identification of management zones in agricultural fields and generation of planting plans for the z

专利摘要:
In one embodiment, yield data representing crop yields that were harvested from an agricultural field and field characteristic data representing agricultural field characteristics are received and used to determine a plurality of management zone design options. each option of the plurality of management zone delineation options comprises zone layout data for one option. the plurality of management zone design options is determined by: determining a plurality of count values for a management class count; generate, for each count value, a management delineation option by grouping yield data and field characteristic data, assigning zones to clusters, and including the zones in a management zone delineation option. One or more options from the plurality of management zone design options are selected and used to determine one or more planting plans. A graphical representation of planting options and plans is displayed for a user.
公开号:BR112019009999A2
申请号:R112019009999-9
申请日:2017-11-15
公开日:2019-10-29
发明作者:Wimbush Alex；Hassanzadeh Anahita；Rowan Emily；Misra Marlon；Chen Ye
申请人:Climate Corp；
IPC主号:

专利说明:

IDENTIFICATION OF MANAGEMENT ZONES IN AGRICULTURAL FIELDS AND GENERATION OF PLANTING PLANS FOR THE AREAS
COPYRIGHT NOTICE [001] A portion of the disclosure in this patent document contains material that is subject to copyright protection. The copyright owner has no objection to facsimile reproduction by anyone of the patent document or patent disclosure as it appears in the patent or trademark file of the Trademark and Patent Office, but otherwise reserves all copyrights or rights whatever they may be. © 2016 The Climate Corporation.
FIELD OF REVELATION [002] The technical field of the present disclosure includes computer systems programmed with operations that are useful in agricultural management. The disclosure is also from the technical field of computer systems that are programmed or configured to generate management zone design options for agricultural fields using digital map data and chained data processing, to generate graphical representations of the design zone design options. management, and to generate computer-implemented recommendations for use in agriculture.
BACKGROUND [003] The approaches described in this section are approaches that can be followed, but are not necessarily approaches that have been designed or followed previously. Therefore, unless otherwise stated, it should not be assumed that any of the approaches described in this section qualify as prior art.
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2/99 merely because of its inclusion in this section.
[004] Management zones refer to contiguous regions within an agricultural field that have similar limiting factors that influence crops harvested from plantations. Field regions that belong to the same management zone can usually be managed uniformly in relation to sowing, irrigation, fertilizer application and harvest.
[005] An advantage of identifying management zones within an agricultural field is that information about the zones can help farmers to customize their agricultural practices to increase the productivity and production of the field. Customization of practices may include, for example, selecting particular seed hybrids, seed populations and nitrogen applications for individual zones.
SUMMARY [006] The attached claims may serve as a summary of the disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS [007] In the drawings:
[008] Figure 1 illustrates an example computer system that is configured to perform the functions described in this document, shown in a field environment with another device with which the system can operate together.
[009] Figure 2 illustrates two views of an example logical organization of instruction sets in main memory when an example mobile application is loaded for execution.
[010] Figure 3 illustrates a programmed process by which
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3/99 the agricultural intelligence computer system generates one or more preconfigured agronomic models using agronomic data provided by one or more data sources.
[Oil] Figure 4 is a block diagram illustrating a computer system 400 in which a modality of the invention can be implemented.
[012] Figure 5 represents an example of a timeline view for data entry.
[013] Figure 6 represents an example of a spreadsheet view for data entry.
[014] Figure 7 represents an example of a chain of creation of management zones.
[015] Figure 8 represents an example method for creating management zones for an agricultural field.
[016] Figure 9 represents a method for postprocessing management zones.
[017] Figure 10 is a screen snapshot of an example graphical user interface configured to outline management zones and generate recommendations for agronomic practices.
[018] Figure 11 represents an example method for delineating management zones and generating prescriptions.
[019] Figure 12 is a screen snapshot of an example graphical user interface configured to display examples of management zones and examples of planting plans.
[020] Figure 13 is a screen snapshot of an example graphical user interface configured to enable requesting a prescription for a selected planting plan.
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4/99 [021] Figure 14 is a screen snapshot of an example graphical user interface configured to display examples of management zones and examples of planting plans.
[022] Figure 15 is a screen snapshot of an example graphical user interface configured to allow a user to customize the planting plan.
DETAILED DESCRIPTION [023] In the following description, for the purpose of explanation, numerous specific details are set out in order to provide a complete understanding of the present disclosure. It will be apparent, however, that modalities can be practiced without these specific details. In other instances, well-known structures and devices are shown in the form of a block diagram in order to avoid unnecessarily obscuring the present disclosure. Modalities are revealed in sections according to the following general lines:
1. OVERVIEW
2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM
2.1. STRUCTURAL OVERVIEW
2.2. OVERVIEW OF APPLICATION PROGRAM
2.3. DATA INGESTION FOR THE COMPUTER SYSTEM
2.4. PROCESS OVERVIEW - AGRONOMIC MODEL TRAINING
2.5. IMPLEMENTATION EXAMPLE - HARDWARE OVERVIEW
3. IDENTIFICATION OF MANAGEMENT ZONES BASED ON PRODUCTION MAPS, SOIL MAPS, SURVEY MAPS AND
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SATELLITE DATA
3.1. MANAGEMENT ZONES
3.2. TRANSITIONAL ATTRIBUTE DATA - PRODUCTION DATA
3.3. PERMANENT ATTRIBUTE DATA
3.3.1. SOIL CHARACTERISTICS
3.3.2. TOPOLOGY CHARACTERISTICS
3.3.3. SOIL SURVEY MAPS
3.3.4. SATELLITE MAPS
3.3.5. MAPS OF NUDE SOILS AS EXAMPLES OF SATELLITE MAPS
3.4. CHAIN OF CREATING ZONES OF
MANAGEMENT
3.4.1. PRE-PROCESSING
3.4.2. SPACE SMOOTHING
3.4.3. NORMALIZATION
3.4.4. GROUPING
3.4.4.1. ZONE IDENTIFICATION OF
MANAGEMENT
3.4.4.2. K-MEANS APPROACH
3.4.4.3. FUSION APPROACH OF REGIONS
3.4.5. POST-PROCESSING
3.5. PERFORMANCE CONSIDERATIONS
4. UTILITY OF DESIGNING MANAGEMENT ZONES
5. APPLICATION OF EXAMPLE TO OUTLINE MANAGEMENT AREAS AND GENERATE RECOMMENDATIONS
5.1. EXAMPLE USES AND APPLICATIONS
5.2. EXAMPLE WORKFLOW
5.3. EXAMPLE OF AUTOMATIC SCRIPTS CREATION
5.4. EXAMPLE OF MANUAL CREATION OF SCRIPTS
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6. EXTENSIONS AND ALTERNATIVES [024] 1. OVERVIEW [025] In one embodiment, a process is provided to determine management zone design options for an agricultural field and to determine planting plans for the design options. The process includes receiving production data and field characteristics data. Yield data represents yields from plantations that have been harvested from the field. The field characteristics data represent characteristics of the field itself. Both types of data can be pre-processed by removing outliers, duplicate data and more. The data of production characteristics are referred to as transient characteristics of the field, while the data of field characteristics are referred to as permanent or persistent characteristics of the field.
[026] Field characteristic data for an agricultural field may include data on soil properties and data on topographic properties. Field characteristic data can be obtained from soil survey maps, bare soil maps and / or satellite images.
[027] Based on data received for a field, a plurality of management zone design options is determined. Each option, out of the plurality of management zone design options, can include a layout of the zones for the field and additional information about the zones. For example, a management zone design option may include information indicating how the field can be divided into zones and information indicating characteristics of individual zones.
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7/99 [028] A process of determining a plurality of management zone design options may include determining a plurality of count values for a management class count, and generating management zone design options for each value counting. Generating a management zone design option may include, for example, grouping production data and associated field characteristics data based on a count value, joining the groupings obtained in zones, and including zones in the design of management zones.
[029] Information regarding a management zone design option can be post-processed. A post-processing of a design option may include merging small management zones into the option with their respective large neighboring zones to generate a fused zone design option.
[030] In one embodiment, a process is configured to generate planting plans and recommendations for a plurality of options for designing management zones. For example, upon receiving certain criteria and / or certain input from a user, one or more planting plans for the management zone design options can be generated.
[031] Information regarding management zone design options and planting plans associated with the options can be used to control agricultural equipment, including a sowing device, an irrigation device, a device for applying fertilizers and / or a combine harvester. The equipment can be directed to follow the recommended planting plans in terms of
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8/99 sowing, irrigation, application of fertilizers and / or harvest.
[032] Layouts of management zones and information regarding planting plans can be displayed on computer display devices. For example, a computer system can be configured to generate a graphical user interface (GUI) and display the GUI on a computer display device. In addition, the computer system can display, in the GUI, graphical representations of options for delineating management zones and planting plans for the options.
[033] In one embodiment, a process is configured to receive user input to customize management zone design options and / or to customize planting plans. For example, the process can be configured to receive requests to merge the zones, divide the zones, modify the layouts of the zones, modify selections of seed hybrids, modify target yields and / or modify details of planting plans. The process can be configured to process incoming requests, and generate new options for designing management zones and / or new planting options for the zones. For example, the process can determine interrelationships between target yields and planting plans, modify planting plans, and display modified planting plans in graphical form on the user's display device.
[034] Using the techniques described in this document, a computer can determine a plurality of management zones based on digital data representing
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9/99 historical productions and characteristics of the field itself. The techniques enable computers to determine the management zones that can be managed uniformly and thus more efficiently and more productively.
[035] Presented techniques can enable an agricultural intelligence computing system to save computational resources, such as data storage, computing power and computer memory, by implementing a programmable chain configured to automatically determine management zones based on digital data obtained for a field. The programmable chain can automatically generate recommendations and alerts for farmers, insurance companies and researchers, thus allowing a more effective management of sowing schedules, fertilization schedules and crop schedules.
[036] Techniques presented may be useful in certain agricultural practices such as selecting a sowing rate. Information regarding management zone design options can be used to generate recommendations for farmers to suggest, for example, seed hybrids, sowing populations and sowing schedules for individual zones.
[037] 2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM [038] 2.1. STRUCTURAL OVERVIEW [039] Figure 1 illustrates an example computer system that is configured to perform the functions described in this document, shown in a field environment with another
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10/99 device with which the system can operate together. In one embodiment, a user 102 owns, operates or controls a field manager computing device 104 at a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more fields agricultural products. 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.
[040] Examples of field data 106 include (a) identification data (for example, area size in acres, field name, field identifiers, geographical identifiers, boundary identifiers, plantation identifiers and any other appropriate data that can be used to identify farm land, such as a common land unit (CLU), lot and block number, a land number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, number number, field number, section, municipality, and / or range), (b) harvest data (for example, type of plantation, variety of plantation, rotation of plantation, whether the plantation is organically grown, date of harvest, Real Production History (APH), expected production, yield, crop price, crop yield, grain moisture, crop practice and previous growing season information), (c) soil data (for example, t type, composition, pH, organic matter (OM), cation exchange capacity (CEC)), (d) planting data (eg planting date, seed type (s), relative seed maturity (RM) (s)
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11/99 planted (s), seed population), (e) fertilizer data (eg type of nutrient (nitrogen, phosphorus, potassium), type of application, date of application, quantity, origin, method), ( f) pesticide data (eg, pesticide, herbicide, fungicide, other substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant, date of application, quantity, origin, method), (g) data irrigation (for example, date of application, quantity, origin, method), (h) meteorological data (for example, precipitation, rain rate, predicted rain, water runoff region, temperature, wind, forecast, pressure , visibility, clouds, heat index, dew point, humidity, snow depth, air quality, sunrise, sunset), (i) image data (for example, image and light spectrum information of an agricultural appliance sensor, camera, computer, telephone in teligent, tablet, unmanned aerial vehicle, airplanes or satellites), (j) exploration observations (photos, videos, freeform notes, voice recordings, voice transcriptions, weather conditions (temperature, precipitation (current and along time), soil moisture, plantation growth stage, wind speed, relative humidity, dew point, black layer)), and (k) soil, seed, plantation phenology, pest and disease reporting, and sources predictions and databases.
[041] A data server computer 108 is communicatively coupled to the agricultural intelligence computer system 130 and is programmed or configured to send external data 110 to the computer system of agriculture.
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12/99 agricultural intelligence 130 through network (s) 109. The external data server computer 108 may be owned or operated by the same person or legal entity of the agricultural intelligence computer system 130, or by a person or different entity such as a government agency, non-governmental organization (NGO) and / or a private data service provider. Examples of external data include meteorological data, image data, soil data, or statistical data relating to crop production, among others. External data 110 may consist of the same type of information as field data 106. In some embodiments, external data 110 is provided by an external data server 108 belonging to the same entity that owns and / or operates the intelligence computer system. agricultural 130. For example, the agricultural intelligence computer system 130 may include a data server focused solely on a type of data that might otherwise be obtained from sources of external entities, such as meteorological data. In some embodiments, an external data server 108 can actually be incorporated into system 130.
[042] An agricultural device 111 can have one or more remote sensors 112 attached to it, the sensors of which are communicated directly or indirectly via agricultural device 111 to the agricultural intelligence computer system 130 and are programmed or configured to send data from sensors for the agricultural intelligence computer system 130. Examples of farm equipment 111 include tractors, combine harvesters, combines, seeders, trucks,
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13/99 fertilizers, unmanned aerial vehicles, and any other item of machinery or physical hardware, typically mobile machinery, that can be used in tasks associated with agriculture. In some embodiments, a single unit of apparatus 111 may comprise a plurality of sensors 112 that are coupled locally to a network in the apparatus; area control network (CAN) is an example of a network like this that can be installed on combined combines or harvesters. The application controller 114 is communicatively coupled to the agricultural intelligence computer system 130 via the network (s) 109 and is programmed or configured to receive one or more scripts, to control an operating parameter of a vehicle or implement of the agricultural intelligence computer system 130. For example, an area control network (CAN) bus interface can be used to enable communications from the agricultural intelligence computer system 130 to the agricultural device 111, such as the CLIMATE FIELDVIEW DRIVE, available from The Climate Corporation, San Francisco, California, is used. Sensor data may consist of the same type of information as field data 106. In some embodiments, remote sensors 112 may not be attached to an agricultural apparatus 111, and may be located remotely in the field and may communicate with network 109.
[043] Apparatus 111 may comprise a cabin computer 115 that is programmed with a cabin application, which may comprise a version or variant of the mobile application for device 104 which will be described further in other sections in this document. In one mode, the
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14/99 cabin computer 115 comprises a compact computer, often a tablet or smart phone, with a graphical screen display, such as a color display, which is mounted inside the operator cabin of the apparatus 111. The cabin computer 115 can implement all or some of the operations and functions that are further described in this document for the mobile computing device 104.
[044] Network (s) 109 generally represents any combination of one or more data communication networks including local area networks, wide area networks, network interconnections or internets, using any one of wired or wireless links, including terrestrial or satellite links. The network (s) can be implemented by any means or mechanism that allows the exchange of data between the various elements of figure 1. The various elements of figure 1 can also have direct communication links ( wired or wireless). Each of the sensors 112, the controller 114, the external data server computer 108 and other elements of the system comprises an interface compatible with the network (s) 109 and is programmed or configured to use standardized protocols for communication over the networks such as TCP / IP, Bluetooth, CAN protocol and higher layer protocols such as HTTP, TLS and more.
[045] The agricultural intelligence computer system 130 is programmed or configured to receive field data 106 from field manager computing device 104, external data 110 from external data server computer 108 and remote sensor sensor data 112 The agricultural intelligence computer system 130 can be
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15/99 additionally configured to host, use or run one or more computer programs, other software elements, digitally programmed logic such as FPGAs or ASICs, or any combination of them to perform translation and storage of data values, model building digital images of one or more plantations in one or more fields, generating recommendations and notifications, and generating and sending scripts to application controller 114, in the manner described further in other sections of this disclosure.
[046] In one embodiment, the agricultural intelligence computer system 130 is programmed or comprises a communication layer 132, the presentation layer 134, the data management layer 140, the hardware / virtualization layer 150 and the data repository model and field data 160. Layer, in this context, refers to any combination of electronic circuits of digital interfaces, microcontrollers, firmware such as drivers and / or computer programs or other software elements.
[047] Communication layer 132 can be programmed or configured to perform connection functions through the input / output interface including sending requests to the field manager computing device 104, the external data server computer 108 and the sensor remote 112 for field data, external data and sensor data respectively. Communication layer 132 can be programmed or configured to send received data to the model and field data repository 160 to be stored as field data 106.
[048] In one embodiment, the computer system of
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agricultural intelligence 130 is programmed or comprises at code instructions 180. For example, instructions in codes 180 may include instructions for receivement in
182 data that are programmed to receive, through network (s) 109, electronic digital data comprising production data. Code instructions 180 may also include data processing instructions 183 which are programmed to pre-process production data received; data smoothing instructions 184 that are programmed to smooth data from preprocessed productions; the data design instructions 187 that are programmed to delineate management zones; post-processing instructions 186 which are scheduled for post-processing of the outlined management zones; the data comparison instructions 185 that are programmed to compare the post-processed management zones; the screen display map generation instructions 189 and the other detection instructions 188.
[049] Presentation layer 134 can be programmed or configured to generate a GUI to be displayed on the field manager computing device 104, cabin computer 115 or on other computers that are coupled to system 130 via network 109. The GUI may comprise controls for entering data to be sent to the agricultural intelligence computer system 130, generating requests for models and / or recommendations, and / or displaying recommendations, notifications, models and other field data.
[050] Data management layer 140 can be programmed or configured to manage data operations
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17/99 reading and writing operations involving the repository 160 and other functional elements of the system, including queries and result sets communicated between the functional elements of the system and the repository. Examples of data management layer 140 include JDBC, SQL server interface code and / or HADOOP interface code, among others. The repository 160 can comprise a database. As used in this document, the term database can refer to a group of data, a relational database management system (RDBMS) or both. As used in this document, a database can comprise any collection of data including hierarchical databases, relational databases, flat file databases, object relational databases, object-oriented databases, and any another structured collection of records or data that is stored on a computer system. Examples of RDBMSs include, but are not limited to, the ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE® and POSTGRESQL databases. However, any database can be used that enables the systems and methods described in this document.
[051] When field data 106 is not provided directly to the agricultural intelligence computer system by means of one or more agricultural machines or agricultural machinery devices that interact with the agricultural intelligence computer system, the user can be guided through one or more user interfaces on the user device (served by the agricultural intelligence computer system) to enter such information. In an example mode, the user can
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18/99 specify identification data when accessing a map on the user's device (served by the agricultural intelligence computer system) and select specific CLUs that have been shown graphically on the map. In an alternative embodiment, user 102 can specify identification data when accessing a map on the user's device (served by the agricultural intelligence computer system 130) and drawing field boundaries on the map. Such a selection of CLU or map drawings represent geographical identifiers. In alternative modalities, the user can specify identification data when accessing field identification data (provided as shape files or in a similar format) from the US Department of Agriculture's Rural Service Agency or another source through the tracking device. user and provide such field identification data to the agricultural intelligence computer system.
[052] In an example embodiment, the agricultural intelligence computer system 130 is programmed to generate and induce display of a graphical user interface comprising a data manager for data entry. After one or more fields have been identified using the methods described above, the data manager can provide one or more graphical user interface symbols that when selected can identify changes to the field, soil, plantations or nutrient practices. The data manager can include a timeline view, a spreadsheet view and / or one or more editable programs.
[053] Figure 5 represents an example modality of
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19/99 a timeline view for data entry. Using the display shown in figure 5, a user computer can enter a selection of a particular field and a particular date for adding an event. Events represented at the top of the timeline can include Nitrogen, Planting, Practices and Soil. To add a nitrogen application event, a user computer can provide input to select the nitrogen tab. The user computer can then select a location on the timeline for a particular field to indicate an application of nitrogen to the selected field. In response to receiving a selection from a location on the timeline for a particular field, the data manager may display a data entry overlay, allowing the user's computer to enter data relating to nitrogen applications, planting procedures, application of soil, tillage procedures, irrigation practices or other information relating to the particular field. For example, if a user's computer selects a portion of the timeline and indicates a nitrogen application, then the data entry overlay may include fields to enter an amount of applied nitrogen, an application date, a type of fertilizer used , and any other information related to the application of nitrogen.
[054] In one embodiment, the data manager provides an interface for creating one or more programs. Program, in this context, refers to a set of data related to nitrogen applications, planting procedures, soil application, tillage procedures,
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20/99 irrigation, or other information that may be related to one or more fields, and that can be stored in digital data storage for reuse as a set in other operations. After a program has been created, it can be applied conceptually to one or more fields and references to the program can be stored in digital storage in association with data identifying the fields. So, instead of manually inputting identical data relating to the same nitrogen applications for multiple different fields, a user computer can create a program that indicates a particular nitrogen application and then apply the program to multiple different fields. For example, in the timeline view in figure 5, the top two timelines have the Applied Autumn program selected, which includes an application of 150 pounds (68.04 kg) N / ac in early April. The data manager can provide an interface for editing a program. In one mode, when a particular program is edited, each field that has selected the particular program is edited. For example, in Figure 5, if the Fall Applied program is edited to reduce nitrogen application to 130 pounds (58.97 kg) N / ac, the top two fields can be updated with a reduced nitrogen application based on edited program.
[055] In one mode, in response to receiving edits to a field that has a selected program, the data manager removes the field's correspondence for the selected program. For example, if a nitrogen application is added to the upper field in figure 5, the
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21/99 interface can update to indicate that the program Applied in the fall is no longer being applied to the upper field. Although the application of nitrogen in early April may remain, updates to the Applied program in the fall would not change the application of nitrogen in April.
[056] Figure 6 represents an example of a spreadsheet view for data entry. Using the display shown in figure 6, a user can create and edit information for one or more fields. The data manager can include spreadsheets to enter information regarding Nitrogen, Planting, Practices and Soil as shown in figure 6. To edit a particular entry, a user computer can select the particular entry in the spreadsheet and update the values. For example, figure 6 represents an ongoing update to a target production value for the second field. In addition, a user computer can select one or more fields in order to apply one or more programs. In response to receiving a program selection for a particular field, the data manager can automatically complete entries for the particular field based on the selected program. As with the timeline view, the data manager can update entries for each field associated with a particular program in response to receiving an update for the program. In addition, the data manager can de-match the program selected for the field in response to receiving an edit for one of the entries for the field.
[057] In one embodiment, model and field data is stored in the model and field data repository
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160. Model data comprises data models created for one or more fields. For example, a plantation model may include a digitally constructed model of the development of a plantation in one or more fields. Model, in this context, refers to a digitally stored electronic set of executable instructions and data values, associated with each other, which are able to receive and respond to a call, invocation or programmatic digital request or other for resolution based at specified input values, to produce one or more stored output values that can serve as the basis for computer-implemented recommendations, displays of output data or machine control, among other things. People with professional knowledge in the field know that it is convenient to express models using mathematical equations, but this form of expression does not limit the models revealed in this document to abstract concepts; instead, each model in this document has a practical application on a computer in the form of instructions and stored executable data that implement the model using the computer. The model data can include a model of events passed in one or more fields, a model of the current status of one or more fields and / or a model of events predicted in one or more fields. Model and field data can be stored in memory data structures, rows in a database table, in flat files or spreadsheets, or in other forms of stored digital data.
[058] The hardware / virtualization layer 150 comprises one or more central processing units (CPUs),
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23/99 memory controllers, and other devices, components or elements of a computer system such as volatile or non-volatile memory, non-volatile storage such as disk, and input / output devices or interfaces as illustrated and described, for example , in connection with figure 4. Layer 150 can also comprise programmed instructions that are configured to support virtualization, containerization or other technologies.
[059] For the purpose of illustrating a clear example, figure 1 shows a limited number of instances of certain functional elements. However, in other modalities, there may be any number of such elements. For example, modalities can use thousands or millions of different mobile computing devices 104 associated with different users. Additionally, system 130 and / or the external data server computer 108 can be implemented using two or more processors, cores, clusters or instances of physical machines or virtual machines, configured in a different location or located together with other elements in a center data, shared computing structure or cloud computing structure.
[060] 2.2. OVERVIEW OF APPLICATION PROGRAM [061] In one embodiment, the implementation of the functions described in this document using one or more computer programs or other software elements that are loaded and executed using one or more general purpose computers will induce the computers in use general to be configured as a particular machine or as a computer that is specially adapted to perform the functions described
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24/99 in this document. In addition, each of the flowcharts that are described further in this document can serve, alone or in combination with the prose process and function descriptions in this document, as algorithms, plans or directions that can be used to program a computer or logic to implement the functions that are described. In other words, all of the prose text in this document and all the figures are presented together to provide revelation of algorithms, plans or directions that are sufficient to allow a qualified person to program a computer to perform the functions that are described in this document, in combination with the professional knowledge and knowledge of such a person, given the level of professional knowledge that is appropriate for inventions and revelations of this type.
[062] In one embodiment, user 102 interacts with the agricultural intelligence computer system 130 using the field manager computing device 104 configured with an operating system and one or more application programs or applications; the field manager computing device 104 can also operate in conjunction with the agricultural intelligence computer system independently and automatically under program control or logical control and direct user interaction is not always required. The field manager computing device 104 generally represents one or more of a smart phone, PDA, tablet computing device, laptop, desktop computer, workstation or any other computing device capable of transmitting and receiving information and perform the functions described in this document.
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25/99 field manager computing device 104 can communicate over a network using a mobile application stored on field manager computing device 104, and in some embodiments the device can be coupled using a cable 113 or connector to sensor 112 and / or controller 114. A private user 102 may own, operate or own and use, in connection with system 130, more than one field manager computing device 104 at a time.
[063] The mobile application can provide client-side functionality, over the network, for one or more mobile computing devices. In an example embodiment, the field manager computing device 104 can access the mobile application via a network browser or a local client application or application. Field manager computing device 104 can transmit data to, and receive data from, one or more client-side servers, using network-based protocols or formats such as HTTP, XML and / or JSON, or application-specific protocols. In an example embodiment, the data may take the form of user input requests and information, such as field data, for the mobile computing device. In some embodiments, the mobile application interacts with location tracking hardware and software on the field manager computing device 104 which determines the location of the field manager computing device 104 using standard tracking techniques such as multilateration of radio signals, the global positioning system (GPS), WiFi positioning systems, or other positioning methods
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26/99 mobile. In some cases, location data or other data associated with device 104, user 102 and / or with user account (s) may be obtained by consulting a device's operating system or by requesting an application on the device to get data from the operating system.
[064] In one embodiment, the field manager computing device 104 sends field data 106 to agricultural intelligence computer system 130 comprising or including, but not limited to, data values representing one or more of: a geographical location of one or more fields, crop information for one or more fields, crops planted in one or more fields and soil data extracted from one or more fields. Field manager computing device 104 can send field data 106 in response to user input from user 102 by specifying data values for the one or more fields. In addition, field manager computing device 104 can automatically send field data 106 when one or more of the data values become available to field manager computing device 104. For example, field manager computing device 104 can be communicatively coupled to remote sensor 112 and / or application controller 114. In response to receiving data indicating that application controller 114 has released water for one or more fields, the field manager computing device 104 can send data field 106 for the agricultural intelligence computer system 130 indicating that water has been released to the one or more fields. The field data 106 identified in this
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27/99 disclosure can be entered and sent using electronic digital data that is transmitted between computing devices using URLs parameterized in HTTP, or another suitable communication or messaging protocol.
[065] A commercial example of the mobile application is CLIMATE FIELDVIEW, commercially available from The Climate Corporation, San Francisco, California. The CLIMATE FIELDVIEW application, or other applications, may be modified, extended or adapted to include features, functions and programming that were not revealed prior to the filing date of this disclosure. In one embodiment, the mobile application comprises an integrated software platform that allows a farmer to make fact-based decisions for his operation because it combines historical data about the farmer's fields with any other data that the farmer wants to compare. Combinations and comparisons can be performed in real time and are based on scientific models that provide potential scenarios to enable the farmer to make better decisions while being more informed.
[066] Figure 2 illustrates two views of an example logical arrangement of instruction sets in main memory when an example mobile application is loaded for execution. In Figure 2, each named element represents a region of one or more pages of RAM or other main memory, or one or more blocks of disk storage or other non-volatile storage, and the instructions programmed within those regions. In one embodiment, in view (a), a mobile computer application 200 comprises instructions for account-fields-data ingestion
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28/99 sharing 202, overview and alert instructions 204, digital map book instructions 206, seed and planting instructions 208, nitrogen instructions 210, weather instructions 212, health instructions for field 214 and performance instructions 216.
[067] In one embodiment, a mobile computer application 200 comprises account-field-data-ingestion-sharing instructions 202 that are programmed to receive, translate and ingest field data from external entity systems via manual transfer or APIs. Types of data can include field boundaries, production maps, maps of how planted, soil test results, maps of how applied and / or management zones, among others. Data formats can include shape files, natural data formats from external entities and / or exports from the farm management information system (FMIS), among others. Data receipt can occur via manual transfer, email with attachment, external APIs that push data to the mobile application, or via instructions that call APIs from external systems to pull data into the mobile application. In one embodiment, the mobile computer application 200 comprises a data entry box. In response to receiving a selection from the data entry box, the mobile computer application 200 may display a graphical user interface for manually transferring data files and importing files transferred to a data manager.
[068] In one embodiment, the 206 digital map book instructions comprise layers of field map data
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29/99 stored in device memory and are programmed with data visualization tools and geospatial field notes. This provides farmers with convenient information very close for reference, login and visual criteria for field performance. In one embodiment, the overview and alert instructions 204 are programmed to provide a broad operating view of what is important to the farmer, and recommendations at the right time to take action or focus on particular problems. This allows the farmer to focus in time on what needs attention, to save time and preserve production throughout the season. In one embodiment, the seed and planting instructions 208 are programmed to provide tools for seed selection, placement of hybrids and creation of scripts, including creation of variable rate (VR) scripts, based on scientific models and empirical data. This enables farmers to maximize production or return on investment through the purchase, placement and optimized seed population.
[069] In one embodiment, script generation instructions 205 are programmed to provide an interface for generating scripts, including variable rate (VR) fertility scripts. The interface enables farmers to create scripts for field implements, such as nutrient applications, planting and irrigation. For example, a planting script interface can comprise tools to identify a type of seed to plant. Upon receiving a seed type selection, the mobile computer application 200 may display one or more fields divided into the management zones, such as the data map layers of
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30/99 fields created as part of the 206 digital map book instructions. In one embodiment, the management zones comprise soil zones together with a panel identifying each soil zone and a soil name, texture, drainage for each zone, or other field data. The mobile computer application 200 can also display tools for editing or creating, such as graphical tools for drawing management zones, such as soil zones, on a map of one or more fields. Planting procedures can be applied to all management zones or different planting procedures can be applied to different subsets of management zones. When a script is created, the mobile computer application 200 can make the script available for download in a format readable by an application controller, such as an archived or compressed format. Additionally, and / or alternatively, a script can be sent directly to the cabin computer 115 by the mobile computer application 200 and / or transferred to one or more data servers and stored for further use. In one embodiment, nitrogen instructions 210 are programmed to provide tools to inform nitrogen decisions when viewing nitrogen availability for plantations. This enables farmers to maximize production or return on investment through optimized nitrogen application during the season. Programmed example functions include displaying images such as SSURGO images to enable design of application zones and / or images generated from subfield soil data, such as data obtained from sensors, at a high spatial resolution
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31/99 (as thin as 10 meters or less because of its proximity to the ground); transfer of existing farmer-defined areas; provide an application graph and / or a map to enable adjustment of nitrogen application (s) across multiple zones; exit scripts to drive machinery; tools for entering and adjusting mass data; and / or maps for data visualization, among others. Mass data entry, in this context, may mean entering data once and then applying the same data to multiple fields that have been defined in the system; Sample data may include nitrogen application data that is the same for many fields on the same farmer, but such mass data entry applies for entering any type of field data for the mobile computer application 200. For example , nitrogen instructions 210 can be programmed to accept definitions of planting programs and nitrogen practices and to accept user input specifying to apply these programs across multiple fields. Nitrogen planting programs, in this context, refer to a named stored set of data that associate: a name, color code or other identifier, one or more application dates, types of materials or products for each of the dates and quantities, method of application or incorporation such as injected or cut with a knife, and / or quantities or rates of application for each of the dates, plantation or hybrid that is the object of the application, among others. Nitrogen practice programs, in this context, refer to a named stored set of data that associates: a practice name; an earlier planting; a culture system;
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32/99 a culture date made primarily; one or more previous crop systems that have been used; one or more application type indicators, such as fertilizer, that has been used. Nitrogen instructions 210 can also be programmed to generate and induce the display of a nitrogen graph, which indicates projections of use per plant of the specified nitrogen and whether a surplus or supplement is predicted; in some modalities, different colored indicators may signal a magnitude of surplus or magnitude of complement. In one embodiment, a nitrogen graph comprises a graphical display on a computer display device comprising a plurality of rows, each row associated with a field and identifying the same; data specifying which crop is planted in the field, the field size, the field location, and a graphical representation of the field perimeter; in each row, a timeline per month with graphical indicators specifying each application and amount of nitrogen at points correlated to month names; and numerical and / or colored surplus or complement indicators, in which color indicates magnitude.
[070] In one embodiment, the nitrogen graph can include one or more user input features, such as diais or sliding bars, to dynamically change planting programs and nitrogen practices so that a user can optimize their nitrogen graph. The user can then use their optimized nitrogen graph and related nitrogen planting and practice programs to implement one or more scripts, including variable rate (VR) fertility scripts. The instructions
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33/99 nitrogen 210 can also be programmed to generate and induce display of a nitrogen map, which indicates projections of use per plant of the specified nitrogen and whether a surplus or supplement is predicted; in some modalities, different colored indicators may signal a magnitude of surplus or magnitude of complement. The nitrogen map can display projections of use per plant of the specified nitrogen and whether a surplus or complement is predicted for different times in the past and in the future (such as daily, weekly, monthly or annually) using numerical and / or colored surplus indicators or complement, in which color indicates magnitude. In one embodiment, the nitrogen map can include one or more user input features, such as diais or sliding bars, to dynamically change planting programs and nitrogen practices so that a user can optimize their nitrogen map , such as to obtain a preferred amount of surplus or complement. 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 modalities, instructions similar to nitrogen 210 instructions can be used for the application of other nutrients (such as phosphorus and potassium), application of pesticide and irrigation programs.
[071] In one embodiment, weather instructions 212 are programmed to provide recent field-specific weather data and predicted weather information. This enables farmers to save time and
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34/99 efficient integrated display in relation to operational decisions taken daily.
[072] In one embodiment, field health instructions 214 are programmed to provide remote sensing images at the right time highlighting plantation variation in the season and potential concerns. Programmed example functions include cloud checking to identify possible clouds or cloud shading; determine nitrogen indices based on field images; graphical visualization of exploration layers, including, for example, those related to field health, and observation and / or sharing of exploration notes; and / or transfer satellite images from multiple sources and prioritize the images for the farmer, among others.
[073] In one embodiment, performance instructions 216 are programmed to provide reports, analyzes and understanding tools using data about the farm for assessment, understandings and decisions. This enables the farmer to look for improved results for the coming year through fact-based conclusions as to why the return on investment was at previous levels, and an understanding of limiting factors of production. Performance instructions 216 can be programmed to communicate over network (s) 109 with server-side analytics programs run on the agricultural intelligence computer system 130 and / or on the external data server computer 108, and configured to analyze metrics such as production, hybrid, population, SSURGO, soil tests, or elevation, among others. Scheduled reports and analyzes may include variability analysis
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35/99 production, production benchmarking and other metrics against other farmers based on anonymous collected data from many farmers, or data for seeds and planting, among others.
[074] Applications having instructions configured in this way can be implemented for different computing device platforms while maintaining the same appearance as a common user interface. For example, the mobile application can be programmed to run on tablets, smart phones or on server computers that are accessed using browsers on client computers. In addition, the mobile application as configured for tablets or smart phones can provide a complete application experience or a cabin application experience that is suitable for the display and processing capabilities of the cabin computer 115. For example, referring now to the view (b) of figure 2, in one embodiment a cabin computer application 220 can comprise cabin map instructions 222, remote view instructions 224, data collection and transfer instructions 226, alert instructions for machine 228, script transfer instructions 230 and scan-cabin instructions 232. The code base for the instructions in view (b) can be the same as in view (a) and executables implementing the code can be programmed to detect the type of platform on which they are running and to expose, through a graphical user interface, only those functions that are o suitable for a cabin platform or complete platform. This approach empowers the system to recognize
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36/99 distinctly different user experience that is appropriate for an environment inside the cabin and environment technology different from that of the cabin. Cabin map instructions 222 can be programmed to provide views of maps of fields, farms or regions that are useful when directing machine operation. Remote view instructions 224 can be programmed to connect, manage and provide machine activity views in real time or near real time to other computing devices connected to the system 130 via wireless networks, wired connectors or adapters and others more. The 226 data collection and transfer instructions can be programmed to connect, manage and provide transfer of data collected from machine sensors and controllers to system 130 via wireless networks, wired connectors or adapters and more. Machine alert instructions 228 can be programmed to detect problems with machine or tool operations that are associated with the cab and generate operator alerts. Script transfer instructions 230 can be configured to transfer instruction scripts that are configured to direct machine operations or data collection. Operating instructions for cabin 230 can be programmed to display alerts based on location and information received from system 130 based on the location of the farm 111 or sensors 112 in the field and ingest, manage and provide transfer of location-based farm observations to the system 130 based on the location of agricultural device 111 or sensors 112 in the field.
[075] 2.3. DATA INGESTION FOR THE COMPUTER SYSTEM
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37/99 [076] In one embodiment, the external data server computer 108 stores external data 110, including soil data representing soil composition for the one or more fields and meteorological data representing temperature and precipitation in the one or more fields. Weather data can include past and present weather data as well as forecasts for future weather data. In one embodiment, the external data server computer 108 comprises a plurality of servers hosted by different entities. For example, a first server can contain soil composition data, while a second server can include weather data. Additionally, soil composition data can be stored on multiple servers. For example, one server can store data representing the percentage of sand, silt and clay in the soil, while a second server can store data representing the percentage of organic matter (OM) in the soil.
[077] In one embodiment, remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations. Remote sensor 112 can be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, crop sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from one or more more fields. In one embodiment, application controller 114 is programmed or configured to receive instructions from the agricultural intelligence computer system 130. Application controller 114 can also be programmed or configured to
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38/99 control an operating parameter of a vehicle or agricultural implement. For example, an application controller can be programmed or configured to control a vehicle's operating parameter, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvester equipment or other agricultural implements such as like a water valve. Other modalities can use any combination of sensors and controllers, of which the following are merely selected examples.
[078] System 130 can obtain or ingest data under user control 102, on a mass basis from a large number of farmers who have contributed data to a shared database system. This way of obtaining data can be called manual data ingestion since one or more user-controlled computer operations are requested or activated to obtain data for use by the 130 system. As an example, the CLIMATE FIELDVIEW application, commercially available from The Climate Corporation, San Francisco, California, can be operated to export data to system 130 for storage in repository 160.
[079] For example, seed monitoring systems can both control seeder device components and obtain planting data, including signals from seed sensors via a bundle of signal wires comprising a main CAN network and point-to-point connections point for registration and / or diagnostics. Seed monitoring systems can be programmed or configured to display seed spacing, population and other information for the seed.
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39/99 user via cabin computer 115 or other devices within system 130. Examples are disclosed in US patent 8,738,243 and US patent publication 20150094916, and the present disclosure assumes knowledge of these other patent disclosures.
[080] Likewise, production monitoring systems can contain production sensors for the combine device that send production measurement data to the cabin computer 115 or to other devices within the 130 system. Production monitoring systems can use one or more remote sensors 112 to obtain grain moisture measurements on a combine harvester or other harvester and transmit these measurements to the user via the cabin computer 115 or other devices within the system 130.
[081] In one embodiment, examples of sensors 112 that can be used with any moving vehicle or device of the type described elsewhere in this document include kinematic sensors and position sensors. Kinematic sensors can comprise any of speed sensors such as radar or wheel speed sensors, accelerometers or gyroscopes. Position sensors can comprise GPS receivers or transceivers, or WiFi-based positioning or mapping applications that are programmed to determine location based on nearby WiFi access points, among others.
[082] In one embodiment, examples of sensors 112 that can be used with tractors or other moving vehicles include engine speed sensors, fuel consumption sensors, area calculators, or
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40/99 distances that interact with GPS or radar signals, PTO speed sensors (PTO), tractor hydraulics sensors configured to detect hydraulic parameters such as pressure or flow and / or hydraulic pump speed, speed sensors wheel or wheel slip sensors. In one embodiment, examples of controllers 114 that can be used with tractors include hydraulic directional controllers, pressure controllers and / or flow controllers; hydraulic pump speed controllers; speed controllers or regulators; obstacle position controllers; or wheel position controllers that provide automatic steering.
[083] In one embodiment, examples of sensors 112 that can be used with seed planting equipment such as seeders, drills, or air seeders include seed sensors, which can be optical, electromagnetic or impact sensors; downward force sensors such as load pins, load cells, pressure sensors; sensors for soil properties such as reflectivity sensors, humidity sensors, electrical conductivity sensors, optical residue sensors or temperature sensors; component operating criteria sensors such as planting depth sensors, down force cylinder pressure sensors, seed disk speed sensors, seed drive engine encoders, seed conveyor system speed sensors, or vacuum level sensors; or pesticide application sensors such as optical or other electromagnetic sensors, or
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41/99 impact. In one embodiment, examples of controllers 114 that can be used with such seed planting equipment include: toolbar folding controllers, such as controllers for valves associated with hydraulic cylinders; down force controllers, such as controllers for valves associated with pneumatic cylinders, airbags or hydraulic cylinders, and programmed to apply downward force on individual row units or on a full seeder frame; planting depth controllers, such as linear actuators; dosing controllers, such as electric seed metering drive motors, hydraulic seed metering drive motors, or row control clutches; hybrid selection controllers, such as seed metering drive motors, or other triggers programmed to selectively allow or prevent seed or a seed air mixture from being delivered to or from seed metering or central volume hoppers; dosing controllers, such as electric seed metering drive motors, or hydraulic seed metering drive motors; seed conveyor system controllers, such as controllers for a seed delivery conveyor motor; marker controllers, such as a controller for a pneumatic or hydraulic actuator; or pesticide application rate controllers, such as dosage drive controllers, size or orifice controllers.
[084] In one embodiment, examples of sensors 112 that
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42/99 can be used with tillage equipment include position sensors for tools such as rods or discs; tool position sensors for such tools that are configured to detect depth, tool set angle, or side spacing; down force sensors; or drag force sensors. In one embodiment, examples of controllers 114 that can be used with tillage equipment include down force controllers or tool position controllers, such as controllers configured to control tool depth, tool set angle or side spacing.
[085] In one embodiment, examples of sensors 112 that can be used in relation to the apparatus to apply fertilizer, insecticide, fungicide and more, such as seed fertilizer starter systems, subsoil fertilizer applicators, or fertilizer sprayers, include: fluid system criteria sensors, such as flow sensors or pressure sensors; sensors indicating that spray head valves or fluid line valves are open; sensors associated with tanks, such as fill level sensors; sectional or wide system supply line sensors, or row-specific supply line sensors; or kinematic sensors such as accelerometers arranged on spray booms. In one embodiment, examples of controllers 114 that can be used with such an apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM and more;
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43/99 or position actuators, such as for boom height, subsoiler depth or boom position.
[086] In one embodiment, examples of sensors 112 that can be used with harvesters include production monitors, such as impact plate strain gauges or position sensors, capacitive flow sensors, load sensors, weight sensors, or torque sensors associated with elevators or conveyor threads, or optical or other electromagnetic grain height sensors; grain moisture sensors, such as capacitive sensors; grain loss sensors, including impact sensors, optical or capacitive; harvester head operating criteria sensors such as harvester head height, harvester head type, pallet slack clearance, feeder speed, and reel speed sensors; separator operating criteria sensors, such as concave clearance, rotor speed, shoe clearance or thresher clearance sensors; screw conveyor sensors for position, operation or speed; or engine speed sensors. In one embodiment, examples of controllers 114 that can be used with harvesters include harvesting head operating criteria controllers for elements such as harvesting head height, harvesting head type, pallet plate clearance, feeder speed, or feed speed. reel; separator operating criteria controllers for features such as hollow clearance, rotor speed, shoe clearance or thresher clearance; or controllers for position, operation, or conveyor screw speed.
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44/99 [087] In one embodiment, examples of sensors 112 that can be used with grain carts include weight sensors, or sensors for position, operation, or conveyor screw speed. In one embodiment, examples of controllers 114 that can be used with grain carts include controllers for position, operation, or conveyor screw speed.
[088] In one embodiment, examples of sensors 112 and controllers 114 can be installed in unmanned aerial vehicle (UAV) devices or drones. Such sensors can include cameras with effective detectors for any range of the electromagnetic spectrum including visible, infrared, ultraviolet, near infrared (NIR) and more; accelerometers; altimeters; temperature sensors; humidity sensors; pitot tube sensors or other sensors related to air or wind speed; battery life sensors; or radar emitters and apparatus for detecting reflected radar energy. Such controllers may include orientation or motor control devices, surface control controllers, camera controllers, or controllers programmed to connect, operate, obtain data and manage and configure any of the sensors indicated above. Examples are disclosed in patent application US 14 / 831,165 and the present disclosure acknowledges that other patent disclosure.
[089] In one embodiment, sensors 112 and controllers 114 can be attached to a soil sampling and measurement device that is configured or programmed to obtain soil samples and perform soil chemistry tests.
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45/99 soil, soil moisture testing and other soil testing. For example, the apparatus disclosed in US patent 8,767,194 and US patent 8,712,148 can be used, and the present disclosure assumes knowledge of those patent disclosures.
[090] In another embodiment, sensors 112 and controllers 114 can comprise weather devices to monitor field weather conditions. For example, the device disclosed in the international patent application PCT / US2016 / 029609 can be used, and the present disclosure assumes knowledge of that patent disclosure.
[091] 2.4. PROCESS OVERVIEW - AGRONOMIC MODEL TRAINING [092] In one embodiment, the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model. In this context, an agronomic model is a data structure in the memory of the agricultural intelligence computer system 130 comprising field data 106, such as identification data and harvest data for one or more fields. The agronomic model can also comprise calculated agronomic properties that describe conditions that can affect the growth of one or more plantations in a field, or properties of one or more plantations, or both. In addition, an agronomic model can comprise recommendations based on agronomic factors such as planting recommendations, irrigation recommendations, planting recommendations and harvest recommendations. Agronomic factors can also be used to estimate one or more results
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6/99 related to plantation, such as agronomic production. The agronomic production of a plantation is an estimate of the amount of the crop that is produced, or in some instances the revenue or profit obtained from the crop produced.
[093] In one embodiment, the agricultural intelligence computer system 130 can use a pre-configured agronomic model to calculate agronomic properties related to location and plantation information currently received for one or more fields. The pre-configured agronomic model is based on previously processed field data, including, but not limited to, identification data, harvest data, fertilizer data and weather data. The pre-configured agronomic model may have been cross-validated to ensure model accuracy. Cross-validation may include a comparison with soil veracity that compares predicted results with actual results in a field, such as a comparison of rainfall estimation with a rain gauge or sensor providing meteorological data in the same or nearby location or an estimate of soil content. nitrogen with a soil sample measurement.
[094] Figure 3 illustrates a programmed process by which the agricultural intelligence computer system generates one or more preconfigured agronomic models using field data provided by one or more data sources. Figure 3 can serve as an algorithm or instructions for programming the functional elements of the agricultural intelligence computer system 130 to perform the operations that are now described.
[095] In block 305, the computer system of
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47/99 agricultural intelligence 130 is configured or programmed to implement pre-processing of agronomic data from field data received from one or more data sources. Field data received from one or more data sources can be pre-processed for the purpose of removing noise and distortion effects on agronomic data including measured outliers that would predispose received field data values. Pre-processing modalities of agronomic data may include, but are not limited to, removing data values commonly associated with outlier data values, specific measured data points that are known to skew unnecessarily to other data values, smoothing techniques data used to remove or reduce additive or multiplicative effects of noise, and other filtering or data derivation techniques used to provide clear distinctions between positive and negative data inputs.
[096] In block 310, the agricultural intelligence computer system 130 is configured or programmed to perform subset selection of data using the pre-processed field data in order to identify data sets useful for generating the initial agronomic model. The agricultural intelligence computer system 130 can implement subset data selection techniques including, but not limited to, a genetic algorithm method, a method of all subset models, a sequential search method, a regression method gradually, a particle set optimization method and an ant colony optimization method. For example, a genetic algorithm selection technique uses an algorithm for
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48/99 adaptive heuristic research, based on evolutionary principles of selection and natural genetics, to determine and evaluate data sets within pre-processed agronomic data.
[097] In block 315, the agricultural intelligence computer system 130 is configured or programmed to implement field data set evaluation. In one embodiment, a specific field data set is assessed when creating an agronomic model and using specific quality thresholds for the created agronomic model. Agronomic models can be compared using cross-validation techniques including, but not limited to, root cross-validation of the mean leaving square error (RMSECV), mean absolute error and mean percentage error. For example, RMSECV can cross-validate agronomic models by comparing predicted agronomic property values created by the agronomic model with historical agronomic property values collected and analyzed. In one embodiment, the agronomic data set assessment logic is used as a feedback loop, where agronomic data sets that do not meet configured quality thresholds are used during future data subset selection steps (block 310).
[098] In block 320, the agricultural intelligence computer system 130 is configured or programmed to implement the creation of an agronomic model based on cross-validated agronomic data sets. In one modality, creating an agronomic model can implement multiple variable regression techniques
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49/99 to create preconfigured agronomic data models.
[099] In block 325, the agricultural intelligence computer system 130 is configured or programmed to store pre-configured agronomic data models for future evaluation of field data.
[0100] 2.5. IMPLEMENTATION EXAMPLE - HARDWARE OVERVIEW [0101] According to one modality, the techniques described in this document are implemented by one or more special-purpose computing devices. Special-purpose computing devices can be physically connected to perform the techniques, or they can include digital electronic devices such as one or more application-specific integrated circuits (ASICs) or field programmable port arrays (FPGAs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques according to program instructions in firmware, memory, other storage, or in a combination. Such special-purpose computing devices can also combine physically connected connected logic, ASICs or FPGAs with customized programming to execute the techniques. Special purpose computing devices may be desktop computer systems, portable computer systems, portable devices, network devices or any other device that incorporates physically connected and / or program logic to implement the techniques.
[0102] For example, figure 4 is a block diagram illustrating a computer system 400 in which a
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50/99 embodiment of the invention can be implemented. Computer system 400 includes a bus 402 or other communication mechanism for transmitting information, and a hardware processor 404 coupled to bus 402 for processing information. The hardware processor 404 can be, for example, a general purpose microprocessor.
[0103] 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 to store information and instructions to be executed by the 404 processor. The memory principal 406 can also be used to store temporary variables or other intermediate information while executing instructions to be executed by the 404 processor. Such instructions, when stored on non-transitory storage media accessible to the 404 processor, render computer system 400 to a special-purpose machine that is customized to perform the operations specified in the instructions.
[0104] Computer system 400 additionally includes a read-only memory (ROM) 408 or other static storage device coupled to bus 402 to store static information and instructions for processor 404. A storage device 410, such as a disk magnetic disk, optical disk or solid state drive, is provided and coupled to the 402 bus to store information and instructions.
[0105] Computer system 400 can be coupled via bus 402 to a display 412, such as a ray tube
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51/99 cathode (CRT), to display information to a computer user. An input device 414, including alphanumeric and other keys, is coupled to bus 402 to provide information and command selections for processor 404. Another type of user input device is cursor control 416, such as a mouse, a stationary mouse, or cursor direction keys to provide direction information and command selections for the 404 processor and to control cursor movement on the 412 display. This input device typically has two degrees of freedom on two axes, a first axis (for example, example, x) and a second axis (for example, y), which allow the device to specify positions on a plane.
[0106] The computer system 400 can implement the techniques described in this document using physically customized connected logic, one or more ASICs or FPGAs, firmware and / or program logic that in combination with the computer system induce or program the computer system 400 to be a special use machine. According to one embodiment, the techniques in this document 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 in main memory 406 from a other storage media, such as storage device 410. Executing the instruction sequences contained in main memory 406 causes processor 404 to perform the process steps described in this document. In alternative modes, physically connected circuitry can be used in place or
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52/99 in combination with software instructions.
[0107] The term storage media as used in this document refers to any non-transitory media that store data and / or instructions that induce a machine to operate in a specific mode. Such storage media may comprise non-volatile media and / or volatile media. Non-volatile media include, for example, optical discs, magnetic disks or solid state drives, such as the storage device 410. Volatile media include dynamic memory, such as main memory 406. Common forms of storage media include, for example , a floppy disk, a floppy disk, hard drive, solid state drive, magnetic tape, or any other magnetic data storage media, a CD-ROM, any other optical data storage media, any physical media with hole patterns , a RAM, a PROM and EPROM, a FLASH-EPROM, NVRAM, any other chip or memory cartridge.
[0108] Storage media is distinct, but can be used in combination with transmission media. Transmission media participates in the transfer of information between storage media. For example, transmission media includes coaxial cables, copper wires and optical fibers, including the wires that comprise the 402 bus. Transmission media can also take the form of acoustic or light waves, such as those generated during data communications. radio and infrared wave.
[0109] Various forms of media can be involved by loading one or more strings of one or more instructions to the 404 processor for execution. For example,
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Instructions can be loaded initially onto a magnetic disk or solid state drive on 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 local modem for computer system 400 can receive the data on the telephone line and use an infrared transmitter to convert the data into an infrared signal. An infrared detector can receive the data loaded in the infrared signal and the appropriate circuitry can place the data on bus 402. Bus 402 loads the data into main memory 406, from which processor 404 retrieves and executes instructions. The instructions received by main memory 406 can optionally be stored in storage device 410 before or after execution by processor 404.
[0110] Computer system 400 also includes a communication interface 418 coupled to bus 402. Communication interface 418 provides a bidirectional data communication coupling for a network link 420 that is connected to a local network 422. For example , the communication interface 418 can be an integrated services digital network card (ISDN), cable modem, satellite modem, or a modem to provide a data communication connection for a corresponding type of telephone line. As another example, communication interface 418 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links can also be implemented. In any such implementation, the communication interface
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418 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
[0111] The network link 420 typically enables data communication through one or more networks to other data devices. For example, the network link 420 may provide a connection via the local network 422 to a host computer 424 or to data equipment operated by an Internet Service Provider (ISP) 426. ISP 426 in turn provides network services. data communication via the worldwide 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. Signals through the various networks and signals on network link 420 and through communication interface 418, which carry digital data to and from computer system 400, are exemplary forms of transmission media.
[0112] The computer system 400 can send messages and receive data, including program code, through the network (s), the network link 420 and the communication interface 418. In the Internet example, a server 430 can transmit a requested code to an application program via the Internet 428, ISP 426, local network 422 and the communication interface 418.
[0113] The received code can be executed by the processor 404 as it is received, and / or stored in the storage device 410, or in other non-volatile storage for execution later.
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55/99 [0114] 3. IDENTIFICATION OF MANAGEMENT ZONES BASED ON PRODUCTION MAPS, SOIL MAPS, TOPOGRAPHY MAPS AND SATELLITE DATA [0115] 3.1. MANAGEMENT ZONES [0116] In the context of precision agriculture, management zones are contiguous subregions within an agricultural field that have similar restrictions or limiting factors that influence crops harvested from plantations. Field regions that belong to the same management zone can usually be managed uniformly in terms of sowing schedules or management practices. Identifying management zones within a field can help farmers to make personalized management decisions, such as choosing seed hybrids and sowing populations that are best for each individual zone.
[0117] One objective when creating zones is to divide the total agricultural field into regions of different productivity with different spatio-temporal production behaviors. Creating, or identifying, such areas can help guide farmers to improve agricultural practices. This may include providing farmers with recommendations for sowing rate selection, sowing time, fertilizer selection and fertilization time for individual zones.
[0118] Recommendations that are tailored to the needs of individual zones to improve field yield and profitability may include prescriptions for sowing, using certain seed hybrids, seed populations and nitrogen fertilizer for different subregions in a field. Recommendations can be
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56/99 determined based on characteristics of regions within a zone.
[0119] A criterion that can be used to determine the quality of management zones is compactness. Zones that are generated using a good management zone design approach are usually compact. Generating compact zones involves maximizing homogeneity within zones. There must also be a well-defined separation between different zones to ensure that the zones created really do require different management practices. The compactness and separation of the management zones that were created can be assessed through a visual assessment by directly overlapping the zones outlined with production maps, or by graphing a distribution of production values in each zone and year, using programmed computers properly. Compactness and separation can also be assessed through a quantitative assessment that defines numerical measures to precisely quantify the compactness and separation of production observations in the outlined areas.
[0120] Management zones can be created automatically via computer programs, based on transient and permanent characteristics of an agricultural field. Transitional features may include production data collected for sub-regions and using maps of historical productions. Permanent features may include soil measurements and topographic properties of the field. Permanent feature data can be obtained from SSURGO maps and satellite images of the field. Permanent features can be particularly useful
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57/99 when maps of historical productions are unavailable to the countryside. Using the permanent characteristics of the field when determining management zones allows you to incorporate data layers, such as soil and elevation data, in addition to production data, into the zone creation process, and thus refine the zone creation process.
[0121] Management zones that are created based on production maps can group regions with similar production patterns and permanent properties. Such management zones aim to elucidate the characteristics of productivity using the underlying properties of the soil. For example, areas with low organic matter or high pH can both have low production.
[0122] In one embodiment, a process of creating management zones comprises obtaining and processing data of transient characteristics and data of permanent characteristics for a field. The process may include determining desired zone sizes, and an optimal zone count to achieve desired field productivity and production. The process may include creating one or more management zone design options, and separating planting plans for individual options.
[0123] In one embodiment, a process for creating management zones comprises an interactive computer tool that is programmed to view graphical representations of management zone design options and corresponding planting plans. The interactive tool can also be configured to manipulate zone layouts in the outlined zone options.
[0124] Graphical representations of management zones
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58/99 and planting plans can be generated using a GUI, and can graphically depict layouts of the zones, information about the zones and planting plans for the zones.
[0125] 3.2. TRANSITIONAL ATTRIBUTE DATA - PRODUCTION DATA [0126] Transient attribute data represents land or field characteristics that vary from time to time. In the context of agricultural management zones, examples of transient attribute data may include yield data because yields from one field vary from one harvest season to another.
[0127] Production data may include maps of historical productions that represent spatial and temporal production patterns for subfields. Yield data may include information about crop yields harvested from an agricultural field within a year or several years. Production data can also include additional information such as a field outline, field size and location of each subfield within the field. Production data can be provided from different sources. Examples of the sources may include research partners, agricultural agencies, agricultural organizations, farmers, government agencies and others.
[0128] 3.3. PERMANENT ATTRIBUTE DATA [0129] Permanent attribute data represents characteristics that remain unchanged from one season to another. In the context of agricultural management zones, examples of permanent attribute data for a field may include soil characteristics, topology and
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59/99 field land because such data usually does not change from one harvest season to another.
[0130] Permanent attribute data can include soil characteristics and topology characteristics. They can be obtained from soil survey maps, satellite maps and bare soil maps. Permanent attribute data can be provided as data sets. Examples of data sets include the Research Partner 2013 and 2014 soil sampling data sets, Rapid-Eye images, SSURGO polygon outlines and National Elevation Data Set (NED).
[0131] 3.3.1. SOIL CHARACTERISTICS [0132] Data for soil characteristics of a field can be obtained based on soil samples collected from the field. Soil sampling for a field can be performed using various sampling techniques, such as collecting soil samples at an approximate resolution of a sample over two acres. Samples can be collected at mesh points within a field and form approximately a rectangle. The original measurement data can be made available as shape files stored on computer servers.
[0133] When soil samples are provided from different sources, there may be some differences in soil sampling methods, the precision with which the samples were collected and the sampling depths at which the soil was sampled. Therefore, data sets can be preprocessed. Pre-processing can include removing duplicate samples, samples without associated values, samples without geographic coordinate information and
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60/99 samples with incorrect geographic information and coordinates.
[0134] 3.3.2. TOPOLOGY CHARACTERISTICS [0135] Topology characteristics of a field may include geographical and elevation characteristics of the field. Topology characteristics can include elevation data for an agricultural field, and other topographic properties that can be derived from elevation data. Properties can include a moisture index, also referred to as a CTI Composite Topographic Index, a Topographic Position Index (TPI) indicator, an aspect, a flow direction and a slope.
[0136] Elevation data can be obtained from different sources, including the National Elevation Data Set (NED). The NED data set usually provides a resolution of about a third of an arc second.
[0137] 3.3.3. SOIL SURVEY MAPS [0138] Characteristics of soil surveys can be provided in the form of soil survey maps. A source of soil survey maps is the SSURGO database which contains soil survey data from many areas in the United States.
[0139] A typical soil survey data set is organized as a set of individual map units, each of which covers a polygon area. The data associated with each polygon can include soil properties and soil texture data, and the data can be provided in different spatial resolutions. The data may or may not be associated with specific geographic locations.
[0140] Soil survey data may represent
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61/99 qualitative evaluation data and samples analyzed in the laboratory. Since SSURGO maps provide a high resolution of soil measurement data, the soil texture data available on SSURGO maps may be sufficient for the purpose of zone creation. In a particular implementation, the applicable soil texture data is at a level 2 mukey (map unit key). This means that the value of the soil texture properties is uniform across the total spatial polygon.
[0141] In one embodiment, SSURGO data for a set of fields of interest is provided as a set of spatial polygons. The set of polygons can be processed, for example, when determining whether soil texture data has been lost for a total polygon, and if so k nearest neighboring data points (kNN) can be used to interpolate the lost data point . In addition, the percentages of sand, silt and clay can be normalized to add up to 100%. Examples of attributes used in a zone creation process include sand and silt attributes.
[0142] 3.3.4. SATELLITE MAPS [0143] Satellite characteristics for an agricultural field are typically determined based on satellite maps. Satellite image data can be provided in different spatial, spectral and temporal resolutions. Satellite maps can provide information regarding agricultural plantation assessment, plantation health, change detection, environmental analysis, irrigated scenario mapping, production determination and soil analysis. Images can be acquired at different times than
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62/99 year and several times within one year.
[0144] Satellite images may represent variations in organic matter and drainage patterns. Soils with more organic matter can be differentiated from lighter sandy soil that has a lower organic matter content. This information can be used in combination with other types of maps to define management zones for a field.
[0145] 3.3.5. NON-SOIL MAPS AS EXAMPLES OF SATELLITE MAPS [0146] Bare-soil maps are examples of satellite maps. Bare soil maps include bare soil characteristics determined on the basis of bare soil maps. Examples of such maps may include RapidEye satellite images. In a typical RapidEye image for a field, data can contain percent reflectance values per pixel (5 by 5 meters) for five different bands: red, red, blue, green and near-infrared. Since RapidEye data represents a better surface layer than deeper soil layers, and in the RP fields, depths of soil samples may be unknown, using RapidEye images can provide additional soil characteristics.
[0147] In one mode, a set of images of bare soil is pre-processed. For example, for each field, images with cloud contaminations can be discarded while images from the most recent year can be selected.
[0148] 3.4. CHAINING TO CREATE MANAGEMENT ZONES [0149] One goal when creating management zones is to divide a total agricultural field into regions of productivity
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63/99 different behaviors with different spatio-temporal production. Creating, or identifying, such areas can assist and guide farmers in providing farmers with recommendations for agricultural practices adapted to individual areas.
[0150] In one modality, management zones are delineated within an agricultural field using a chain of creation of management zones.
[0151] Figure 7 represents an example of a chain of creation of management zones. The example represents programmed processing steps and an algorithm for use when programming the instructions discussed in advance in connection with figure 1. The management zone creation chain 700 includes blocks of
processing for actions performed sequentially, in parallel or what are optional, as described additionally in this section •
[0152] Block 701 represents program instructions for storing data representing transient and permanent characteristics of an agricultural field. The data can be stored in various data repositories, including server computers, databases, cloud storage systems, service providers, external data storage devices and more. Transient characteristic data may include production data 701a. Permanent feature data can be provided such as soil maps 701b, soil survey maps 701c, topology maps 701d, bare soil maps 701e and satellite images 701f. Other information regarding permanent soil characteristics
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64/99 and field can also be used.
[0153] Block 702 represents program instructions for receiving data. In block 702, data is received; for example, system 130 (figure 1) receives production data and permanent feature data as part of field data 106. The data can include maps of historical productions at the field level or subfield level, and maps representing permanent characteristics from soil. The maps represent spatial and temporal patterns for the subfields and are used to classify a field in regions with different or different productivity potentials.
[0154] Data can be received from different sources such as research partners (PR), agencies, organizations, farmers and others. Data received may include information regarding the production of crops harvested from an agricultural field within a year or several years. In one embodiment, production data can also include metadata such as a field outline, field size and location of each subfield within the field.
[0155] 3.4.1. PRE-PROCESSING [0156] Blocks 704, 706 and 708 represent program instructions for pre-processing, density processing and data smoothing of received production data. Instructions for blocks 704, 706 and 708 can be executed selectively, optionally, sequentially or in parallel. The way in which the tasks are performed can vary based on the implementation and the quality of production data received. For example, some of the data received may need pre-processing, but not smoothing. Other data may only need
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65/99 density processing. Selecting one or more of blocks 704, 706, 708 can be based on manual or machine based inspection of the data received as part of block 702.
[0157] Preprocessing may comprise programmatically identifying and removing data items that are outliers, invalid, redundant or collected outside a field boundary. Pre-processing may also include identifying, and removing, production observations if multiple crops were planted within the field in the same season.
[0158] Block 704 represents program instructions for pre-processing received data. Preprocessing in block 704 can be performed, for example, because some of the data observations for a field have been collected outside the corresponding field limits. Pre-processing can also be recommended when data are provided from a field in which multiple crops were planted in the same season.
[0159] Pre-processing of production data can be performed to reduce noise observations from production observations, imput lost production values to standardize the zone design step and so on. In one embodiment, production data received is pre-processed to correct certain issues with the data. Pre-processing can include various types of data cleaning and filtering.
[0160] Pre-processing of production data may include removing outliers from production data. Production data may include observations of subfield productions that consist of various contaminations caused by
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6/99 inevitable errors introduced by the way in which crops are harvested, or by the way in which production data is collected or recorded. Removing such errors or outliers effectively results in decontaminating production data.
[0161] In one embodiment, production data received is analyzed to determine if less than two years of production maps are provided for a field. If less than two years of production maps are provided for a field, then the production maps are not included in the zone design.
[0162] Additional pre-processing and filtering of the data can be performed on production data. An example is adjustment to consider grain moisture. Grain moisture adjustment allows you to correct yield data records for some fields and years when crops were harvested at a moisture level other than a standard moisture level such as 15.5% moisture.
[0163] Additional processing can be directed to correct induced field throughput data when experimental production data is provided. Additional processing may include correction of production data if the data has been pre-smoothed by the data provider using unwanted algorithms or parameters. This type of additional processing is recommended to reduce the effect of production data being improperly smoothed on the results of creating management zones.
[0164] Additional pre-processing of the data may include transforming the data from latitude-longitude coordinates to Universal Transverse coordinates
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Mercator (UTM), and map to a mesh that has been defined for the field. A 10 m x 10 m mesh has been used in one modality. The mapping allows standardization of production record locations within the field.
[0165] Preprocessing of permanent feature data may include adjusting soil samples for the resolution of samples per acre that was reported in the longitude and latitude coordinate system if the received data was sampled at a different resolution, and projecting accordingly. programmatically the soil sample data in UTM coordinates. Lost sample values can be interpolated in the UTM coordinates from the available data using a Gaussian process model with a constant trend whose parameters are obtained with maximum probability estimation.
[0166] Elevation, CTI and slope data for production data can be obtained directly from maps or from property scanning data. This may include extracting cell values from the elevation scan where a spatial point of production falls. If no cell scans are discovered, then an indication of no values is returned.
[0167] After a projection of the coordinates of a spatial polygon to UTM coordinates is performed, SSURGO polygons can be exaggerated for the spatial locations of the production data.
[0168] When projecting the image data in the UTM coordinate system, values of the image data at the production data location points can be obtained by rasterizing the production data and the results can be transferred to the production scan cells . If
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68/99 a production data cell is covered by multiple data points from the image bands, so an arithmetic mean of the values can be used to associate with the scan cell.
[0169] Block 706 represents program instructions for processing the density of received data. Data density processing can be performed to normalize production data across different fields and fields. In one embodiment, data density processing comprises using an empirical cumulative distribution function transformation (ECDF), which can be performed on the production records for each field and year so that the transformed production data remains within a certain range. track through different fields and plantations. For example, ECDF can be applied to production data received to transform the data into production data transformed in the range of [0, 1]. Once production data are transformed, transformed production data can be compared across different years and crops, such as corn, soybeans or wheat.
[0170] 3.4.2. SPACE SMOOTHING [0171] Spatial smoothing is performed to remove noise from measurements in raw production observations and reduce unnecessary fragmentation of outlined management zones and can be performed using approaches such as a core smoother or a stationary Gaussian process. Data smoothing can be performed on raw data or processed data depending on the quality of raw data received.
[0172] A core smoother is a statistical technique
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9/99 to estimate a function when using its noise observations when a parametric model for the function is not known. The resulting estimated function is usually smooth and can be used to remove noise observations from a set of observations, such as production data. In one embodiment, core smoothers that are reliable and useful nonparametric estimators are selected to perform spatial smoothing of production data. Examples of core smoothers that can be used to smooth the production data include: Gaussian core, inverse distance weighting core, rectangular core, triangular core, bicatric core, tricubic core, weighted core, etc. In addition to their standard parameterization, they all have a scale parameter h and an extension parameter H such that the distance between observations of production data can be scaled and observations that are more than H from the destination point can be omitted in the mitigation process.
[0173] Block 708 represents program instructions for smoothing received data. Data smoothing can include testing whether any production data records are lost, whether production data records need to be further smoothed, or whether certain production data records need to be removed or challenged.
[0174] 3.4.3. STANDARDIZATION [0175] In one embodiment, received data are normalized through transformation to a particular data range and the process of designing management zones may include using programmed instructions to transform production data to generate production data
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70/99 transformed. Transforming production data may include applying an empirical cumulative density function (ECDF) to production data to normalize data to a certain range, such as a [0, 1] range. The data on processed productions can be compared across different years and types of plantations. For example, ECDF may allow you to transform, or normalize, production records for each field and year, regardless of the type of plantation and the collection time, for a range of [0, 1], so that the transformed data can be comparable to each other.
[0176] ECDF transformation can be used to transform production data into transformed production data. Applying ECDF to production data can cause transformation from production data records to transformed production data records, each of which is included in a particular range. Applying ECDF to production data causes production data to normalize so that normalized production data is comparable across different years and crops, such as corn, soybeans and wheat.
[0177] 3.4.4. GROUPING [0178] Grouping is performed on data representing transient and permanent characteristics of an agricultural field to determine a plurality of group labels associated with pixels represented by the pre-processed data. In one embodiment, k-means grouping can be used. In the final step, zones with sizes smaller than s, which is established using configuration or input data, are merged with their large neighboring zones
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71/99 more similar.
[0179] In block 710, pre-processed data representing transient and permanent characteristics of an agricultural field are used to outline a set of management zones for an agricultural field. The set of management zones outlined can be represented using stored digital zone data, and created by applying centroid-based approaches, such as the Kmeans approach, or a diffuse C-means approach. Details of these approaches will be further described in this document in connection with figure 8. The process performed on block 710 can be repeated, as represented by arrow 712, one or more times until the quality of the management zones created is satisfactory. The process can be repeated using different criteria, different parameters or different parameter values.
[0180] To achieve the objective of compactness that was discussed earlier, in block 714, a set of outlined management zones is analyzed to determine whether some of the zones can be merged. For example, a set of outlined management zones can be analyzed to identify small zones and to determine whether small zones can be merged with larger neighboring zones. Small zones can be identified automatically by a computer system, or manually by a computer system user. For example, the computer system can display information about the set of first management zones for a farmer on a graphical user interface that is programmed with graphic symbols or controls to allow
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72/99 for the farmer to remove undesirable small fragmented areas, or to merge small fragmented areas with larger areas. Merging of zones results in obtaining a set of merged management zones. If small zones are not identified in a set of
management outlined, then the set of zones management outlined is provided for the block 718. [0181] 0 process performed on block 714 Can be repeated one or more times until small areas don't be identified in the set of zones management. 0
The process can be repeated using different criteria, different parameters or different parameter values.
[0182] In block 718, a set of management zones is post-processed. Post-processing of management zones can include eliminating zones that are fragmented or unusable.
[0183] The process performed in block 718 can be repeated one or more times until the quality of the management zones created is satisfactory. The process can be repeated using different criteria, different parameters or different parameter values.
[0184] In one mode, metadata about the created management zones is stored. In addition, a test can be performed to determine whether the process of delineating management zones needs to be repeated. If the design process has to be repeated, then the design of the management zones is repeated in block 710.
[0185] 3.4.4.1. IDENTIFICATION OF MANAGEMENT ZONES [0186] In one embodiment, the process of delineating
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73/99 management zones are performed for values other than a management class count. A management class refers to areas in a field that have relatively homogeneous production limiting factors, but which are not restricted to be spatially contiguous. In concept, several management zones that are spatially separated from each other can belong to the same management class and can be operated in the same way.
[0187] Figure 8 represents an example method for creating management zones for an agricultural field. In step 810, a first count value for counting management classes from a plurality of management classes is determined. Selecting a first count value for the management classes may include selecting a number of management classes that have been shown in the past to be an ideal number of classes for creating zones. A count of management classes corresponds to an adjustment parameter described previously.
[0188] An ideal number of management classes can be discovered using a variety of approaches. According to one approach, an ideal number of management classes is selected when using training production maps every year at the same time. In this approach, a clustering algorithm is applied to the smoothed training production maps with different number of classes and for each class. Then a training zone quality measure for each class number is determined and used to identify an ideal number of classes.
[0189] According to another approach, an ideal number
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74/99 management classes are selected when performing a one-year cross-validation approach to training production maps.
[0190] Once a first count value is determined for counting a plurality of classes, a first set of management zones is generated in step 820. The first set of management zones can be generated, for example, using a management zone design process that is performed using either a clustering approach or a merging of regions approach. Examples of a clustering approach may include centroid-based multiple variable clustering approaches, such as a K-means approach and a diffuse C-means approach. Examples of a region fusion approach may include agglomerating region fusion approaches, such as a hierarchical region-based segmentation approach.
[0191] 3.4.4.2. K-MEANS APPROACH [0192] In one embodiment, the process of delineating management zones is implemented using the Kmeans approach, which aims to partition a set of observations from production data into k clusters where each observation belongs to the cluster with the nearest average. A benefit of using the K-means approach in the management zone design process is its simplicity, but K-means does not consider spatial locations of production data observations within the field. As a result, a direct output from the K-means cluster is the management class labels for each pixel i, and some additional steps may be necessary to
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75/99 spatially identify contiguous zones. In addition, it is recommended to use well pre-processed production maps before using the K-means approach. If the production maps are insufficiently pre-processed, then the results produced by the K-means approach can include many small fragmented zones.
[0193] 3.4.4.3. REGION FUSION APPROACH [0194] In one embodiment, the management zone design process is programmed to use segmentation based on hierarchical region. In this approach, two zones are neighbors to each other if, and only if, at least a pair of pixels between the two zones are neighboring pixels based on the 4 closest neighbors rule.
[0195] One benefit of the region fusion approach is that it uses a spatial location of production observations when creating management zones. The approach is expected to generate spatially contiguous zones in a natural way unless the inequality threshold is set too rigid or the production maps are also coarse. Furthermore, as the inequality threshold e is a continuous adjustment parameter, like the opposite ak, which assumes only positive integers in K-means or diffuse C-means, the hierarchical region fusion algorithm may have more flexibility to fine-tune the resulting zone design, and satisfy the different needs of different farmers.
[0196] Another benefit of the region fusion approach is that the region fusion algorithm generates zone labels directly without class labels.
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76/99 [0197] However, although the region fusion approach may not include additional processing to present management zones, some further processing of zone properties may be recommended.
[0198] In step 830, a test is performed to determine whether a count of management classes is to be changed. If the count has to be changed, then step 840 is performed. Otherwise, the steps described in figure 9 are performed.
[0199] In step 840, a second count value for a count of management classes out of a plurality of management classes is determined, and steps 820-840 are repeated for the second count value.
[0200] 3.4.5. POST-PROCESSING [0201] In one embodiment, a set of management zones is post-processed, for example, to remove small isolated zones to ensure that all zones are spatially contiguous and are reasonably sized. Post-processing can also be performed to remove small fragmented zones. Even with spatial smoothing of production maps during the production data pre-processing phase, the set of management zones can include small fragmented zones that can be difficult to manage individually.
[0202] In one mode, a test is performed to determine if a zone size is less than a user defined threshold. If the size of the zone is less than the threshold s, then the zone is merged with its larger neighboring zone more similar which is larger than the small zone. The label
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77/99 zone / class of the large zone can be assigned to the molten zone.
[0203] If the class labels are obtained from the diffuse K-means or C-means approaches, however, two additional steps can then be performed. For example, before cleaning zones, a set of zones can be built based on class labels and the spatial location of pixels so that the size and neighboring zones
from each zone in management can to be identified. After the cleaning in zones, labels in classes can to be recovered of built assembly, and mergers of zones
additional steps can be taken.
[0204] Figure 9 represents a method for postprocessing management zones. In step 910, a test is performed to determine if any small zones are present near a large zone in a set of management zones.
[0205] If in step 920 it is determined that a small zone close to a large zone is not present in a set of management zones, then in step 930 the set of management zones is stored. The set of management zones can be stored on a storage device, a memory unit, a cloud storage service, or any other storage device. The set of management zones can be used to determine sowing recommendations for farmers, for research purposes and to provide information to other agencies.
[0206] However, if in step 920 it is determined that at least one small zone is present close to a zone
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78/99 large in a set of management zones, then the small zones are merged with their respective large zones.
[0207] Merging of zones can be performed for each small zone identified, as indicated in steps 950-960. Once all the identified small zones are merged with their respective large zones, in step 970 the resulting set of merged management zones is stored. The set of fused management zones can be stored on a storage device, a memory unit, a cloud storage service, or any other storage device. The set of management zones can be used to determine sowing recommendations for farmers, for research purposes and to provide information to other agencies.
[0208] 3.5. PERFORMANCE CONSIDERATIONS [0209] Accuracy of delineation of management zones in an agricultural field can be increased with additional data. For example, assuming the quality of production maps is comparable from year to year, the quality and accuracy of the approach increases proportionally with the number of production maps from different years provided for the system. Consequently, for a given field, the more years of production maps are provided the greater the
quality and precision in outline in zones in management.[0210] 4. UTILITY IN DESIGN IN ZONES IN
MANAGEMENT [0211] Using the techniques described in this document, a
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79/99 computer can determine a plurality of management zones based on digital data representing historical productions harvested from an agricultural field. The techniques can empower computers to determine contiguous regions that have similar limiting factors influencing crop yields. The techniques presented can also enable the agricultural intelligence computing system to automatically generate recommendations for farmers regarding sowing, irrigation, fertilizer application such as nitrogen and / or harvesting.
[0212] Presented techniques can enable the agricultural intelligence computing system to save computational resources, such as data storage, computing power and system computer memory, by implementing a programmable chain configured to automatically determine management zones for a field based on digital data. The programmable chain can automatically generate recommendations and alerts for farmers, insurance companies and researchers, thereby allowing more effective agricultural management in sowing schedules, agricultural equipment operations and application of chemicals to fields, plantation protection and other tangible steps in agricultural field management. Management zones created based on historical production data can be particularly useful in certain agricultural practices such as selecting a sowing rate. For example, information about the management zones created can be used to generate recommendations for farmers. The recommendations
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80/99 can refer to seed and sowing selections. Selecting a recommended sowing rate based on the identified management zones can be very useful in increasing harvested yields.
[0213] 5. APPLICATION OF EXAMPLE TO OUTLINE MANAGEMENT ZONES AND GENERATE RECOMMENDATIONS [0214] The management zone design approach described in this document can be widely implemented in a variety of agricultural applications. For example, the approach can be integrated with computer-based tools that a farmer can use to optimize his agronomic practices. The approach can be implemented in an application that generates a graphical user interface for a user, and displays recommendations and strategy options for the farmer.
[0215] In one embodiment, a process of delineating management zones for an agricultural field is implemented in an interactive computer-based tool. The tool can provide a user with interactivity in terms of providing functionality to select an agricultural field, request and receive graphic representations of management zones outlined for the field, request and receive recommendations for agronomic practices for the management zones, and modify the recommendations obtained.
[0216] A management zone design tool can be implemented as a graphical user interface that is configured to receive a selection of an agricultural field from a user and execute a management zone design algorithm based on
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81/99 at the incoming entry. The graphical user interface can be configured to generate graphical representations of the outlined zones, display the graphical representations generated from the zones, and interact with the user to generate recommendation options.
[0217] In one embodiment, a management zone design application is configured to allow farmers to create manual scripts containing settings and parameters to specify details for designing management zones. The application can also provide a set of predefined script scenarios and make the set available to the farmer. The scenarios may include a scenario that provides information about, for example, predicted production if the farmer does not change his current agronomic practice. Another scenario can provide recommendations for achieving the best economic results. Another scenario may include a scenario providing recommendations for achieving maximum field production. These example scenarios can allow a farmer to compare different agronomic practices with reference to the field, compare production results if different practices are applied, and in the end choose the recommendations or scenario that best matches his goals. An example of an application that implements management zone design and recommendation generator is The Climate Corporation's Script Creator.
[0218] 5.1. EXAMPLE USES AND OPERATIONS [0219] In one embodiment, an application that integrates a management zone design approach and generates agronomic recommendations is configured to allow
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82/99 a farmer generates scripts quickly and easily to obtain recommendations for the areas outlined for the farmer's field. A script, or prescription, is a set of recommendations generated by the application for a farmer. A script can be generated based on input provided by a user and including a set of definitions that the application can use to outline management zones and generate recommendations. Definitions can include values for a count of management zones to be outlined, an identifier of the seeds to be sown, expected yield, a sowing range and more.
[0220] In one embodiment, a management zone design and recommendation application is configured to generate one or more custom scripts for a particular agricultural field. The scripts can reflect risk tolerance and objectives specified by a farmer.
[0221] Figure 10 is a screen snapshot of an example graphical user interface configured to outline management zones and generate recommendations for agronomic practices. Sample screen snapshot 1000 can be generated by executing instructions that provide interactivity between a user and the application. A typical user of the application is a farmer who cultivates an agricultural field. Executing the instructions can allow a farmer to import 1002 certain information about an agricultural field for the application. Executing the instructions can also allow a farmer to request in 1004 an interactive tool that allows the farmer to define and display in the graphical user interface one or more planting plans for a farmer's agricultural field. Run the
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83/99 instruction can also induce generation and display of one or more recommended scripts for an agricultural field, allowing a farmer to select one or more scripts from the displayed scripts, and display recommendations associated with the selected scripts.
[0222] In terms of importing 1004 information into a management zone and prescription design application, the application can be configured to allow a farmer to import soil information into the application and link the imported information with the outlined zones . The application can also be configured to retrieve and use information from management zones, for example, from SSURGO maps, zone design generated by a farmer in the past, old prescription, and any type of information the farmer used in the past to design zones management. For example, a farmer may be instructed to provide information regarding the types of seeds he plans to sow in his field. To facilitate inputting the information, a 1022 extraction menu can be provided to allow the farmer to make the selection.
[0223] A farmer can also be provided with a 1024 extraction menu that allows the farmer to research the types of seeds, including hybrids and more. In addition, a farmer may be presented with a text field to enter, for example, a target production quantity 1032 expected in a given year, a lower sowing rate 1034 normally used in the field, an average sowing rate 1036 used normally in the field, and a higher seeding rate normally used in the field.
[0224] The application can also allow a farmer
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84/99 navigate through data entry screens. For example, the application can be configured to allow a farmer to return in 1042 to a screen with the information previously entered, or to go forward in 1044 and enter additional information. The application can also be configured to allow a farmer to restore the settings and information previously provided.
[0225] 5.2. EXAMPLE WORKFLOW [0226] Figure 11 represents an example method for delineating management zones and generating prescriptions. The example method can be implemented in an application running on a computing device, such as a laptop, smart phone, tablet, PDA or other computing device.
[0227] In one embodiment, an application includes instructions for a graphical user interface generator, an eyeliner and a prescriber. The graphical user interface generator can be configured to generate and display a graphical user interface on a display of a computing device, receive input from a user, and display results generated by the eyeliner and the prescriber. The outliner can be configured to generate management zones for an agricultural field and based on data provided by a user. The prescriber can be configured to generate prescriptions for agricultural practices and recommendations designed to achieve objectives set by the user for the user's agricultural field.
[0228] In step 1102, a graphical user interface is generated and displayed on a display of a farmer's computing device. A graphical user interface
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An example 85/99 is shown in figure 10. The graphical user interface can be implemented as a web page of a web site that the farmer can access via the Internet. The network page can include various keys, icons and interactive extraction menus to provide data for the application configured to execute the method for delineating management zones and generating prescriptions. The farmer can use the keys, icons and interactive menus to provide parameter values to be used by the eyeliner and the prescriber.
[0229] In step 1104, values for one or more parameters for an outliner and / or for a prescriber are received through a graphic user interface of a farmer. For example, the values can specify an agricultural field for which management zone design is requested. The values can also specify the farmer's objectives in terms of expected profits, quantities and types of seeds for the field, sowing rates and more. Examples of the parameters are shown in figure 10.
[0230] In step 1106, values received through a graphical user interface are used to start an outline that is configured to generate a plurality of management zones based, at least in part, on the values provided by a farmer.
[0231] In step 1108, a test is performed to determine whether a farmer wants to provide any additional values for one or more parameters for an outliner and / or for a prescriber. For example, the farmer can provide some additional values for parameters
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Additional 86/99, or modify the values already provided. In addition, the farmer can request resetting the values to default values provided by the application, or can import the values from the farmer's files, publicly available databases, the farmer's previous settings and more.
[0232] If it is determined that a farmer wants to provide additional values for parameters for the eyeliner and / or for a prescriber, then in step 1110 the additional values for the parameters are received via a graphical user interface, and step 1106 is performed.
[0233] However, if it is determined that additional values will not be provided, then in step 1112 a plurality of outlined management zones are generated and a plurality of planting plans are generated. Management zones can be determined by an outliner based, at least in part, on values provided, for example, by a farmer through a graphical user interface. Planting plans can be generated by a prescriber based, at least in part, on the values provided by the farmer. Planting plans can be customized for individual management zones and based on the targets and objectives specified by a farmer. Examples of outlined management zones and planting plans are shown in figure 12.
[0234] Figure 12 is a screen snapshot of an example graphical user interface configured to display examples of management zones and examples of planting plans. The sample interface shows three
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87/99 examples of management zones outlined for a particular agricultural field; however, the approach is not limited to showing three examples. The approach may allow you to specify a number of ways in which a particular field can be divided into management zones. For example, a user can specify that he would like to see the two best ways of dividing the particular field into zones. The user can also specify that he would like to see the three best ways, or the four best ways, of dividing the field into zones and so on.
[0235] The examples shown in figure 12 include a first set of management zones 1210, a second set of management zones 1212 and a third set of management zones 1214. The first set of management zones 1210 includes zone 2 ( element 1232), zone 3 (element 1234), zone 4 (element 1236) and zone 5 (element 1238). The second set of management zones 1212 includes zone 1 (element 1230), zone 2 (element 1232), zone 3 (element 1234), zone 4 (element 1236) and zone 5 (element 1238). The third set of management zones 1214 includes zone 1 (element 1230), zone 2 (element 1232), zone 3 (element 1234), zone 4 (element 1236) and zone 5 (element 1238). The zones are plotted using different shades or colors. Distribution and quantity of zones for other fields may differ from those shown in figure 12.
[0236] In addition to graphical representations of outlined management zones, planting plans and / or yields expected for each management zone arrangement can
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88/99 be provided. The additional information may indicate a relationship between a particular planting approach and expected yield. For example, for the first set of management zones 1210, additional information may include an average seed population 1240, an amount of seed bags 1242 and a relationship between the seed population and expected yield. The relationship can be represented using a two-dimensional graph; however, other ways of representing the relationship can also be employed. The graph represented in figure 12 includes a horizontal axis 1260 labeled as a seed population, and a vertical axis 1250 labeled as a target production. The data points obtained for various values of the seed populations are represented as a first data point 1252, a second data point 1254 and a third data point 1256. Other ways of representing the data points for the relationship between populations of seeds and yields can also be implemented.
[0237] A farmer can analyze the data shown in figure 12, to compare the three different ways of delineating management zones, and to compare the expected yields generated for the different ways of delineating management zones, respectively. In addition, the farmer, for example, may decide to adjust some of the initial values. To do so, the farmer can select a 1270 icon that is labeled Previous, and provide additional values for parameters and modify some of the values already provided.
[0238] A farmer can also select one of three sets 1210, 1212, 1214, and request an agricultural prescription
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89/99 which, if implemented for the set of management zones selected, will allow to achieve the objectives indicated for the selected set.
[0239] A selection of a particular delineated management set, of the plurality of available delineated management sets, can be performed in many ways. One mode is shown in figure 13.
[0240] Figure 13 is a screen snapshot of an example graphical user interface configured to enable requesting a prescription for a selected planting plan. The sample interface shows three examples of management zones outlined for a particular agricultural field; however, the approach is not limited to showing three examples. The displayed arrangements correspond to three different modes, or options, of delineating management zones for the same field. For each option, some additional information can be provided.
[0241] The three examples shown in figure 13 include a first set of management zones 1310, a second set of management zones 1312 and a third set of management zones 1314. The first set of management zones 1310 includes zone 2 (element 1332), zone 3 (element 1334), zone 4 (element 1336) and zone 5 (element 1338). The second set of management zones 1312 includes zone 1 (element 1330), zone 2 (element 1332), zone 3 (element 1334), zone 4 (element 1336) and zone 5 (element 1338). The third set of management zones 1314 includes zone 1 (element 1330), zone 2 (element 1332), zone 3 (element 1334), zone 4 (element 1336) and zone 5 (element 1338).
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The zones are plotted using different shades or colors. Each set of management zones outlined can include additional information. For example, additional information for the first set of management zones 1210 may include an average seed population 1340 and a quantity of seed bags 1342.
[0242] In one mode, a graphical user interface can include keys, radio buttons, icons or other types of selectors to select an option from the options displayed on the interface. In the example shown in figure 13, the graphical user interface includes the keys labeled option 1, option 2 and option 3. A farmer can select any of the option keys to select the option, and thus indicate a particular set of zones. management plans for which the farmer is requesting an agronomic prescription.
[0243] Referring again to figure 11, in step 1114, a test is performed to determine whether any of a plurality of management zone options has been selected by a farmer. If in step 1116 it is determined that a particular set of outlined management zones has been selected, then step 1118 is performed. Otherwise, step 1122 is performed.
[0244] In step 1118, a prescriber is called to generate a prescription for a selected management zone option. In one embodiment, a prescription corresponds to a planting plan and indicates recommendations for achieving certain objectives. In this step, the prescriber can generate one or more prescriptions for the farmer. Prescriptions can provide recommendations for achieving different goals.
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91/99 [0245] In one mode, the application of the design of management zones and recommendations is configured to generate one or more custom scripts for a particular agricultural field. The scripts can reflect risk tolerance and objectives specified by a farmer. The application can also generate recommendations based on two or more scripts, and thus allow a farmer to compare the impact of different objectives on the farmer's script, and select the recommendations that best satisfy the farmer.
[0246] In one embodiment, the application of management zone design and recommendations is configured to allow a farmer to generate a script that maximizes the return on investment (ROI) that the farmer can receive based on the profits generated by his field . The application can also allow a farmer to determine recommendation for the sowing population to maximize profits and obtained by weighing costs and risks against potential increases in production. The application can also allow a farmer to enter an expected seed price in terms of dollars per thousand seeds, or in terms of dollars per seed bag.
[0247] In addition, a farmer can specify his expected market price in terms of dollars per bushel. He can also request generation of a script that maximizes production if a given hybrid seed is planted in the farmer's field. The farmer can also request the creation of a script that represents his existing business practices.
[0248] Referring again to figure 11, in step 1120, one or more prescriptions are generated and displayed for a
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92/99 farmer. Prescriptions can be displayed using a graphical user interface. Prescriptions can be displayed so that the farmer can make comparisons using the displayed prescription, and clearly see the differences between the scripts. The comparison can include information about a range of seed populations, a range of target yields, a total seed bag count, and a population map with a fixed legend. Examples of different prescriptions are shown in figure 14.
[0249] Figure 14 is a screen snapshot of an example graphical user interface configured to display examples of management zones and examples of planting plans. The sample interface shows three examples of sets of management zones outlined for a particular agricultural field. However, the approach is not limited to showing three examples. The displayed sets correspond to three different modes, or options, of delineating management zones for the same field. For each option, some additional information can be provided. Additional information may include planting plans or recommendations for achieving certain agricultural objectives.
[0250] The three examples shown in Figure 14 include three options for outlining a particular agricultural field. Either option can be selected using, for example, a radio selection button or button. For example, a first set of management zones can be selected by pointing to a 1410 key, a second set of management zones can be selected by pointing to a 1412 key, and a third set of management zones can be selected when
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93/99 point to a key 1414. The first set of management zones includes zone 2 (element 1432), zone 3 (element 1434), zone 4 (element 1436) and zone 5 (element 1438). The second set of management zones includes zone 1 (element 1430), zone 2 (element 1432), zone 3 (element 1434), zone 4 (element 1436) and zone 5 (element 1438). The third set of management zones includes zone 1 (element 1430), zone 2 (element 1432), zone 3 (element 1434), zone 4 (element 1436) and zone 5 (element 1438). Zones can be represented graphically using different shades or colors.
[0251] Each of the sets of management zones displayed to a user can be selected for the user based on certain criteria. For example, the first set can be selected for the user based on information corresponding to current agricultural practice. Therefore, this plan can be referred to as a current plan. The second set can be selected for the user to provide the user with a planting plan to maximize revenue and recommendations to allow the user to achieve a revenue maximization goal. The third set can be selected for the user to provide the user with a planting plan to maximize production and to provide the planting plan to allow the user to achieve a production maximization goal.
[0252] In one mode, a user can select one of the sets of management zones displayed in a GUI. In the example shown in figure 14, a user selected the third set by pointing to a displayed 1414 key
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94/99 near the third set. In response to receiving the particular selection, the GUI can display additional information, including recommendations to help the user achieve a certain goal. Additional information can also include data representing expected production, cost and revenue. In addition, additional information for the third set of management zones 1414 may include information regarding a target yield 1420, a type of hybrid seed selection 1422, an expected corn price 1424, a seed cost per bag 1426, a seed cost per bag 1434 and a total seed cost 1436.
[0253] Referring again to figure 11, in step 1122, a test is performed to determine whether a farmer has requested any modification of values used by an outliner and / or prescriber. If in step 1124 it is determined that no changes to the values of one or more parameters have been requested, then step 1114 is performed. However, if it is determined that some changes have been requested then step 1104 is performed.
[0254] 5.3. EXAMPLE OF AUTOMATIC SCRIPTS CREATION [0255] A management zone design and recommendation application can be configured to provide an autoscript option. An autoscript option is an application feature that allows the user to request a prescription for agricultural practice. For example, the application can be configured to allow the user to modify the parameters used by the application and request that the application generate an outline map of management zones and recommendations. The application can also be
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95/99 configured to allow the user to fine-tune prescriptions generated by the application.
[0256] In one mode, the application of management zone design and recommendations is configured to allow a farmer to use an autoscript option. An autoscript option allows the farmer to request that at least the top three prescriptions are generated for the farmer automatically. In addition, the farmer can request that the autoscript option be selected for the farmer each time the farmer is using the application.
[0257] In addition, a farmer can select a particular source of data to be used by the application. For example, a farmer can rely on the SSURGO soil map more than on his production data. Therefore, the farmer may be able to indicate that the SSURGO soil map data should be used for application in each field when an autoscript option is called, or when the farmer manually requests a script, or when the farmer requests an old prescription.
[0258] In one embodiment, the application of management zone design and recommendations is configured to allow a farmer to exclude certain years of production data from the calculation, provided that a sufficient amount of data from other years is available to perform the calculations.
[0259] In addition, the application can be configured to allow a farmer to include personalized population recommendations for a specific type of seed or plantation. For example, the application can be
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6/99 configured to include personalized population recommendations for a set of certified Monsanto hybrids. Monsanto hybrids refers to all hybrids that have undergone the GENV test and for which data models exist to generate the recommendations. Examples of Monsanto hybrids include brands such as Dekalb, Channel, Regional Brands and products from Agreliant and Croplan.
[0260] 5.4. MANUAL SCRIPTS CREATION EXAMPLE [0261] A management zone design and recommendation application can be configured to provide an option for manual scripting. An option for manually creating scripts is an application feature that allows the user to customize parameters used by the application. Customization can include fine-tuning manually, for example, a counting class, management zone design options and prescriptions generated by the application for an agricultural field. For example, the application can be configured to allow the user to modify the parameters used by the application and to fine-tune options for designing management zones and recommendations. For example, the application can also be configured to allow a user to fine-tune parameters of the management zone design algorithm, request regeneration of management zone design options and request regeneration of prescriptions for the options.
[0262] Figure 15 is a screen snapshot of an example graphical user interface configured to allow a user to customize the planting plan. IS
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97/99 assumed here that a user has already selected a 1570 private management zone design option. The 1570 design option represents a particular agricultural field divided into a set of management zones. The set includes a zone 1 labeled 1530, a zone 2 labeled 1532, a zone 3 labeled 1532, a zone 4 labeled 1536 and a zone 5 labeled 1538.
[0263] In one embodiment, a management zone design and recommendation application is configured to provide functionality that allows a user to merge zones by selecting a merge zones 1550 key, split zones by selecting a 'split zones 1552 key, draw / save / cancel a shape within a set of management zones by selecting a draw shape 1554 key, placing a square when selecting an option placing square 1556, and placing an axis when selecting an option placing axis 1558. Other options can also be implemented by applying management zone design and recommendations.
[0264] In one mode, the application of the design of management zones and recommendations is configured to allow a user to manually generate a script. An example of the process for manually generating a script is shown in figure 15. The process for manually generating a script can be facilitated using a GUI.
[0265] Using the features of a GUI, a user can indicate the name of the data file that the user wants to import into the application. For example, in 1510 the user can supply a data file name containing production data. In addition, the user can indicate in
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1512 the type of seed to be used. In addition, the user can indicate in 1514 whether any liquid is to be used, and in 1516, 1518 in which zones it should be used. The user can also enter in 1520 the expected target production and in 1522 the expected seed population for one of the zones, and in 1524 the expected target production and in 1524 the expected seed population for another zone and so on. In response to receiving user input, the application can generate a prescription for the user. The information can be displayed if a user selects, for example, a summary icon 1546. Additional information, such as an average seed population 1542 and a number of bags with seeds 1544, can also be displayed in a GUI for a user.
[0266] 6. EXTENSIONS AND ALTERNATIVES [0267] In one embodiment, a process for delineating management zones for an agricultural field is improved by taking into account not only historical production maps, but also weather forecast information. In this approach, meteorological information can be used to provide explanations for inconsistencies in observations of productions recodified on maps of historical productions.
[0268] A process for delineating management zones for a field can be improved by providing information regarding soil properties and topographic properties of individual zones delineated in a field. Usually, permanent soil and topographic properties play an important role in determining variability in subfield production, and can sometimes be more important than transient factors such as weather conditions.
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99/99 [0269] Accuracy of results generated by a process to delineate management zones can be improved by providing sufficient historical production data or subfield production maps for the system. The accuracy of the results generated can also be improved when historical production data is provided in a particular data format or is pre-processed in particular.

权利要求:
Claims (20)
[1]
CLAIM
1. Method, characterized by the fact that it comprises: using instructions programmed into a computer system comprising one or more processors and computer memory: receiving production data representing crops production that have been harvested from an agricultural field, and characteristic data representing one or more characteristics of the agricultural field;
use the instructions programmed in the computer system, determine a plurality of management zone design options, where each option, from the plurality of management zone design options, comprises digital zone layout data for an option, where the plurality of management zone design options is determined by: determining a plurality of count values for a management class count; for each count value, of the plurality of count values, generate an option of delineation of management zones when grouping, using a count value of the plurality of count values, the production data and the data of field characteristics, designating zones for groupings, and include the option of delineating management zones in the plurality of delineation options of management zones;
use the instructions programmed in the computer system, receive one or more selection criteria; and based, at least in part, on one or more selection criteria, select one or more options from the plurality of management zone design options, and determine one or more planting plans for each of the one or more options;
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[2]
2/9 use a computer system presentation layer, generate and induce display, on a computing device, of a graphical representation of the one or more options of the plurality of management zone design options and a graphical representation of the one or more planting plans associated with one or more options.
2. Method, according to claim 1, characterized by the fact that it additionally comprises, using the instructions programmed in the computer system, receiving a user input; use user input to generate the one or more selection criteria; use one or more selection criteria to select one or more options from the plurality of management zone design options; Based, at least in part, on one or more selection criteria, determine the one or more planting plans for the one or more options.
[3]
3. Method according to claim 2, characterized by the fact that it additionally comprises receiving one or more of: instructions for merging zones, instructions for dividing zones, instructions for modifying zones, instructions for selecting seeds, instructions for target yields or instructions for planting historic seeds;
use the instructions programmed into the computer system, based, at least in part, on the instructions for target yields, determine, for each option from one or more options, interrelationships between target yields and planting recommendations, and display the interrelationships relationships in a graphical form on the computing device.
[4]
4. Method according to claim 3,
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3/9 characterized by the fact that it additionally comprises: using the instructions programmed in the computer system, based, at least in part, on the instructions for target productions and on a seed cost, determining, for each option of the one or more options , interrelationships between target yields, planting recommendations and planting costs, and display the interrelationships in a graphical form on the computing device.
[5]
5. Method, according to claim 3, characterized by the fact that it additionally comprises: using the instructions programmed in the computer system, based, at least in part, on the instructions for target productions, on the instructions for selecting seeds and on a seed cost, determine, for each option of one or more options, interrelationships between target yields, planting recommendations and planting costs, and display the interrelationships in a graphical form on the computing device.
[6]
6. Method, according to claim 3, characterized by the fact that it additionally comprises: using the instructions programmed in the computer system, based, at least in part, on the instructions for dividing zones, determining a second plurality of options for design of management zones; determine one or more second criteria; and based on one or more second criteria, select one or more second options from the second plurality of management zone design options.
[7]
7. Method, according to claim 1, characterized by the fact that it additionally comprises, using the instructions programmed in the computer system,
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4/9 pre-process production data and field characteristics data by: generating transformed data by applying an empirical cumulative density function to production data and field characteristics data; generate smooth transformed data by smoothing the transformed data; generate the plurality of options for designing management zones from the smooth transformed data.
[8]
8. Method, according to claim 1, characterized by the fact that it additionally comprises using the instructions programmed in the computer system, post-processing the plurality of options for designing management zones by merging one or more small management zones , included in the plurality of management zone design options, with their respective large neighboring zones in a plurality of fused management zone design options.
[9]
9. Method, according to claim 1, characterized by the fact that the data of field characteristics for the agricultural field comprise one or more of: data of soil properties, data of topographic properties, one or more base maps geographic data from soil surveys, one or more bare soil maps, or one or more satellite images; where data on soil properties comprise data from soil measurements; where topographic property data comprises elevation and property data associated with elevations.
[10]
10. Method according to claim 1, characterized by the fact that it additionally comprises,
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5/9 use the instructions, and based on one or more options of the plurality of management zone design options and on one or more planting plans associated with one or more options, induce triggering of one or more of: one seeding apparatus, an irrigation apparatus, an apparatus for application of fertilizers such as nitrogen, or a harvesting apparatus for carrying out, respectively, sowing, irrigation, application of fertilizers and / or harvesting of the agricultural field according to an option of the one or more options.
[11]
11. Data processing system, characterized by the fact that it comprises:
a memory;
one or more memory-attached processors and programmed to:
receiving production data representing plantation yields that have been harvested from an agricultural field, and field characteristic data representing one or more characteristics of the agricultural field;
determine a plurality of management zone design options, where each option, of the plurality of management zone design options, comprises digital data for an option, where the plurality of management zone design options is determined by means of: determining a plurality of count values for a count of management classes; for each count value, of the plurality of count values, generate an option of delineating management zones when grouping, using a count value of the plurality of count values, the
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6/9 production data and field characteristics data, designating zones for groupings, and including the option of delineating management zones in the plurality of delineation options of management zones;
receive one or more selection criteria; and based, at least in part, on one or more selection criteria, select one or more options from the plurality of management zone design options, and determine one or more planting plans for each of the one or more options;
generate and induce the display, on a computing device, of a graphical representation of one or more options from the plurality of management zone design options and a graphical representation of one or more planting plans associated with one or more options.
[12]
12. Data processing system, according to claim 11, characterized by the fact that the one or more processors are additionally programmed to:
receive user input;
use user input to generate the one or more selection criteria;
use one or more selection criteria to select one or more options from the plurality of management zone design options;
Based, at least in part, on one or more selection criteria, determine the one or more planting plans for the one or more options.
[13]
13. Data processing system, according to claim 12, characterized by the fact that the one or more processors are additionally programmed to:
Petition 870190104737, of 10/17/2019, p. 13/17
7/9 receive one or more of: instructions for merging zones, instructions for dividing zones, instructions for modifying zones, instructions for selecting seeds, instructions for target yields, or instructions for planting historic seeds;
based, at least in part, on the instructions for target yields, determine, for each option from one or more options, interrelationships between target yields and planting recommendations, and display the interrelationships in a graphical form on the computing device .
[14]
14. Data processing system, according to claim 13, characterized by the fact that the one or more processors are additionally programmed to:
based, at least in part, on instructions for target yields and on a seed cost, determine, for each option from one or more options, interrelationships between target yields, planting recommendations and planting costs, and display the interrelationships -relationships in a graphical form on the computing device.
[15]
15. Data processing system, according to claim 13, characterized by the fact that the one or more processors are additionally programmed to:
based, at least in part, on the instructions for target yields, on the instructions for selecting seeds and on a seed cost, determine, for each option of the one or more options, interrelationships between target yields, planting recommendations and costs planting, and display the interrelations in a graphical form on the computing device.
[16]
16. Data processing system, according to
Petition 870190104737, of 10/17/2019, p. 14/17
8/9 to claim 13, characterized by the fact that the one or more processors are additionally programmed to:
based, at least in part, on the zoning instructions, determine a second plurality of management zone design options; determine one or more second criteria; and based on one or more second criteria, select one or more second options from the second plurality of management zone design options.
[17]
17. Data processing system, according to claim 11, characterized by the fact that the one or more processors are additionally programmed to:
pre-process production data and field characteristics data by: generating transformed data by applying an empirical cumulative density function to production data and field characteristics data; generate smooth transformed data by smoothing the transformed data; generate the plurality of options for designing management zones from the smooth transformed data.
[18]
18. Data processing system, according to claim 11, characterized by the fact that the one or more processors are additionally programmed to:
post-process the plurality of management zone design options by merging one or more small management zones, included in the plurality of management zone design options, with their respective large neighboring zones in a plurality of zone design options merged management systems.
[19]
19. Data processing system, according to
Petition 870190104737, of 10/17/2019, p. 15/17
9/9 claim 11, characterized by the fact that the data of field characteristics for the agricultural field comprise one or more of: data of soil properties, data of topographic properties, one or more maps of geographic databases of surveys of soils, one or more bare soil maps, or one or more satellite images; where data on soil properties comprise data from soil measurements; where topographic property data comprises elevation and property data associated with elevations.
[20]
20. Data processing system, according to claim 11, characterized by the fact that the one or more processors are additionally programmed to:
based on one or more options of the plurality of management zone design options and one or more planting plans associated with one or more options, induce triggering of one or more of: a sowing device, an irrigation device , a device for applying fertilizers such as nitrogen, or a device for harvesting, respectively, for sowing, irrigating, applying fertilizers and / or harvesting the agricultural field according to an option from one or more options.

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同族专利:

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公开号 | 公开日

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US20220015308A1|2022-01-20|

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WO2018093931A1|2018-05-24|

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ZA201903636B|2020-12-23|

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AU2017362975A1|2019-06-20|

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US20180132422A1|2018-05-17|

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US20200008371A1|2020-01-09|

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US10398096B2|2019-09-03|

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EP3542325A4|2020-05-06|

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EP3542325A1|2019-09-25|

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US11160220B2|2021-11-02|

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CA3044072A1|2018-05-24|

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AR110097A1|2019-02-20|

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法律状态:
2021-10-13| B350| Update of information on the portal [chapter 15.35 patent gazette]|

优先权:

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申请号 | 申请日 | 专利标题

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US15/352,898|US10398096B2|2016-11-16|2016-11-16|Identifying management zones in agricultural fields and generating planting plans for the zones|

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PCT/US2017/061846|WO2018093931A1|2016-11-16|2017-11-15|Identifying management zones in agricultural fields and generating planting plans for the zones|

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