer system configured to perform the functions described herein is illustrated, the example computer system being shown in a field environment along with other devices with which the system may interoperate.

[0010] Figure 2 Illustrated are two views of an example logical organization of a set of instructions in main memory when an example mobile application is loaded for execution.

[0011] Figure 3 Illustrated is a programmed process by which an agricultural intelligence computer system generates one or more preconfigured agronomic models using agronomic data provided by one or more data sources.

[0012] Figure 4 is a block diagram illustrating a computer system upon which embodiments of the present invention may be implemented.

[0013] Figure 5 An example embodiment of a timeline view of data entries is depicted.

[0014] Figure 6 An example embodiment of a spreadsheet view of data entries is depicted.

[0015] Figure 7 is a simplified end elevation view of agricultural equipment including a tractor with a spray boom in an agricultural field illustrating a plurality of weather stations and proximity sensors secured to the equipment.

[0016] Figure 8 is a simplified data flow diagram of the functional elements of a distributed electronic system for collecting weather data and proximity data on agricultural equipment.

[0017] Figure 9 is a simplified hardware architecture diagram of the functional elements of a distributed electronic system for collecting weather data and proximity data on agricultural devices.

[0018] Figure 10 An example computer-implemented process for updating an in-cab operator display based on localized collection of real-time weather data is illustrated.

[0019] Figure 11 An example graphical user interface for a cab computer generated during operation is illustrated, showing the area of ​​a field that has been covered by a sprayer, wind speed and direction at the time of application, temperature, humidity, and other data.

[0020] Figure 12 An example graphical user interface for a cab computer is illustrated showing a map of wind speeds across an entire field while applying product generated after application or operation is complete.

[0021] Figure 13 An example computer-implemented process for generating an in-cab warning message in response to determining that localized real-time weather conditions exceed a threshold associated with a product or operation is illustrated.

[0022] Figure 14A An example computer-implemented process for automatically determining field buffer area dimensions based on localized real-time weather conditions during agricultural operations is illustrated.

[0023] Figure 14B An example graphical user interface for a cockpit computer is illustrated showing four (4) different dynamically generated buffers in the subviews labeled (1), (2), (3), (4).

[0024] Figure 15 An example computer-implemented process for automatically generating regulatory reports related to completed agricultural operations is illustrated.

[0025] Figure 16 Illustrated is an integrated data processing system programmed to provide automatic drift control for agricultural equipment while operating in a field based on local real-time weather data. DETAILED DESCRIPTION

[0026] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent that embodiments can be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form to avoid unnecessarily obscuring the present disclosure. The embodiments are disclosed in various sections according to the following outline:

[0027] 1. General Overview

[0028] 2. Example Agricultural Intelligent Computer System

[0029] 2.1. Structural Overview

[0030] 2.2. Application Overview

[0031] 2.3. Data Ingestion by Computer Systems

[0032] 2.4 Process Overview—Agronomic Model Training

[0033] 2.5 Implementation Example—Hardware Overview

[0034] 3. Example system integrating local real-time weather data in agricultural operations

[0035] 3.1 Example Hardware and Software Configuration

[0036] 3.2 Example Operator Display Update

[0037] 3.3 Automatic monitoring of operational effectiveness

[0038] 3.4 Automatic creation of field buffers

[0039] 3.5 Automatic Generation of Regulatory Reports

[0040] 3.6 Automatic Drift Management

[0041] 4. Practical Application

[0042] 5. Benefits of Certain Embodiments

[0043] *

[0044] 1. Overview

[0045] In one embodiment, an agricultural device operable in an agricultural field includes one or more digital electronic weather stations secured to the device and, optionally, one or more GPS receivers and / or proximity sensors, each coupled to a mobile computing device, such as a cab computer. The weather station transmits data representing wind speed, temperature, and / or other weather parameters measured on the device to the mobile computing device. Under control of program logic, the mobile computing device continuously compares the real-time, current weather data received from the weather station with programmed or configured thresholds relevant to the current agricultural operation. If the weather data indicates weather conditions exceeding one of the thresholds, a warning message may be generated at the mobile computing device prompting the operator to confirm whether to proceed with the operation. Post-operation wind maps support assessment of the effectiveness of the operation. Other embodiments may provide for continuous monitoring of spraying effectiveness, automatic creation of field buffers to prevent product application under conditions that would adversely affect adjacent crops or fields, automatic generation of regulatory reports, and automated drift management.

[0046] 2. Example Agricultural Intelligent Computer System

[0047] 2.1 Structural Overview

[0048] Figure 1 An example computer system configured to perform the functions described herein is shown in a field environment along with other devices with which the system can interoperate. In one embodiment, a user 102 owns, operates, or controls a field manager computing device 104 in or associated with a field location, such as a field intended for agricultural activities or a management location for one or more agricultural fields. The field manager computing device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.

[0049] Examples of field data 106 include (a) identification data (e.g., number of acres, field name, field identifier, geographic identifier, boundary identifier, crop identifier, and any other suitable data that can be used to identify farm land, such as Common Land Units (CLUs), lot and plot numbers, parcel numbers, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, zone number, field number, region, township, and / or range), (b) harvest data (e.g., crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, actual production history (APH), expected yield, yield, crop price, crop income, grain moisture, tillage practices, and previous growing season information), (c) soil data (e.g., type, composition, pH, organic matter (OM), cation exchange capacity (CEC)), (d) planting data (e.g., planting date, seed type(s), relative maturity (RM) of the seed(s) planted, seed population), (e) fertilizer data (e.g., nutrient type(s), nitrogen, phosphorus, potassium), application type, application date, amount, source, method), (f) chemical application data (e.g., pesticides, herbicides, fungicides, other substances or mixtures of substances intended for use as plant regulators, defoliants, or desiccants, application date, amount, source, method), (g) irrigation data (e.g., application date, amount, source, method), (h) weather data (e.g., precipitation, rainfall rate, predicted rainfall, water runoff rate area, temperature, wind, forecast, pressure, visibility, clouds, heat index, dew point, humidity, snow depth , air quality, sunrise, sunset), (i) imagery data (e.g., imagery and spectral information from agricultural device sensors, cameras, computers, smartphones, tablets, unmanned aerial vehicles, aircraft, or satellites; (j) reconnaissance observations (photos, videos, free-form annotations, voice recordings, voice transcriptions, weather conditions (temperature, precipitation (current and long-term), soil moisture, crop growth stage, wind speed, relative humidity, dew point, dark layer)), and (k) soil, seed, crop phenology, pest and disease reports, and forecast sources and databases.

[0050] Data server computer 108 is communicatively coupled to agricultural intelligence computer system 130 and is programmed or configured to transmit external data 110 to agricultural intelligence computer system 130 via network(s) 109. External data server computer 108 may be owned or operated by the same legal person or entity as agricultural intelligence computer system 130, or by a different person or entity, such as a government agency, non-governmental organization (NGO), and / or private data service provider. Examples of external data include weather data, image data, soil data, or statistics related to crop yields. External data 110 may consist of the same type of information as field data 106. In some embodiments, external data 110 is provided by external data server 108 owned by the same entity that owns and / or operates agricultural intelligence computer system 130. For example, agricultural intelligence computer system 130 may include a data server specifically focused on data types that may otherwise be obtained from third-party sources, such as weather data. In some embodiments, external data server 108 may be incorporated into system 130.

[0051] Agricultural equipment 111 may have one or more remote sensors 112 affixed thereto. These sensors are communicatively coupled, directly or indirectly, to agricultural intelligence computer system 130 via agricultural equipment 111 and are programmed or configured to transmit sensor data to agricultural intelligence computer system 130. Examples of agricultural equipment 111 include tractors, combines, harvesters, planters, trucks, fertilizer spreaders, aerial vehicles including unmanned aerial vehicles, and any other physical machinery or hardware that is generally mobile and can be used for tasks associated with agriculture. In some embodiments, a single unit of equipment 111 may include multiple sensors 112 locally coupled to a network on the equipment; a controller area network (CAN) is an example of such a network that can be installed in combines, harvesters, sprayers, and cultivators. Application controller 114 is communicatively coupled to agricultural intelligence computer system 130 via network(s) 109 and is programmed or configured to receive one or more scripts from agricultural intelligence computer system 130 that are used to control the operating parameters of agricultural vehicles or implements. For example, a controller area network (CAN) bus interface can be used to support communication from the agricultural intelligence computer system 130 to the agricultural device 111, such as how the CLIMATEFIELDVIEW DRIVE available from CLIMATE, Inc. of San Francisco, California is used. The sensor data can consist of the same type of information as the field data 106. In some embodiments, the remote sensor 112 may not be fixed to the agricultural device 111, but rather may be located remotely in the field and may communicate with the network 109.

[0052] The apparatus 111 may include a cab computer 115 programmed with a cab application, which may include a version or variation of a mobile application for the device 104, which is further described in other sections herein. In one embodiment, the cab computer 115 comprises a compact computer, typically a tablet-sized computer or smartphone, with a graphical screen display (such as a color display) mounted in the operator's cab of the apparatus 111. The cab computer 115 may implement some or all of the operations and functions further described herein for the mobile computer device 104.

[0053] The network(s) 109 broadly represent any combination of one or more data communication networks including a local area network, a wide area network, an interconnected network, or the Internet, using any of wired or wireless links including terrestrial links or satellite links. The network(s) may be provided by Figure 1 The data exchange between the various elements can be achieved by any medium or mechanism. Figure 1 The various elements of the system may also have direct (wired or wireless) communication links. The sensors 112, controller 114, external data server computer 108, and other elements of the system each include an interface compatible with the network(s) 109 and are programmed or configured to communicate across the network using standardized protocols such as TCP / IP, Bluetooth, CAN protocols, and higher layer protocols such as HTTP, TLS, etc.

[0054] The agricultural intelligence computer system 130 is programmed or configured to receive field data 106 from the field manager computing device 104, external data 110 from the external data server computer 108, and sensor data from the remote sensors 112. The agricultural intelligence computer system 130 may also be configured to host, use, or execute one or more computer programs, other software elements, digitally programmed logic (such as an FPGA or ASIC), or any combination thereof, to perform conversion and storage of data values, construction of digital models of one or more crops on one or more fields, generation of recommendations and notifications, and generation of scripts and sending of scripts to the application controller 114 in the manner further described in other sections of this disclosure.

[0055] In one embodiment, the agricultural intelligence computer system 130 is programmed to have or include a communication layer 132, a presentation layer 134, a data management layer 140, a hardware / virtualization layer 150, and a model and field data repository 160. In this context, "layers" refer to any combination of electronic digital interface circuits, microcontrollers, firmware such as drivers, and / or computer programs or other software elements.

[0056] The communication layer 132 may be programmed or configured to perform input / output interface functions, including sending requests for field data, external data, and sensor data, respectively, to the field manager computing device 104, the external data server computer 108, and the remote sensors 112. The communication layer 132 may be programmed or configured to send received data to the model and field data repository 160 for storage as the field data 106.

[0057] The presentation layer 134 may be programmed or configured to generate a graphical user interface (GUI) to be displayed on the field manager computing device 104, the cab computer 115, or other computer coupled to the system 130 via the network 109. The GUI may include controls for inputting 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.

[0058] The data management layer 140 may 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 that are transferred 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 include a database. As used herein, the term "database" may refer to a body of data, a relational database management system (RDBMS), or both. As used herein, a database may include 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 stored in a computer system. Examples of an RDBMS include, but are not limited to MYSQL, SERVER, and POSTGRESQL databases. However, any database that supports the systems and methods described herein may be used.

[0059] When the field data 106 is not provided directly to the agricultural intelligent computer system via one or more agricultural machines or agricultural machine devices that interact with the agricultural intelligent computer system, the user can be prompted to enter such information via one or more user interfaces on a user device (served by the agricultural intelligent computer system). In an example embodiment, the user can specify the identification data by accessing a map on a user device (served by the agricultural intelligent computer system) and selecting a specific CLU that has been graphically shown on the map. In an alternative embodiment, the user 102 can specify the identification data by accessing a map on a user device (served by the agricultural intelligent computer system 130) and drawing a field boundary on the map. Such CLU selection or map drawing represents a geographic identifier. In an alternative embodiment, the user can specify the identification data by accessing field identification data from the United States Department of Agriculture Farm Service Agency or other sources (provided in a shape file or similar format) via a user device, and provide such field identification data to the agricultural intelligent computer system.

[0060] In an exemplary embodiment, the agricultural intelligence computer system 130 is programmed to generate and cause the display of a graphical user interface including a data manager for data entry. After one or more fields have been identified using the methods described above, the data manager can provide one or more graphical user interface widgets that, when selected, can identify changes to the fields, soils, crops, tillage, or nutrient practices. The data manager can include a timeline view, a spreadsheet view, and / or one or more editable programs.

[0061] Figure 5 An example embodiment of a timeline view of data entries is depicted. Figure 5 , the user computer can input a selection of a particular field and a specific date for event addition. The events depicted at the top of the timeline can include nitrogen, planting, practices, and soil. To add a nitrogen application event, the user computer can provide input to select the nitrogen tag. The user computer can then select a location on the timeline for the particular field to indicate nitrogen application on the selected field. In response to receiving the selection of a location on the timeline for the particular field, the data manager can display a data entry overlay allowing the user computer to enter data regarding nitrogen application, planting procedures, soil application, tillage procedures, irrigation practices, or other information related to the particular field. For example, if the user computer selects a portion of the timeline and indicates nitrogen application, the data entry overlay can include fields for entering the amount of nitrogen applied, the date of application, the type of fertilizer used, and any other information related to the nitrogen application.

[0062] In one embodiment, the data manager provides an interface for creating one or more programs. In this context, a "program" refers to a collection of data about nitrogen applications, planting procedures, soil applications, tillage procedures, irrigation practices, or other information that may be related to one or more fields, which data can be stored in a digital data storage device for reuse as a collection in other operations. After a program has been created, it can be conceptually applied to one or more fields, and a reference to the program can be stored in the digital storage device in association with the data identifying the fields. Thus, instead of manually entering identical data related to the same nitrogen application for multiple different fields, a user computer can create a program that indicates a specific application of nitrogen, and then apply the program to multiple different fields. For example, in Figure 5 In the timeline view of the data, the top two timelines have selected a "Spring Application" program that includes applying 150 pounds of nitrogen per acre (150 lbs N / ac) in early April. The data manager can provide an interface for editing programs. In one embodiment, when a particular program is edited, each field for which the particular program has been selected is edited. For example, in Figure 5 , if the "Spring Application" program is edited to reduce the nitrogen application to 130 pounds of nitrogen per acre, the top two fields can be updated to have the reduced nitrogen application based on the edited program.

[0063] In one embodiment, in response to receiving an edit to a field for which a program has been selected, the data manager removes the field from the selected program. For example, if nitrogen application is added to Figure 5 If a field at the top of the application is not applied to the field, the interface may be updated to indicate that the "Spring Application" program is no longer applied to the field at the top of the application. Although the early April nitrogen application may remain, the update to the "Spring Application" program will not change the April nitrogen application.

[0064] Figure 6 An example embodiment of a spreadsheet view of data entries is depicted. Figure 6 As shown in the display, the user can create and edit information for one or more fields. Figure 6 As depicted in FIG, the data manager may include a spreadsheet for entering information about nitrogen, planting, practices, and soil. To edit a particular entry, the user computer may select the particular entry in the spreadsheet and update the value. For example, Figure 6 The updating of the target yield value for the second field is depicted. In addition, the user computer can select one or more fields for application of one or more programs. In response to receiving the program selection for a particular field, the data manager can automatically complete the entry for the particular field based on the selected program. Figure 1Likewise, in response to receiving an update to a particular program, the data manager can update the entry for each field associated with that program. Additionally, in response to receiving an edit to one of the entries for a field, the data manager can remove the selected program from that field.

[0065] In one embodiment, models and field data are stored in a model and field data repository 160. The model data includes data models created for one or more fields. For example, a crop model may include a digitally constructed model of crop development on one or more fields. In this context, a "model" refers to an electronic digital storage of associated executable instructions and data values ​​that is capable of receiving a program or other digital call, invocation, or resolution request and responding to the program or other digital call, invocation, or resolution request based on specified input values ​​to produce one or more stored or calculated output values, which can serve as the basis for computer-implemented recommendations, output data displays, or machine controls. Those skilled in the art find it convenient to express models using mathematical equations, but this form of expression does not limit the models disclosed herein to abstract concepts; rather, each model herein has practical application in a computer in the form of stored executable instructions and data that implement the model using a computer. Models may include models of past events on one or more fields, models of the current state of one or more fields, and / or models of predicted events for one or more fields. Model and field data may be stored in data structures in memory, in rows in a database table, in flat files or spreadsheets, or in other forms of stored digital data.

[0066] In one embodiment, each of the communication layer 132, presentation layer 134, localized weather processing logic 136, and data management layer comprises a collection 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 agricultural intelligence computer system to perform the functions or operations described herein with reference to those modules. For example, the localized weather processing logic is programmed to perform cloud-based processing of localized real-time weather data collected during agricultural operations and useful for upstream processing, such as report generation based on field data, landscape data, protected area data, cloud-based weather data and tag data, integration of international weather datasets and similar applications, generation of digital field maps showing wind drift, and automatic drift management calculations. The instructions may be in machine-executable code in the instruction set of the CPU and may be 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 scripts in JavaScript, other scripting languages, and other programming source text. The term "page" is intended to broadly refer to any area within main memory, and the specific term used in the system may vary depending on the memory architecture or processor architecture. In another embodiment, each of the communication layer 132, presentation layer 134, localized weather processing logic 136, and data management layer 140 may also represent one or more files or projects of source code that are digitally stored in a mass storage device such as non-volatile RAM or disk storage, stored in the agricultural intelligence computer system 130 or a separate repository system, and which, when compiled or interpreted, cause the generation of executable instructions that, 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 figures may represent the manner in which a programmer or software developer organizes and arranges source code for later compilation into an executable file, or interpretation into bytecode or equivalent for execution by the agricultural intelligence computer system 130.

[0067] The hardware / virtualization layer 150 includes 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 devices such as disks, and other devices, components, or elements of a computer system, such as a memory or nonvolatile memory device, a memory or nonvolatile storage device such as a disk, and a memory or nonvolatile storage device, such as a memory or nonvolatile storage device, in combination with, for example, a memory or nonvolatile storage device. Figure 4 Layer 150 may also include programmed instructions configured to support virtualization, containerization, or other technologies.

[0068] For the purpose of illustrating a clear example, Figure 1A limited number of instances of certain functional elements are shown. However, in other embodiments, there may be any number of such elements. For example, an embodiment may use thousands or millions of different mobile computing devices 104 associated with different users. In addition, the system 130 and / or the external data server computer 108 may be implemented using two or more processors, cores, clusters, or instances of physical or virtual machines, configured in discrete locations or co-located with other elements in a data center, shared computing facility, or cloud computing facility.

[0069] 2.2. Application Overview

[0070] In one embodiment, implementation of the functions described herein using one or more computer programs or other software elements loaded into and executed using one or more general-purpose computers will result in the general-purpose computers being configured as specific machines or computers specifically adapted to perform the functions described herein. In addition, each of the flowcharts further described herein may, alone or in combination with the descriptions of the processes and functions described herein, serve as an algorithm, plan, or direction that can be used to program a computer or logic to implement the functions described. In other words, all prose text herein and all appended Figure 1 It is intended that, combined with the skill and knowledge of persons having a level of skill appropriate to such invention and disclosure, disclosure of an algorithm, plan or direction sufficient to allow the skilled person to program a computer to perform the functions described herein be provided.

[0071] In one embodiment, user 102 interacts with agricultural intelligence computer system 130 using a field manager computing device 104 configured with an operating system and one or more application programs or apps. Field manager computing device 104 can also independently and automatically interoperate with the agricultural intelligence computer system under program control or logic control, and does not always require direct user interaction. Field manager computing device 104 broadly represents one or more of a smartphone, PDA, tablet computing device, laptop computer, desktop computer, workstation, or any other computing device capable of transmitting and receiving information and performing the functions described herein. Field manager computing device 104 can communicate via a network using mobile applications stored on field manager computing device 104, and in some embodiments, the device can be coupled to sensors 112 and / or controllers 114 using cables 113 or connectors. User 102 can own, operate, or otherwise control and use more than one field manager computing device 104 at a time in conjunction with system 130.

[0072] The mobile application can provide client-side functionality to one or more mobile computing devices via a network. In one example embodiment, the field manager computing device 104 can access the mobile application via a web browser or a local client application or app. The field manager computing device 104 can transmit and receive data to and from one or more front-end servers using network-based protocols or formats (such as HTTP, XML, and / or JSON) or app-specific protocols. In one example embodiment, the data can take the form of requests to the mobile computing device and user information input (such as field data). In some embodiments, the mobile application interacts with location tracking hardware and software on the field manager computing device 104, which uses standard tracking technologies such as multilateration of radio signals, Global Positioning System (GPS), WiFi positioning systems, or other mobile positioning methods to determine the location of the field manager computing device 104. In some cases, location data or other data associated with the device 104, user 102, and / or user account(s) can be obtained by querying the device's operating system or requesting an app on the device to obtain data from the operating system.

[0073] In one embodiment, the field manager computing device 104 transmits field data 106 to the agricultural intelligence computer system 130. The field data 106 includes or contains, but is not limited to, data values ​​representing one or more of the following: the geographic location of one or more fields, farming information for one or more fields, crops grown in one or more fields, and soil data extracted from one or more fields. The field manager computing device 104 may transmit the field data 106 in response to user input from the user 102, where the user input 102 specifies data values ​​for one or more fields. Additionally, the field manager computing device 104 may automatically transmit the field data 106 when one or more of the data values ​​become available to the field manager computing device 104. For example, the field manager computing device 104 may be communicatively coupled to remote sensors 112 and / or application controllers 114, including irrigation sensors and / or irrigation controllers. In response to receiving data instructing the application controller 114 to release water onto one or more fields, the field manager computing device 104 may send field data 106 indicating that water has been released onto the one or more fields to the agricultural intelligence computer system 130. The field data 106 identified in this disclosure may be input and transmitted using electronic digital data that is transmitted between computing devices using parameterized URLs over HTTP or another suitable communication or messaging protocol.

[0074] A commercial example of a mobile application is CLIMATE FIELDVIEW, commercially available from CLIMATE, Inc. of San Francisco, California. The CLIMATE FIELDVIEW application or other applications may be modified, expanded, or adapted to include features, functionality, and programming that have not been disclosed prior to the filing date of this disclosure. In one embodiment, the mobile application includes an integrated software platform that allows growers to make fact-based decisions about their operations because the platform combines historical data about the grower's fields with any other data the grower wishes to compare. The combination and comparison can be performed in real time and based on a scientific model that provides potential scenarios to allow growers to make better, more informed decisions.

[0075] Figure 2 Figure 1 shows two views of an example logical organization of an instruction set in main memory when an example mobile application is loaded for execution. Figure 2 In FIG, each named element represents a region of one or more pages of RAM or other main memory or a region of 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), the mobile computer application 200 includes account field data ingestion and sharing instructions 202, summary and alert instructions 204, digital map book instructions 206, seed and planting instructions 208, nitrogen instructions 210, weather instructions 212, field health instructions 214, and performance instructions 216.

[0076] In one embodiment, the mobile computer application 200 includes account, field, data ingestion, and sharing instructions 202 that are programmed to receive, convert, 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, third-party native data formats, and / or Farm Management Information System (FMIS) exports, among others. Receiving data may occur via manual upload, email with attachments, an external API that pushes data to the mobile application, or instructions that call an external system's API to pull data into the mobile application. In one embodiment, the mobile computer application 200 includes a data inbox. In response to receiving a selection of the data inbox, the mobile computer application 200 may display a graphical user interface for manually uploading data files and importing the uploaded files into the data manager.

[0077] In one embodiment, the digital map book instructions 206 include a field map data layer stored in the device memory and are programmed with data visualization tools and geospatial field annotations. This provides growers with convenient information at their fingertips for reference, logging, and visual insight into field performance. In one embodiment, the overview and alert instructions 204 are programmed to provide an operational scope view of what is important to the grower and provide timely suggestions for taking action or focusing on specific issues. This allows growers to focus their time where attention is needed, saving time and maintaining yields 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 enables growers to maximize yield or return on investment through optimized seed purchases, placement, and populations.

[0078] In one embodiment, script generation instructions 205 are programmed to provide an interface for generating scripts, including variable rate (VR) fertility scripts. This interface enables growers to create scripts for field applications, such as nutrient application, planting, and irrigation. For example, the planting script interface may include a tool for identifying the seed type to be planted. In response to receiving a seed type selection, mobile computer application 200 may display one or more fields divided into management zones, such as a field map data layer created as part of digital map book instructions 206. In one embodiment, the management zones include soil zones and a panel identifying each soil zone, along with soil name, texture, drainage, or other field data for each zone. Mobile computer application 200 may also display tools for editing or creating such zones, such as a graphical tool for drawing management zones (such as soil zones), over the 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, mobile computer application 200 may make the script available in a format readable by the application controller, such as an archived or compressed format. Additionally and / or alternatively, the script may be sent from the mobile computer application 200 directly to the cab computer 115 and / or uploaded to one or more data servers and stored for future use.

[0079] In one embodiment, the nitrogen instructions 210 are programmed to provide tools for informing nitrogen decisions by visualizing the availability of nitrogen to crops. This enables growers to maximize yields or return on investment through optimized nitrogen applications during the season. Example programmed functions include displaying images (such as SSURGO images) to enable plotting of fertilizer application zones and / or images generated from sub-field soil data (such as data obtained from sensors) at high spatial resolution (down to millimeters or less, depending on the proximity and resolution of the sensors); uploading existing grower-defined zones; providing graphs of plant nutrient availability and / or maps that enable adjustment of nitrogen application(s) across multiple zones; exporting scripts to drive machinery; tools for mass data entry and adjustment; and / or maps for data visualization, etc. In this context, "mass data entry" can mean entering data once and then applying the same data to multiple fields and / or zones defined in the system; example data can include nitrogen application data that is the same for many fields and / or zones of the same grower, but such mass data entry is applicable to entering any type of field data into the mobile computer application 200. For example, nitrogen instructions 210 can be programmed to accept definitions of nitrogen application programs and nitrogen practice programs, and to accept user input specifying the application of those programs across multiple fields. In this context, a "nitrogen application program" refers to a named set of stored data associated with: a name, color code, or other identifier; one or more application dates; the type of material or product used for each of the dates and amounts; the method of application or incorporation (such as injection or broadcasting); and / or the amount or rate of application for each of the dates; the crop or hybrid to which the application was applied; and the like. In this context, a "nitrogen practice program" refers to a named set of stored data associated with: a practice name; a previous crop; a tillage system; a primary tillage date; one or more previous tillage systems used; and one or more indicators of the type of application used (such as organic fertilizer). Nitrogen instructions 210 can also be programmed to generate and cause the display of a nitrogen map indicating the plant's planned use of a specified nitrogen and whether a surplus or shortage is predicted; for example, in some embodiments, different color indicators can indicate the magnitude of the surplus or shortage. In one embodiment, the nitrogen map comprises a graphical display in a computer display device including: a plurality of rows, each row being associated with and identifying a field; data specifying what crops are planted in the field, the field size, the field location, and a graphical representation of the field perimeter; within each row, a monthly timeline with a graphical indicator specifying each nitrogen application and amount at a point associated with the month name; and a numerical and / or colored surplus or shortage indicator, where the color indicates the magnitude.

[0080] In one embodiment, the nitrogen map may include one or more user input features (such as a dial or slider) to dynamically change the nitrogen planting and practice program so that the user can optimize their nitrogen map. The user can then use their optimized nitrogen map and the associated nitrogen planting and practice program to implement one or more scripts, including a variable rate (VR) fertility script. The nitrogen instructions 210 can also be programmed to generate and cause the display of a nitrogen map that indicates the plant's planned use of a specified nitrogen and whether a surplus or shortage is predicted; in some embodiments, different colored indicators can mark the magnitude of the surplus or shortage. Using numerical and / or colored surplus or shortage indicators, the nitrogen map can display the plant's predicted use of a specified nitrogen and whether a surplus or shortage is predicted for different times in the past and future (such as daily, weekly, monthly, or yearly), with the color indicating the magnitude. In one embodiment, the nitrogen map may include one or more user input features (such as a dial or slider) to dynamically change the nitrogen planting and practice program so that the user can optimize their nitrogen map, such as to obtain a preferred amount of surplus to shortage. 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 nitrogen instructions 210 can be used for the application of other nutrients (such as phosphorus and potassium), the application of pesticides, and irrigation programs.

[0081] In one embodiment, weather instructions 212 are programmed to provide field-specific recent weather data and forecasted weather information. This enables growers to save time and have an integrated display that is efficient in making daily operational decisions.

[0082] In one embodiment, field health instructions 214 are programmed to provide timely remote sensing imagery to highlight seasonal crop changes and potential problems. Example programmed functions include: cloud checking to identify possible clouds or cloud shadows; determining nitrogen index based on field imagery; graphical visualization of scouting layers, including, for example, layers related to field health, and viewing and / or sharing scouting notes; and / or downloading satellite imagery from multiple sources and prioritizing imagery for growers.

[0083] In one embodiment, the performance instructions 216 are programmed to provide reports, analysis and insight tools that use farm data for evaluation, insight and decision making. This enables growers to seek improved results for the coming year through fact-based conclusions about why the return on investment is at previous levels and insights into yield limiting factors. The performance instructions 216 can be programmed to communicate to a back-end analysis program via (multiple) networks 109, which is executed at the agricultural intelligence computer system 130 and / or the external data server computer 108 and is configured to analyze metrics such as yield, yield differences, hybrids, populations, SSURGO zones, soil test attributes or altitude. Programmed reports and analyses can include yield variability analysis, treatment impact estimates, benchmarking of yields and other metrics against other growers based on anonymous data collected from many growers, or data for seeds and plantings, etc.

[0084] An application with instructions configured in this manner can be implemented for different computing device platforms while maintaining the same general user interface appearance. For example, a mobile application can be programmed for execution on a tablet, smartphone, or server computer accessed using a browser at a client computer. Furthermore, a mobile application configured for a tablet or smartphone can provide a complete app experience or in-cab app experience that is suitable for the display and processing power of the in-cab computer 115. For example, referring now to Figure 2For view (b), in one embodiment, cab computer application 220 may include map cab instructions 222, remote view instructions 224, data collection and transmission instructions 226, machine alert instructions 228, script transmission instructions 230, and scout cab instructions 232. The code base for the instructions for view (b) can be the same as that for view (a), and the executable files implementing the code can be programmed to detect the type of platform on which these executable files are executing and expose only those functions appropriate for the cab platform or full platform through the graphical user interface. This approach enables the system to identify distinct user experiences appropriate for the in-cab environment and the different technical environments of the cab. Map cab instructions 222 can be programmed to provide a map view of a field, farm, or region useful in guiding machine operations. Remote view instructions 224 can be programmed to initiate, manage, and provide views of machine activity in real time or near real time to other computing devices connected to system 130 via a wireless network, wired connector, adapter, or the like. Data collection and transmission instructions 226 can be programmed to initiate, manage, and transmit data collected at sensors and controllers to system 130 via a wireless network, a wired connector, or an adapter. Machine alert instructions 228 can be programmed to detect operational issues with a machine or implement associated with the cab and generate operator alerts. Script transmission instructions 230 can be configured to transmit instruction scripts configured to direct machine operation or data collection. Scout cab instructions 232 can be programmed to display location-based alerts and information received from system 130 based on the location of the field manager computing device 104, agricultural implement 111, or sensor 112 in the field, and to capture, manage, and transmit location-based reconnaissance observations based on the location of the agricultural implement 111 or sensor 112 in the field to system 130.

[0085] 2.3. Data Ingestion by Computer Systems

[0086] In one embodiment, the external data server computer 108 stores external data 110, including soil data representing the soil composition for one or more fields and weather data representing the temperature and precipitation over the one or more fields. The weather data may include past and current weather data as well as forecasts for future weather data. In one embodiment, the external data server computer 108 includes multiple servers hosted by different entities. For example, a first server may contain soil composition data, while a second server may contain weather data. Additionally, the soil composition data may be stored on multiple servers. For example, one server may store data representing the percentage of sand, silt, and clay in the soil, while a second server may store data representing the percentage of organic matter (OM) in the soil.

[0087] In one embodiment, the remote sensor 112 includes one or more sensors that are programmed or configured to produce one or more observations. The remote sensor 112 can be an aerial sensor such as a satellite, a vehicle sensor, a planting equipment sensor, a tillage sensor, a fertilizer or pesticide application sensor, a harvester sensor, and any other device that can receive data from one or more fields. In one embodiment, the application controller 114 is programmed or configured to receive instructions from the agricultural intelligent computer system 130. The application controller 114 can also be programmed or configured to control the operating parameters of an agricultural vehicle or appliance. For example, the application controller can be programmed or configured to control the operating parameters of a vehicle (such as a tractor), planting equipment, tillage equipment, fertilizer or pesticide equipment, harvester equipment, or other farm appliances (such as water valves). Other embodiments can use any combination of sensors and controllers, the following are just selected examples.

[0088] System 130 can ingest data in bulk from a large number of growers who have contributed data to a shared database system under the control of user 102. This form of acquiring data can be referred to as "manual data ingestion" when one or more user-controlled computer operations are requested or triggered to acquire data for use by system 130. For example, the CLIMATE FIELDVIEW application commercially available from CLIMATE, Inc. of San Francisco, California, can be operated to export data to system 130 for storage in repository 160.

[0089] For example, the seed monitor system can both control planter assembly components and obtain planting data, including signals from seed sensors via a signal harness that includes a CAN backbone and point-to-point connections for registration and / or diagnostics. The seed monitor system can be programmed or configured to display seed spacing, population, and other information to a user via the cab computer 115 or other device within the system 130. Examples are disclosed in U.S. Patent No. 8,738,243 and U.S. Patent Publication 20150094916, and this disclosure assumes knowledge of those other patent disclosures.

[0090] Likewise, a yield monitor system may include yield sensors for harvester equipment that transmit yield measurement data to the cab computer 115 or other equipment within the system 130. The yield monitor system may utilize one or more remote sensors 112 to obtain grain moisture measurements in a combine or other harvester and transmit these measurements to a user via the cab computer 115 or other equipment within the system 130.

[0091] In one embodiment, examples of sensors 112 that can be used with any mobile vehicle or device of the type described elsewhere herein include kinematic sensors and positioning sensors. Kinematic sensors can include any speed sensor, such as a radar or wheel speed sensor, an accelerometer, or a gyroscope. Positioning sensors can include a GPS receiver or transceiver, or a WiFi-based positioning or mapping app programmed to determine location based on nearby WiFi hotspots, and the like.

[0092] In one embodiment, examples of sensors 112 that can be used with a tractor or other mobile vehicle include an engine speed sensor, a fuel consumption sensor, an area counter or distance counter that interacts with GPS or radar signals, a PTO (power take-off) speed sensor, a tractor hydraulic sensor configured to detect hydraulic parameters (such as pressure or flow) and / or hydraulic pump speed, a wheel speed sensor, or a wheel slip sensor. In one embodiment, examples of controllers 114 that can be used with a tractor include: a hydraulic directional controller, a pressure controller, and / or a flow controller; a hydraulic pump speed controller; a speed controller or governor; a hitch alignment controller; or a wheel alignment controller that provides automatic steering.

[0093] In one embodiment, examples of sensors 112 that can be used with seed planting equipment such as a planter, seed drill, or air seeder include: a seed sensor, which can be an optical, electromagnetic, or impact sensor; a downforce sensor, such as a load pin, load sensor, pressure sensor; a soil property sensor, such as a reflectivity sensor, a moisture sensor, a conductivity sensor, an optical residue sensor, or a temperature sensor; a component operation standard sensor, such as a planting depth sensor, a downforce cylinder pressure sensor, a seed tray speed sensor, a seed drive motor encoder, a seed conveyor system speed sensor, or a vacuum sensor; or a pesticide application sensor, such as an optical or other electromagnetic sensor, or an impact sensor. In one embodiment, examples of controllers 114 that may be used with such seed planting equipment include: a toolbar fold controller, such as a controller for a valve associated with a hydraulic cylinder; a down force controller, such as a controller for a valve associated with a pneumatic cylinder, an airbag, or a hydraulic cylinder, the controller being programmed to apply down force to individual row units or the entire planter frame; a planting depth controller, such as a linear actuator; a metering controller, such as an electric seed meter drive motor, a hydraulic seed meter drive motor, or a swath control clutch; a hybrid selection controller, such as a seed meter drive motor, or programmed to selectively allow or prevent seed or air seed mixture from being delivered to or from a seed meter or a central bulk hopper; a metering controller, such as an electric seed meter drive motor or a hydraulic seed meter drive motor; a seed conveyor system controller, such as a controller for a belt seed delivery conveyor motor; a marking controller, such as a controller for a pneumatic or hydraulic actuator; or a pesticide application rate controller, such as a metering drive controller, an orifice size, or a positioning controller.

[0094] In one embodiment, examples of sensors 112 that can be used with tillage equipment include: a positioning sensor for an implement, such as a handle or disk; an implement positioning sensor for such an implement, the positioning sensor configured to detect depth, rake group angle, or lateral spacing; a downforce sensor; or a traction force sensor. In one embodiment, examples of controllers 114 that can be used with tillage equipment include a downforce controller or an implement positioning controller, such as a controller configured to control implement depth, rake group angle, or lateral spacing.

[0095] In one embodiment, examples of sensors 112 that can be used in conjunction with an apparatus for applying fertilizers, pesticides, fungicides, and the like (such as a fertilizer system on a planter, a subsoil fertilizer applicator, or a fertilizer sprayer) include: fluid system standard sensors, such as flow sensors or pressure sensors; sensors that indicate which sprinkler valves or fluid line valves are open; tank-associated sensors, such as level sensors; section or system-wide supply line sensors, or row-specific supply line sensors; or kinematic sensors, such as accelerometers placed on the sprayer boom. In one embodiment, examples of controllers 114 that can be used with such an apparatus include: pump speed controllers; valve controllers programmed to control pressure, flow, direction, PWM, and the like; or positioning actuators, such as for boom height, subsoil depth, or boom positioning.

[0096] In one embodiment, examples of sensors 112 that can be used with a harvester include: yield monitors such as impact plate strain gauges or position sensors, capacitive flow sensors, load sensors, weight sensors, or torque sensors associated with an elevator or auger, or optical or other electromagnetic grain height sensors; grain moisture sensors such as capacitive sensors; grain loss sensors including impact, optical, or capacitive sensors; header operation standard sensors such as header height sensors, header type sensors, deck clearance sensors, feeder speed, and reel speed sensors; separator operation standard sensors such as concave plate clearance, rotor speed, brake shoe clearance, or chaff screen 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 a harvester include: header operation standard controllers for elements such as header height, header type, deck clearance, feeder speed, or reel speed; separator operation standard controllers for characteristics such as concave plate clearance, rotor speed, brake shoe clearance, or chaff screen clearance; or auger controllers for position, operation, or speed.

[0097] In one embodiment, examples of sensors 112 that can be used with a grain cart include weight sensors, or sensors for auger positioning, operation, or speed. In one embodiment, examples of controllers 114 that can be used with a grain cart include controllers for auger positioning, operation, or speed.

[0098] In one embodiment, examples of sensors 112 and controller 114 may be installed in an unmanned aerial vehicle (UAV) device or "drone." Such sensors may include a camera with a detector that is effective in any range of the electromagnetic spectrum, including visible light, infrared, ultraviolet light, near infrared (NIR), etc.; an accelerometer; an altimeter; a temperature sensor; a humidity sensor; a pitot tube sensor or other airspeed or wind speed sensor; a battery life sensor; or a radar transmitter and a device that detects reflected radar energy; or other electromagnetic radiation transmitters and devices that detect reflected electromagnetic radiation. Such controllers may include guidance or motor control devices, control surface controllers, camera controllers, or controllers programmed to activate, operate, obtain data from, manage, and configure any of the aforementioned sensors. Examples are disclosed in U.S. Patent Application No. 14 / 831,165, and this disclosure assumes knowledge of other patent disclosures.

[0099] In one embodiment, the sensor 112 and the controller 114 may be attached to a soil sampling and measuring device that is configured or programmed to sample soil and perform soil chemical testing, soil moisture testing, and other soil-related tests. For example, the devices disclosed in U.S. Patent Nos. 8,767,194 and 8,712,148 may be used, and this disclosure assumes knowledge of those patent disclosures.

[0100] In one embodiment, the sensors 112 and the controller 114 may include a weather device for monitoring weather conditions in the field. For example, the devices disclosed in U.S. Provisional Application No. 62 / 154,207, filed April 29, 2015, U.S. Provisional Application No. 62 / 175,160, filed June 12, 2015, U.S. Provisional Application No. 62 / 198,060, filed July 28, 2015, and U.S. Provisional Application No. 62 / 220,852, filed September 18, 2015 may be used, and this disclosure assumes knowledge of those patent disclosures.

[0101] 2.4. Process Overview - Agronomic Model Training

[0102] In one embodiment, the agricultural intelligent 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 intelligent computer system 130 that includes field data 106, such as identification data and harvest data for one or more fields. The agronomic model can also include calculated agronomic attributes, which describe the conditions that may affect the growth of one or more crops in the field or the characteristics of one or more crops, or both. In addition, the agronomic model can include suggestions based on agronomic factors, such as crop suggestions, irrigation suggestions, planting suggestions, fertilizer suggestions, fungicide suggestions, pesticide suggestions, harvest suggestions, and other crop management suggestions. Agronomic factors can also be used to estimate results related to one or more crops, such as agronomic yield. The agronomic yield of a crop is an estimate of the quantity of the produced crop, or in some examples, the income or profit obtained from the produced crop.

[0103] In one embodiment, the agricultural intelligence computer system 130 can use preconfigured agronomic models to calculate agronomic attributes related to the location and crop information of one or more fields currently received. The preconfigured agronomic models are based on previously processed field data, including but not limited to identification data, harvest data, fertilizer data, and weather data. The preconfigured agronomic models may have been cross-validated to ensure the accuracy of the models. Cross-validation can include a comparison with ground truth that compares the predicted results with the actual results on the field, such as comparing rainfall estimates with rain gauges or sensors that provide weather data for the same or nearby location, or comparing estimates of nitrogen content with soil sample measurements.

[0104] Figure 3 The diagram illustrates a programmed process by which an agricultural intelligence computer system generates one or more preconfigured agronomic models using field data provided by one or more data sources. Figure 3 It may serve as an algorithm or instruction for programming the functional elements of the agricultural intelligence computer system 130 to perform the operations now described.

[0105] At block 305, the agricultural intelligence computer system 130 is configured or programmed to implement agronomic data preprocessing of field data received from one or more data sources. The field data received from the one or more data sources may be preprocessed for the purpose of removing noise, distorting effects, and confounding factors within the agronomic data, including measurement outliers that may adversely affect the received field data values. Examples of agronomic data preprocessing may include, but are not limited to, removing data values ​​that are commonly associated with outlier data values, removing specific measurement data points that are known to unnecessarily skew other data values, data smoothing, aggregation, or sampling techniques used to remove or reduce additive or multiplicative effects from noise, and other filtering or data derivation techniques used to provide a clear distinction between positive and negative data inputs.

[0106] At block 310, the agricultural intelligence computer system 130 is configured or programmed to perform data subset selection using the preprocessed field data to identify datasets useful for initial agronomic model generation. The agricultural intelligence computer system 130 may implement data subset selection techniques, including but not limited to genetic algorithm methods, all subset model methods, sequential search methods, stepwise regression methods, particle swarm optimization methods, and ant colony optimization methods. For example, the genetic algorithm selection technique uses an adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics to identify and evaluate datasets within the preprocessed agronomic data.

[0107] At box 315, the agricultural intelligent computer system 130 is configured or programmed to implement field data set evaluation. In one embodiment, a specific field data set is evaluated by creating an agronomic model and using a specific quality threshold for the created agronomic model. One or more comparison techniques can be used to compare and / or validate the agronomic model, such as, but not limited to, leave-one-out cross-validation root mean square error (RMSECV), mean absolute error, and mean percentage error. For example, RMSECV can cross-validate the agronomic model by comparing the predicted agronomic attribute values ​​created by the agronomic model with the historical agronomic attribute values ​​that were collected and analyzed. In one embodiment, the agronomic data set evaluation logic is used as a feedback loop, where agronomic data sets that do not meet the configured quality threshold are used during future data subset selection steps (box 310).

[0108] At block 320 , the agricultural intelligence computer system 130 is configured or programmed to implement agronomic model creation based on the cross-validated agronomic data set. In one embodiment, the agronomic model creation may implement multivariate regression techniques to create a pre-configured agronomic data model.

[0109] At block 325 , the agricultural intelligence computer system 130 is configured or programmed to store the pre-configured agronomic data model for use in future field data evaluations.

[0110] 2.5 Implementation Example - Hardware Overview

[0111] According to one embodiment, the technology described herein is implemented by one or more special-purpose computing devices. Special-purpose computing devices can be hard-wired to perform these technologies, or can include digital electronic devices, such as one or more application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) that are permanently programmed to perform these technologies, or can include one or more general-purpose hardware processors that are programmed to perform these technologies according to program instructions with firmware, memory, other storage devices, or a combination. Such special-purpose computing devices can also combine customized hard-wired logic, ASICs, or FPGAs with customized programming to achieve these technologies. Special-purpose computing devices can be desktop computer systems, portable computer systems, handheld devices, networked devices, or any other devices that incorporate hard-wiring and / or program logic to implement these technologies.

[0112] For example, Figure 4 4 is a block diagram illustrating a computer system 400 upon which embodiments of the present 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 bus 402 for processing information. Hardware processor 404 may be, for example, a general-purpose microprocessor.

[0113] Computer system 400 also includes a main memory 406, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 402 for storing information and instructions to be executed by processor 404. Main memory 406 may also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. Such instructions, when stored in a non-transitory storage medium accessible to processor 404, render computer system 400 into a special-purpose machine customized to perform the operations specified in the instructions.

[0114] Computer system 400 also 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, optical disk, or solid-state drive, is provided and coupled to bus 402 for storing information and instructions.

[0115] The computer system 400 may be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT), for displaying information to a computer user. An input device 414, including alphanumeric and other keys, is coupled to bus 402 for communicating information and command selections to processor 404. Another type of user input device is a cursor control 416, such as a mouse, trackball, or cursor direction keys, for communicating direction information and command selections to processor 404 and for controlling cursor movement on display 412. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), which allows the device to be positioned in a specified plane.

[0116] Computer system 400 can implement the techniques described herein using custom hardwired logic, one or more ASICs or FPGAs, firmware, and / or program logic that, in combination with the computer system, makes computer system 400 a special-purpose machine or programs computer system 400 to be a special-purpose machine. According to 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 can be read into main memory 406 from another storage medium, such as storage device 410. Execution of the sequences of instructions contained in main memory 406 causes processor 404 to perform the process steps described herein. In alternative embodiments, hardwired circuitry can be used in place of software instructions or in combination with software instructions.

[0117] As used herein, the term "storage medium" refers to any non-transient medium that stores data and / or instructions that cause a machine to operate in a specific manner. Such storage media can include non-volatile media and / or volatile media. Non-volatile media include, for example, optical disks, magnetic disks, or solid-state drives, such as storage device 410. Volatile media include dynamic memory, such as main memory 406. Common forms of storage media include, for example, floppy disks, disks, hard disks, solid-state drives, magnetic tape or any other magnetic data storage medium, CD-ROMs, any other optical data storage medium, any physical medium with a pattern of holes, RAM, PROMs and EPROMs, FLASH-EPROMs, NVRAMs, any other memory chips, or cassette tapes.

[0118] Storage media are distinct from transmission media, but can be used in conjunction with them. Transmission media participate in the transfer of information between storage media. For example, transmission media include coaxial cables, copper wires, and optical fibers, including the wires that comprise bus 402. Transmission media can also take the form of acoustic or optical waves, such as those generated during radio wave and infrared data communications.

[0119] Various forms of media can be involved in 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 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 data on the telephone line and convert the data into an infrared signal using an infrared transmitter. 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. The instructions received by main memory 406 can optionally be stored on storage device 410 before or after execution by processor 404.

[0120] Computer system 400 also includes a communication interface 418 coupled to bus 402. Communication interface 418 provides two-way data communication coupled to network link 420, which is connected to local area network 422. For example, communication interface 418 can be an integrated services digital network (ISDN) card, a cable modem, a satellite modem, or a modem that provides a data communication connection to a corresponding type of telephone line. As another example, communication interface 418 can be a local area network (LAN) card that provides a data communication connection to a compatible LAN. A wireless link can also be implemented. In any such implementation, communication interface 418 sends and receives electrical signals, electromagnetic signals, or optical signals that carry digital data streams representing various types of information.

[0121] The network link 420 typically provides data communication through one or more networks to other data devices. For example, the network link 420 can provide a connection through a 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 through the global packet data communication network now commonly referred to as the "Internet" 428. Both the local network 422 and the Internet 428 use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 420 and through the communication interface 418 are example forms of transmission media that carry the digital data to and from the computer system 400.

[0122] Computer system 400 can send messages and receive data, including program code, through network(s), network link 420, and communication interface 418. In the Internet example, server 430 can transmit the requested code for an application program through Internet 428, ISP 426, local network 422, and communication interface 418.

[0123] The received code may be executed by processor 404 as it is received, and / or stored in storage device 410 or other non-volatile storage for later execution.

[0124] 3. Example system integrating local real-time weather data in agricultural operations

[0125] 3.1 Example Hardware and Software Configuration

[0126] Figure 7 is a simplified end elevation view of agricultural equipment including a tractor with a spray boom in an agricultural field, illustrating multiple weather stations and proximity sensors secured to the equipment. Figure 8 is a simplified data flow diagram of the functional elements of a distributed electronic system for collecting weather data and proximity data on agricultural equipment. Figure 9 This is a simplified hardware architecture diagram of the functional elements of a distributed electronic system for collecting weather data and proximity data on agricultural equipment. Figure 7 In one embodiment, an agricultural device 702, such as a tractor, may include an implement 704, such as a spray boom. A plurality of digital electronic weather stations 706 are secured to the device 702 and / or the implement 704. The location at which the weather stations 706 are secured to the device 702 is not critical, but is typically selected so as to provide a location free from wind deflection or other disruptive influences on the device; for example, mounting on the roof of the device cab and mounting on the end of the implement are suitable. Although for purposes of illustration and clarity of the example, Figure 7 Three (3) weather stations are shown, but in some other embodiments, fewer or more weather stations may be used. In cases where the distance from the implement to the crop 710 may be important for the proper completion of the agricultural operation, a plurality of proximity sensors 708 may optionally be affixed to the implement 704. Although for the purpose of illustrating the example clearly, Figure 7 Three (3) proximity sensors are shown, but in other embodiments, fewer or more sensors may be used.

[0127] In one embodiment, each weather station 706 includes one or more of a processor or microcontroller, memory, a data communication interface, a digital thermometer, an anemometer, a rain gauge, a humidity sensor, and / or other sensors for other weather parameters. In some embodiments, the weather station 706 includes a GPS receiver capable of receiving signals from Global Positioning System satellites in the sky above or within range of the device 702 and converting the signals into latitude and longitude values ​​or other geographic location data indicating the device's current geographic location. In other embodiments, the GPS receiver may be integrated into or coupled to a cab computer 115 separate from the weather station 706. The data communication interface of the weather station 706 may include a serial port connected via a wired cable to the cab computer 115 in or on the device 702. The weather station 706 may be programmed to continuously or periodically transmit data messages containing digital values ​​representing geographic location, temperature, wind speed, wind direction, air pressure, precipitation, humidity, and / or other weather parameters to the cab computer via the data communication interface. The specific weather parameters collected and provided to the cab computer may vary in different embodiments, and a specific combination of parameters is not mandatory. Because the weather station is on the device 702 and / or implement 704 and moves with the hardware during operation in the field, location, or environment, the weather parameters represent localized, real-time weather conditions occurring at the device and / or implement 704 while the device and / or implement 704 is operating in the field, other location, or environment.

[0128] In one embodiment, real-time action refers to the agricultural intelligent computer system performing one or more operations immediately after receiving input from one or more entities or within a few seconds of receiving the data. For example, executing real-time instructions to compensate for weather conditions in a field may include: receiving weather data that is local to a portion of the field from a weather station 706 via the agricultural intelligent computer system. The location of the appliance in the field may also be received. Immediately after receiving the input or a few seconds after receiving the input, the agricultural intelligent computer system can use the received data to determine one or more portions of the field that are experiencing conditions that are not ideal for treating the crop canopy. The system can then send a warning message to the operator of the appliance immediately or shortly thereafter.

[0129] In one embodiment, each of the proximity sensors 708 includes a microcontroller coupled to an ultrasonic transducer and a wireless networking interface with an antenna. The proximity sensors 708 can be programmed via firmware to transmit ultrasonic signals toward the crop 710, calculate or detect the distance from the instrument 704 to the top of the crop canopy via reflection of the signals, and wirelessly transmit a message containing a proximity value or proximity data to the cab computer 115, which has a compatible wireless networking interface. In this manner, the cab computer 115 is configured to continuously or periodically receive proximity data from the proximity sensors 708 while the device 702 is operating in a field, other location, or environment.

[0130] Now refer to Figure 8 , shows a simplified data flow diagram of one embodiment indicating the functional elements of a distributed electronic system for collecting weather data and proximity data on agricultural equipment. In this embodiment, the proximity sensors 708 each include an ultrasonic sensor coupled to a WiFi-enabled microprocessor and configured to output an analog voltage representing an altitude or proximity measurement, which is transmitted to a Message Queuing Telemetry Transport (MQTT) message broker 802. Although in Figure 8 MQTT is specified as an example, but other messaging protocols may be used in some other embodiments. The MQTT broker 802 may be executed as a process or thread in the cab computer 115 and programmed to queue messages received from hardware devices (such as proximity sensors 708 and weather stations 706) and forward the messages under program control to a message log or database 806 and / or to a telemetry display page 808, which may be shown on an in-cab operator display of the cab computer 115. The MQTT broker 802 may also be programmed to transmit locally collected weather and proximity data to a remote computer or server using a wireless communication protocol to update a cloud-based server or other remote system. Figure 8 As seen in FIG, weather station 706 may include a weather station that uses NMEA 0183 messaging via an RS-232 serial connection (i.e., AirMar 200WX) using the CAN bus of device 702 or a CAN bus independent of the device. Such serial data is coupled to NMEA to JSON converter 804, which packages the weather parameters in a JSON blob (binary large object) and transmits an MQTT message carrying the JSON blob to MQTT broker 802.

[0131] Now refer to Figure 9, shows an example of the hardware architecture of an embodiment. In this view, weather station 706 has a hardwired RS-232 serial connection to data agent 802, which is wirelessly coupled to a server computer in a cloud computing center represented by cloud 902, and to an operator display in the cab of the device. Proximity sensor 708 transmits an ultrasonic signal toward the crop canopy and generates a 4-20 mA analog output signal to a WiFi-enabled microprocessor, which is configured to wirelessly transmit data to data agent 802 via WiFi.

[0132] 3.2 Example Operator Display Update

[0133] Figure 10 The figure illustrates an example computer-implemented process for updating an in-cab operator display based on localized collection of real-time weather data. In one embodiment, after execution begins, at step 1001, the process loads or retrieves a configuration for one or more threshold values. In various embodiments, the threshold values ​​are received from an electronic storage device attached to the device 702. In various embodiments, the threshold values ​​are manually entered by an agricultural intelligence computer system or a human operator of the device 702. In various embodiments, the threshold values ​​are received from a cloud-based or remote system that stores the threshold values. A threshold value is a digitally stored value that represents the maximum acceptable conditions for performing a specified agricultural operation. For example, a threshold value may specify the maximum recommended wind speed for applying a spray product (such as a herbicide). A threshold value may specify temperature, humidity, or other values. The threshold value may be derived from product label data for an agricultural product. Step 1001 may include loading the threshold value from a configuration value, transmitting a query to a database, scanning a product label, or other operations. The specific program or electronic operation that occurs as step 1001 is not critical if, for example, the in-cab computer 115 obtains a basis for comparing real-time localized weather parameters with data relevant to the proper performance of a specific agricultural operation.

[0134] At step 1002, the process receives weather data from a device-mounted weather station. For example, the cab computer 115 receives a continuous stream of messages from the weather station 706 and / or proximity sensor 708 representing the current, real-time, localized weather conditions as measured at the device 702 where the cab computer is mounted. The communication of weather parameters may be as described herein with respect to Figure 7 、 Figure 8 、 Figure 9 Happened as described.

[0135] At step 1004, the process receives GPS positioning data indicating the current geographic location of the device having the computer executing the process. For example, GPS latitude and longitude data may be received from one of weather stations 706 or from another GPS receiver.

[0136] At step 1006, the process optionally receives proximity data from a sensor indicating a separation or distance of a device or implement from crops in an agricultural field. For example, step 1006 may include using a proximity sensor to receive a distance of a spray boom from the top of a crop canopy.

[0137] At step 1008, the process optionally wirelessly transmits one or more composite messages to a remote computer, such as a cloud-based server, for storage and processing. Step 1008 may include periodically packaging received localized real-time weather parameters, timestamp values ​​based on the local system clock of the cab computer 115, GPS latitude and longitude values, and / or proximity sensor values, and transmitting or uploading this data to the cloud-based server using a wireless network interface. Parameters may include temperature, barometric pressure, true wind speed, true wind direction, relative humidity, sprayer speed, distance from the boom to the soil or crop canopy, and the like. This step facilitates later analysis of data generated in the field as operations occur, logging, backup, and the like.

[0138] At step 1010 , the process updates an in-cab operator display showing one or more real-time values ​​of weather data parameters and / or GPS location and / or proximity data. Figure 11 FIG. 1 shows an example graphical user interface for the cab computer generated during operation, showing the area of ​​the field that the sprayer has covered, wind speed and direction at the time of application, temperature, humidity, and other data. In one embodiment, the cab computer 115 generates and displays, under program control, a touch screen graphical display device such as Figure 11 In one embodiment, the screen display 1102 includes a graphical field display 1104, an equipment icon 1106, a pass graphic 1108, a parameter panel 1110, a tool panel 1112, a wind speed legend 1114, a view tool 1116, and a wind direction indicator 1118. The graphical field display 1104 typically represents an agricultural field based on a satellite or aerial image of the field. The equipment icon 1106 represents the current location of the agricultural equipment 702 within the field; Figure 11 In the example shown, the device is moving from right to left. Passage graphic 1108 includes one or more graphically colored polygons representing all or part of one or more passes of device 702 through the field; in one embodiment, the color of the polygons within the passages represents the wind speed measured during the portion of the passage represented by the polygons, using colors corresponding to the wind speed values ​​shown in wind speed legend 1114. Furthermore, the dimensions and size of the polygons within the passages correspond to and are proportional to the actual area of ​​the field that device 702 has traversed.

[0139] The parameter panel 1110 includes a plurality of rectangular sub-panels, each of which displays one or more different physical parameters associated with agricultural operations, weather conditions, or the device 702. For example, in Figure 11 , parameter panel 1110 shows the application type (nitrogen); application rate value; temperature and humidity values; wind speed and wind direction values; relative wind direction and reverse risk values. Sub-panel 1120 includes an icon representing device 702 and an arrow showing the current wind direction relative to the device. The configuration of sub-panel 1120 enables the operator of device 702 to see the wind direction the operator is experiencing relative to the direction of travel of the device, rather than the absolute compass heading or direction. In contrast, the field display 1104 is typically arranged with compass north at the top, and a wind direction indicator 1118 indicates the actual wind direction measured while passing through a particular section, shown using the true compass heading or direction. For this reason, the orientation of the wind direction indicators in sub-panel 1120 and field map 1104 are different.

[0140] The tool panel 1112 includes a number of touch accessible display tools to change the size, scale or zoom level and arrangement of the screen display 1102; for example, the legend panel can be opened and closed or changed. The wind speed legend 1114 includes a set of color-coded wind speed values ​​that can be used to correlate the measured wind speed values ​​with the channel graph 1108. In some other embodiments, different types of legends for other weather parameters can be displayed, and wind speed is not required. For example, the display 1102 can show color coding or channel data based on temperature, humidity, or other parameters measured at the device 702. The view tool 1116 is programmed to enable the operator to select a specific field name, active year, and display type through touch input. In Figure 11 In the example shown, data and wind speed for 2019 have been selected. Selecting different values ​​using the view tool 1116 causes the cockpit computer 115 to generate a new version of the screen display 1102 and update the display device with the new view.

[0141] At step 1012, the process compares the values ​​of discrete parameters of the weather data and / or proximity data to the threshold values ​​obtained at step 1001. At step 1014, the process tests whether any of the parameters exceeds any of the threshold values. If so, then at step 1016, the operator display is updated with one or more warning messages or the like indicating that a threshold has been exceeded. Steps 1001, 1012, 1014, 1016 are programmed to generate a warning message to the operator based on a threshold-crossing algorithm; however, in some other embodiments, comparisons other than threshold crossings may be used to determine when a warning message should be generated. For example, the process can be programmed with rules that specify multiple comparisons of related parameters using any combination of arithmetic operators for comparisons, IF...THEN relationships, etc.

[0142] Reference again Figure 10 Optionally, after initiating execution, at step 1020 , input or commands from an operator may instruct the process to generate and display a wind map for the conditions under which the device is operating. Figure 12The figure illustrates an example graphical user interface for a cab computer, which shows a wind speed map generated after application or after the operation is completed, across an entire field. In one embodiment, graphical user interface 1202 includes a wind map 1204 comprised of a plurality of differently colored connected polygons that collectively represent all lanes or areas covered by a particular agricultural operation in field 1104. Each individual colored polygon within wind map 1204 represents wind speed measured in real time as device 702 traverses the portion of field 1104 represented by the polygon. In one embodiment, the colors of the polygons in the wind map are selected based on and correlated with the color values ​​and wind speed values ​​shown in wind speed legend 1114. The particular shade or color representing wind speed shown on the map can indicate wind speeds that fall within a certain range of wind speed values, as interpreted by wind speed legend 1114. For example, a portion of a field experiencing minimal wind can be shown in green to indicate the acceptability of working conditions in that portion of the field. Another portion of the field can be shown in red to indicate inefficient or unacceptable wind conditions. The color or shape of the polygon can change in real time as the wind conditions in the field change. For example, as wind speed increases, the agricultural intelligent computer system can determine that a portion of the field has become inoperable and change the color of the field to red. As an additional example, that portion of the field has a corresponding polygon that is changed to cover adjacent portions of the field that are experiencing similar conditions. In addition, interface 1202 includes multiple wind direction indicators 1118 that indicate the wind direction measured in real time when device 702 crosses the portion of field 1104 represented by the polygon closest to the wind direction indicator based on compass heading. In one embodiment, the wind direction indicators can be spaced apart in interface 1202 based on the time at which the wind direction value is received from weather station 706. Alternatively, the wind direction indicator can be shown only at points where the measured wind direction value represents a substantial change compared to the most recent previously measured wind direction value. The threshold used to indicate a substantial change may vary in different embodiments; one example is that a 5 degree difference in the compass heading of the wind direction may result in a new wind direction indicator being shown in the display.

[0143] use Figure 12 By using a display of the wind map 1204, an operator, supervisor, or another automated program or system can quickly determine the average wind speed during operation and / or the proportion of the field covered during acceptable wind conditions in terms of speed and direction. For example, the cab computer 115 or a separate computer or program can be programmed to examine the data values ​​forming the basis of the wind map 1204 to determine the proportion of the field covered within the parameters of a particular spray product and / or identify areas or regions that may have received too much product or not received enough product.

[0144] 3.3 Effectiveness of Automatic Monitoring Operations

[0145] Figure 13 FIGURE 1 illustrates an example computer-implemented process for generating an in-cab warning message in response to determining that localized real-time weather conditions exceed a threshold associated with a product or operation. Figure 13 In the process, steps 1001 and 1002 are as described in this article. Figure 10 At step 1302, the process updates an in-cab operator display showing one or more real-time values ​​of weather data parameters. Figure 11 The result of step 1302 is as follows Figure 11 The display is updated to show the current values ​​of temperature, wind speed, wind direction, etc. at the device 702 when the agricultural operation begins or is in progress.

[0146] At step 1304, the process tests whether an agricultural operation has begun. For example, the test of step 1304 may be true when the cab computer 115 has been started or restarted, or when operator input indicates the start of a new spraying operation or other agricultural operation. Step 1304 may also be true if the state of the cab computer 115 memory indicates that a value indicating the progress of the agricultural operation is not stored or is out of date. If step 1304 is true, then at step 1306, the operator display is updated with a request to confirm that the operation should proceed based on the then current weather data. For example, a dialog box or prompt may be superimposed on the Figure 11 1308. If a cancel input is given, the process exits at step 1310.

[0147] If a confirmation input is received at step 1308, or if the test at step 1304 is FALSE, then at step 1312 the process receives weather data from a weather station installed on the device during operation. Figure 7 、 Figure 8 、 Figure 9 The described process may be used to obtain weather data during operation of device 702 .

[0148] At step 1314, the process compares the values ​​of the discrete parameters of the weather data and / or proximity data to the threshold values ​​obtained at step 1312, and the process tests whether any of the parameters exceeds any of the threshold values. If so, then at step 1016, the operator display is updated with one or more warning messages or the like indicating that the threshold value has been exceeded, such as for Figure 10 As described above. While the example of generating a warning message to an operator based on an algorithm exceeding a threshold is shown, in other embodiments, comparisons other than exceeding a threshold can be used to determine when a warning message should be generated. For example, a process can be programmed with rules that specify multiple comparisons of related parameters using any combination of arithmetic operators for comparisons, IF...THEN relationships, etc. Figure 13 Step 1016 may also include executing a confirmation dialog in which the operator is prompted to confirm that the agricultural operation should continue or be canceled due to adverse weather conditions.

[0149] At step 1316, the process optionally transmits the warning message data to one or more supervisor accounts, other processes, or systems. Wireless transmission from the cab computer 115 to the cloud 902 may be used, or a more localized wireless transmission to another computer associated with another account, such as a field manager's location near the current field where the operation is occurring, may be used. Step 1316 can be programmed to allow the grower, farm or field manager, or other supervisor or account to receive data indicating that step 1314 generated the warning message at step 1016. This step can allow the remotely located supervisor to further assess whether the operation should continue and provide instructions via radio, cellular telephone, or other means to instruct the operator of the device 702 to continue, modify, or terminate the operation and / or scout or inspect the field or affected area for possible reapplication of the product. Reports and summaries can be generated to facilitate application quality checks, understanding which applications and fields are most affected and which operators are applying the product under incorrect conditions.

[0150] 3.4 Automatic creation of field buffers

[0151] Figure 14A An example computer-implemented process for automatically determining dimensions of field buffer areas based on localized real-time weather conditions during agricultural operations is illustrated. Figure 14B The figure shows an example graphical user interface of a cockpit computer showing four (4) different dynamically generated buffer zones in the sub-views labeled (1), (2), (3), (4). Figure 14AIn one embodiment, at step 1402, the process loads or retrieves field data for the field to be operated, including boundary data specifying the dimensions and locations of protected field elements. The field data may specify the field dimensions as a set of points and edges, and one or more protected field elements in the form of points and edges, to allow the cab computer to create an in-memory representation of the geometry of the field and protected elements. Examples of protected elements include adjacent fields or areas of crops that should be protected from spray drift or other impacts. In this context, an "area" may include a field management area that has been previously defined for other purposes using the cab computer 115 and / or other software applications of the cab computer or cloud 902.

[0152] Step 1402 may also include obtaining, from operator input, a stored configuration file, a database, or a query to a cloud-based resource, information regarding the operation to be performed or the product to be applied using the agricultural device 702. For example, step 1402 may include retrieving or obtaining product label data for the chemical being sprayed that indicates or represents allowable wind speed parameters to avoid product drift.

[0153] At step 1404, the process creates a digital representation of the buffer area and stores it in the memory of the cab computer. In some embodiments, step 1404 may include generating a display Figure 14B A graphical user interface display of the type seen in is provided to support operator visualization of the buffer area. Figure 14B , in various embodiments represented by views (1), (2), (3), and (4), field 1450 may be experiencing wind direction 1452 ( Figure 14B Buffer area 1454 may be defined in the stored data. Adjacent fields 1456, 1458, 1460 may require protection via a buffer zone.

[0154] Steps 1002, 1406, 1408, 1410, 1412, 1414 represent a loop that can be continuously executed in real time while the agricultural device 702 is performing operations in the field. At step 1002, the process receives weather data from one or more weather stations mounted on the agricultural device. Step 1002 can be as described for Figure 10 As described and used Figure 7 、 Figure 8 、 Figure 9The technique is executed. At step 1406, the process calculates the real-time impact of the prevailing wind conditions in terms of speed and direction on the application of the prevailing product or operation (such as the material being sprayed). At step 1408, the process may calculate one or more updated buffer areas based on the results of step 1406. For example, step 1408 may include expanding or reducing the size of a previously defined buffer area based on the impact calculated at step 1406; based on the prevailing GPS location of device 702, high wind speeds may require a much larger buffer area to prevent product from drifting into the buffer area, while low wind speeds may allow for a smaller buffer area to be observed.

[0155] At step 1410, GPS location data specifying the current location of the device 702 is received. At step 1412, the process tests whether the current location of the device 702 is near one or more of the buffer zones defined in memory. If so, at step 1414, the operator display is updated with one or more warning messages and a confirmation dialog is initiated. The operator display may be updated with a warning message indicating that the device is approaching or too close to one of the buffer zones, and a confirmation dialog is requested whether to continue or change the direction of the device 702 to avoid entering or approaching the buffer zone.

[0156] In this way, localized real-time weather data acquired on agricultural device 702 can be used to guide the device to avoid performing agricultural operations, including but not limited to spraying, in locations that would adversely affect operations and / or crops in the current field or other fields, areas, or regions.

[0157] 3.5 Automatic Generation of Regulatory Reports

[0158] Figure 15 An example computer-implemented process for automatically generating regulatory reports related to completed agricultural operations is illustrated.

[0159] At step 1502, the process queries one or more databases coupled to or managed by the cab computer 115 or cloud 902 to obtain operating parameters related to agricultural operations. In one embodiment, the operating parameters include average speed, total area, applicator name, and license number for each sprayer associated with the device 702 or with the field or grower. In other embodiments, other operating parameters may be obtained or used. For example, other operating parameters may include specifications for commercial product application, user input, or cloud-based input. Specifications for commercial product application may include specific instructions or metrics for commercial treatment products, such as, in some embodiments, a recommended application rate for a crop product, the viscosity of the liquid to be used in the sprayer, or a wind sensitivity metric associated with the product. In some embodiments, user input parameters may include the manual speed of the implement operated by the human user, a time period for treatment initiation, or other constraints on treatment operations performed by the human user. In some embodiments, cloud-based input may include one or more remotely stored metrics related to historical crop or treatment data or additional weather details.

[0160] At step 1504, the process obtains the temperature, wind speed, and wind direction values ​​at the start and end times of the spray application based on a database or log file generated from other processes. The database or log file may include, for example, Figure 10 、 Figure 13 、 Figure 14A The database or log files may be associated with the cab computer 115 or the cloud 902, or any other process that results in persistent storage of weather parameter values ​​that have been acquired during operation of the device 702 in the field.

[0161] At step 1506, the field boundary area is retrieved from the stored field data and the application area is calculated. Figure 14A The field data is obtained in the same manner as in step 1402. At step 1508, the registration number for each material or product is obtained. The registration number data can be used as a Figure 14A The registration number value may be obtained as part of the product data retrieved or acquired at step 1402. Alternatively, the registration number value may have been persistently stored in the memory of the cab computer 115 or in the cloud 902 using other applications or processes.

[0162] At step 1510 , a regulatory compliance report is generated and stored at the cab computer 115 and / or transmitted to the cloud 902 .

[0163] 3.6 Automatic Drift Management

[0164] Figure 16The figure illustrates an integrated data processing system programmed to provide automatic drift control for agricultural equipment while operating in a field based on local real-time weather data. Figure 16 An integration approach is represented where real-time localized weather data acquired at device 702 during operations in an agricultural field is integrated with other data that has been previously acquired and stored in conjunction with other program applications or processes.

[0165] In one embodiment, Figure 6 As seen in the figure, the agricultural intelligent computer system 130 ( Figure 1 ) is programmed with drift control instructions 1602 that can integrate multiple different data values ​​and provide drift control instructions to the cab computer 115. In one embodiment, the computer system 130 is coupled to or receives crop detail data 1602, field data 1604, surrounding landscape data 1606, protected area data 1608, product label data 1610, and weather forecast data 1612. In various embodiments, the crop detail data 1602, field data 1604, surrounding landscape data 1606, protected area data 1608, product label data 1610, and weather forecast data 1612 can be obtained from the field data 106 ( Figure 1 )、External data 110( Figure 1 ) or model and field data repository 160 ( Figure 1 ). Alternatively, product label data 1610 can be obtained by scanning a QR code on the product packaging and performing a database query to correlate the QR code with a set of product parameters. The specific location where the data is stored is not important as long as the computer system 130 can access and retrieve the data. The communication layer 132 facilitates the acquisition of this data from the field data 106, the external data 110, or the model and field data repository 160.

[0166] The technology herein can improve the efficiency of agricultural operations, such as increasing the efficiency of applying spray products. The technology herein can also improve regulatory compliance by providing equipment operators with information on how to avoid operating outside of parameters recommended by product manufacturers.

[0167] 4. Practical Application

[0168] In one embodiment, optimization of field treatment based on received field condition data is achieved by an agronomic or agricultural implement. The agricultural implement can traverse a field, applying treatment to crops in a specific manner that is modified based on the field condition data. For example, an agricultural intelligent computer system can be installed and executed on an agricultural implement such as a pesticide sprayer, which is designed to traverse a field while spraying pesticides to the crop canopy. The collected field condition data can be received, stored, and used in a manner that improves the operation of the sprayer over the standard method of traversing the field in a simple mode and guessing at the appropriate application of pesticides. For example, the agricultural intelligent computer system can use a variety of collected real-time data, including weather, GPS, proximity, and historical data, and then create or modify existing recommended operations for the sprayer based on the detection of hazardous conditions in the field, such as field crossing paths, spray angles, spray amounts, or complete cessation of vehicle operation.

[0169] In one embodiment, the agricultural intelligent computer system uses the received GPS data to determine the real-time location of the agricultural implement in the field. In various other embodiments, the agricultural intelligent computer system includes a display that can show an indication of the location of the agricultural implement in the field in real time. The indicator of the implement can be superimposed on an existing map of the field to show the current and / or previous path of the agricultural implement across the field. As a result, an operator using the display of the agricultural intelligent computer system can view the current and recent paths of the agricultural implement across the field, and can change future crossing paths based on the indications. For example, the operator of a spray truck can view the real-time path across the field to determine whether each zone or portion of the crop canopy in the field has been or will be subjected to the appropriate amount of pesticide spraying to ensure optimal crop growth. In this way, the agricultural intelligent computer actually applies the above-mentioned embodiments to eliminate inefficient guesswork methods for the expected path of field crossing.

[0170] In one embodiment, an agricultural intelligent computer system uses received weather data to optimize the treatment process of agricultural implements by determining perceived differences and inaccuracies in field crossing techniques. For example, converting liquid pesticides into a mist or spray for application to the crop canopy may be particularly sensitive to changes in wind speed or direction, and may affect the way the pesticide is applied to the field. In various embodiments, recommendations based on analysis of real-time weather data local to the agricultural implement can influence the operation of the agricultural implement. For example, an increase in detected "downwind" velocity may correspond to a greater spread of pesticide to the area of ​​crops downwind of the sprayer. As a result, the agricultural intelligent computer system can determine that a portion of the crop canopy intended to be treated during a particular field crossing will not receive the appropriate amount of treatment due to the significant amount of pesticide spread "downwind." Therefore, the agricultural intelligent computer system can warn the operator of the presence of unfavorable wind conditions and further recommend that the operator change the sprayer's route to a more "upwind" position of the particular crop canopy to ensure that the pesticide is properly applied to that portion of the crop canopy.

[0171] In one embodiment, an agricultural intelligent computer system uses received proximity data to optimize the treatment process of an agricultural implement by determining the distance between the treatment portion of the agricultural implement and the corresponding portion of the field. For example, the agricultural intelligent system may detect that the proximity sensor on a spray truck is a certain distance away from the crop canopy that will be the subject of the pesticide treatment. Weather conditions may affect how the treatment is applied to the field, which is subject to an error rate based on the proximity distance between the treatment implement and the portion of the field to be treated. For example, the agricultural intelligent system may determine that for every 5 mph gust of wind affecting a sprayer located 1 meter above the crop canopy, a certain sprayed pesticide will move 3 centimeters downwind, with an absolute error margin of 4 centimeters. Sensors on the spray truck may detect that the current weather conditions indicate a wind speed of 5 miles per hour during the spraying process, but the nozzle is within one meter of the crop canopy at the time of spraying. As a result, the agricultural intelligent computer system can determine that the conditions are not unsuitable for the application of the pesticide and can advise the operator to proceed with the spraying process normally.

[0172] In various embodiments, an agricultural implement can be any of a variety of vehicles used for crop processing or facilitating operations. In one embodiment, the implement is a tractor-drawn device that traverses a field, performing processing on the crop canopy in response to the conditions present in the field. For example, the device can be a fertilizer application device, and the efficiency of fertilizer application can be based on the relative humidity in the field. In one embodiment, the implement is a baler, and the baler's operational efficiency can depend on its ability to collect and bundle objects in the field based on current weather conditions. For example, on a windy day, crop collection and bundling may not be as efficient as on a calm day. In one embodiment, the implement is a combine harvester, and the harvesting process for certain crops may be affected by the current weather conditions in the field at the time of harvesting. For example, crops may be harvested more efficiently when they have a certain moisture content, which can be affected by the presence of rainfall during the harvesting process. In one embodiment, the implement is a plow, and the ability to cultivate the soil using the plow depends on the current state of weather conditions affecting the soil in the field being used to grow the crops. For example, when rainfall occurs during the tillage process, essential soil nutrients are more likely to be lost from the field. In one embodiment, the implement is a planter, and the operation of the planter and the ideal conditions for planting crop seeds in the field depend on the current weather conditions in the field. For example, high winds during the planting process may result in a more diverse seed scatter, thereby affecting future traversal patterns in the field.

[0173] 5. Benefits of Certain Embodiments

[0174] When considered as a whole in light of the description herein and its features, the present disclosure relates to improvements to weather, positioning and proximity data, and various other types of data, to determine optimal conditions and to recommend changes to processing procedures based on real-time local conditions. The present disclosure is not intended to cover or advocate abstract models for determining and comparing data, but rather is intended to provide practical applications of using computers to sense, store, and manipulate real-time local conditions for a field, and to change the manner in which operations or operation recommendations are sent to agricultural implements or operators of agricultural implements. By considering real-time processing conditions in the field obtained locally at the agricultural implement, the system is also able to improve the accuracy, reliability, and usability of processing models while preventing otherwise unaccounted-for complexities in field processing due to unstoppable weather changes. Therefore, implementations of the invention described herein can have tangible benefits in increasing the agronomic yield of crops, reducing resource expenditures while managing crops, and / or improving the crops themselves.

Claims

1. A computer-implemented method for providing crop treatment based on field conditions, the method comprising: receiving and digitally storing, by an agricultural implement in the field, field condition data relating to real-time field conditions from a plurality of sensors coupled to the agricultural implement, the field condition data including real-time weather data and real-time location data local to the agricultural implement; updating an electronic display coupled to the agricultural implement based on the field condition data to display a digital indication of at least a portion of the field condition data; using the field condition data to determine one or more field condition values ​​representing real-time field conditions local to the agricultural implement, the field condition values ​​including wind speed; comparing one or more field condition values ​​to one or more corresponding threshold condition values; as well as In response to determining that at least one of the one or more field condition values ​​exceeds a corresponding threshold condition value: expanding or reducing the size of one or more buffer areas of the field, and The electronic display is updated to include a warning message based on the position of the agricultural implement relative to the one or more buffer areas as indicated by the position data.

2. The method according to claim 1, wherein: The location data includes GPS data corresponding to the coordinate location of the agricultural implement; and The GPS data is received from one or more GPS sensors of the plurality of sensors attached to the agricultural implement.

3. The method according to claim 1, wherein: The field condition data includes proximity data corresponding to a distance between a crop canopy and a processing implement attached to the agricultural implement; The one or more field condition values ​​also include a distance between the crop canopy and the processing implement; and The proximity data is received from one or more proximity sensors attached to the agricultural implement.

4. The method according to claim 1, further comprising: transmitting said field condition data relating to real-time field conditions to one or more cloud-based servers using a wireless communication protocol; The one or more cloud-based servers are updated to include the field condition data.

5. The method according to claim 1, further comprising: receiving the one or more threshold condition values; The one or more threshold condition values ​​are stored at the agricultural implement.

6. The method according to claim 1, further comprising: displaying a map of the field on the electronic display; displaying in real time on the electronic display an indication of movement of the agricultural implement, the indication of movement corresponding to a history of the position of the agricultural implement as it traverses the field; Based on the field condition data, the movement indication is updated on the electronic display to include an overlay indication corresponding to a history of treatments applied to the field as the agricultural implement traverses the field.

7. The method of claim 1 , wherein the one or more field condition values ​​further include wind direction; and The method further includes displaying a wind map on the electronic display, the wind map including the wind speed and the wind direction at one or more field locations while the agricultural implement is operating.

8. The method according to claim 1, further comprising: determining, based on the field condition data, one or more portions of the field corresponding to areas that have received overtreatment or undertreatment; The one or more portions of the field determined to correspond to areas that have received overtreatment or undertreatment are displayed on the electronic display.

9. The method according to claim 1, further comprising: receiving field boundary data corresponding to dimensions of the field; defining the one or more buffer areas indicating areas of the field where a field treatment should not be applied based on the field condition data and the field boundary data; displaying the one or more defined buffer areas of a determined field on the electronic display before expanding or reducing the size of the one or more defined buffer areas; And then In response to determining that at least one of the one or more field condition values ​​exceeds the corresponding threshold condition value, the one or more defined buffer areas are displayed on the electronic display after expanding or contracting the one or more defined buffer areas.

10. The method according to claim 1, further comprising: receiving one or more treatment parameters corresponding to a specification for applying a treatment to a field; The field condition data and the one or more threshold condition values ​​are updated based on the received one or more processing parameters.

11. The method according to claim 1 , further comprising: determining, from the field condition data and the one or more threshold condition values, one or more automated instructions for operating the agricultural implement to apply a treatment to the field; The determined one or more automated instructions are displayed by the electronic display to apply a treatment to the field.

12. A crop treatment system for providing agricultural science improvements by optimizing crop treatment based on field conditions, the system comprising: Agricultural implements, including: electronic displays; Weather sensors, GPS sensors, and proximity sensors; and a processor and a main memory comprising executable instructions which, when executed by the processor, cause the processor to: receiving and digitally storing, by the agricultural implement in the field, field condition data relating to real-time field conditions, the field condition data comprising real-time weather data received from the weather sensor, real-time location data received from the GPS sensor, and field proximity data received from the proximity sensor, the field condition data being local to the agricultural implement in the field; updating the electronic display coupled to the agricultural implement based on the field condition data to display a digital indication of one or more real-time field conditions associated with the field; determining one or more field condition values ​​representing real-time field conditions local to the agricultural implement based on the weather data using the field condition data, the one or more field condition values ​​including a wind speed at the agricultural implement; comparing the one or more field condition values ​​to one or more corresponding threshold condition values; and In response to determining that at least one of the one or more field condition values ​​exceeds a corresponding threshold condition value: expanding or reducing the size of one or more buffer areas of the field; and The electronic display is updated to include a warning message based on the position of the agricultural implement relative to the one or more buffer areas as indicated by the position data.

13. The system of claim 12, further comprising: one or more cloud-based servers in communication with the agricultural implement via a wireless communication protocol; and When the executable instructions are executed by the processor, the processor further causes the processor to execute: transmitting the field condition data relating to real-time field conditions to the one or more cloud-based servers; and The one or more cloud-based servers are updated to include the field condition data.

14. The system of claim 12, wherein the executable instructions, when executed by the processor, further cause the processor to: receiving, by the agricultural implement, one or more threshold condition values; and The one or more threshold condition values ​​are stored at the agricultural implement.

15. The system of claim 12, wherein the executable instructions, when executed by the processor, further cause the processor to: displaying a map of the field on the electronic display; displaying in real time on the electronic display an indication of movement of the agricultural implement, the indication corresponding to a history of the position of the agricultural implement as it traverses the field; and Based on the field condition data, the movement indication is updated on the electronic display to include an overlay indication corresponding to a history of treatments applied to the field as the agricultural implement traverses the field.

16. The system of claim 12, wherein the executable instructions, when executed by the processor, further cause the processor to display a wind map on the electronic display, the wind map illustrating wind speed and direction when the agricultural implement is operating, the wind speed and direction including at least wind speed and direction at one or more field locations.

17. The system of claim 12, wherein the executable instructions, when executed by the processor, further cause the processor to: determining, based on the field condition data, one or more portions of the field corresponding to areas that have received overtreatment or undertreatment; The one or more portions of the field determined to correspond to areas that have received overtreatment or undertreatment are displayed on the electronic display.

18. The system of claim 12, wherein the executable instructions, when executed by the processor, further cause the processor to: receiving, by the agricultural implement, field boundary data corresponding to dimensions of the field; defining, based on the field condition data and the field boundary data, the one or more buffer areas corresponding to boundaries of the field where a treatment should not be applied; displaying the defined one or more buffer areas on the electronic display; And then In response to determining that at least one of the one or more field condition values ​​exceeds the corresponding threshold condition value, the one or more defined buffer areas are displayed on the electronic display after expanding or reducing the size of the one or more defined buffer areas.

19. The system of claim 12, wherein the executable instructions, when executed by the processor, further cause the processor to: receiving, by the agricultural implement, one or more treatment parameters corresponding to a specification for applying a treatment to a field; The field condition data and the one or more threshold condition values ​​are updated based on the received one or more processing parameters.

20. The system of claim 12, wherein the executable instructions, when executed by the processor, further cause the processor to: determining, from the field condition data and the one or more threshold condition values, one or more automated instructions for operating the agricultural implement to apply a treatment to the field; The determined one or more automated instructions are displayed by the electronic display to apply a treatment to the field.

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