WO2017074692A1 - Computer-implemented calculation of corn harvest recommendations - Google Patents
Computer-implemented calculation of corn harvest recommendations Download PDFInfo
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- WO2017074692A1 WO2017074692A1 PCT/US2016/056012 US2016056012W WO2017074692A1 WO 2017074692 A1 WO2017074692 A1 WO 2017074692A1 US 2016056012 W US2016056012 W US 2016056012W WO 2017074692 A1 WO2017074692 A1 WO 2017074692A1
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Definitions
- the present disclosure relates to computer-implemented techniques for modeling grain moisture related to determining optimal harvest time for hybrid corn seeds based upon seed type, agricultural field data, and weather data.
- Harvested corn grain is classified into five grades, with the highest quality grade, Grade No. 1, being the most expensive.
- Classifying corn grain involves classifying a minimum weight per bushel and a percentage of damaged kernels per bushel.
- grain moisture is not used in determining corn grain quality grades, it is used to determine a sales price per bushel of a particular grade.
- Grain moisture refers to and is measured as the ratio of water mass to wet kernel mass, referred to herein as "wet-basis”. Grain moisture level is important to buyers because the level of moisture in grain can affect the amount of degradation of grain during storage and shipment. Therefore buyers generally request that grain moisture be around 15.5% or less. If a grower harvested corn that has higher than desired grain moisture, then buyers may demand a discount for the harvested corn. The cost may be significant.
- the economic impact of a grower who harvested 1000 lbs. of corn with grain moisture of 20% is that the price per bushel would be discounted based upon the harvest weight after drying down the harvest to the desired moisture level.
- drying down 1000 lbs. of harvest at a moisture level of 20% to a moisture level of 15.5% would shrink the overall harvest weight to approximately 955 lbs. This loss in total weight would equate to about a 4.5% write-down in value. Therefore the economic impact can be significant if a harvest is not at the desired grain moisture level.
- the R6 stage During maturation the amount of kernel moisture begins to slowly decrease.
- the R6 stage is also referred to as "black layer” because physiological maturity occurs when a black layer forms at the base of the kernels.
- the black layer is a hard starch layer that turns black or brown and cuts off the water and dry matter transfer to the kernel.
- the decrease in kernel moisture is primarily due to the rate of water loss from the kernel itself to outside air. This rate is referred to as grain dry down.
- Grain dry down is influenced by many factors.
- One such factor is the ambient air temperature and humidity. Higher humidity or cooler temperatures may slow grain dry down because there is less difference between the humidity in the kernel and the ambient air.
- Producers of hybrid corn seeds provide relative maturity ratings to help growers predict when to harvest their grain based upon the environment and the type of hybrid seed.
- Relative maturity is a method to classify a corn hybrid seed based on the mega-environment where it is planted.
- Relative maturity is a rating system that helps determine when a hybrid may be safely harvested with minimal harvest loss due to excessive moisture or kernel damage, usually based upon the assumption that grain moisture loss equals about 0.5 percentage points per day. Therefore two days of field drying equals one day of relative maturity.
- hybrid A is assigned a relative maturity of 110 and hybrid B is assigned a relative maturity of 114, and if hybrid A and hybrid B are planted on the same day, then it is understood that on average hybrid B has two percentage points more moisture than hybrid A when hybrid A completes its dry down. Growers use the relative maturity data to approximate when to harvest their corn based upon relative maturity values.
- FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate.
- FIG. 3 illustrates a programmed process by which the agricultural intelligence computer system generates one or more preconfigured agronomic models using agronomic data provided by one or more data sources.
- FIG. 4 is a block diagram that illustrates a computer system 400 upon which an embodiment of the invention may be implemented.
- FIG. 5 depicts an example programmed algorithm or process for determining optimal harvest time for a specific corn planted at a specific geo-location based upon target grain moisture content at harvest.
- FIG. 7 depicts an example programmed algorithm or process by which grain moisture logic is used to estimate grain moisture content for the specific hybrid seed at R6.
- FIG. 8 depicts an example programmed algorithm or process by which grain dry down logic is used to calculate daily dry down rates starting at R6.
- FIG. 9 depicts a graphical representation of a sample harvest recommendation model that may be displayed digital form on a computer display.
- FIG. 10 depicts an example embodiment of a timeline view for data entry.
- FIG. 11 depicts an example embodiment of a spreadsheet view for data entry.
- a computer system and computer-implemented techniques are provided for determining crop harvest times during a growing season based upon hybrid seed properties, weather conditions, and geo-location of planted fields.
- determining crop harvest times for corn fields may be accomplished using a server computer system that is programmed to receive, over a digital communication network, electronic digital data representing hybrid seed properties, including seed type and relative maturity, and weather data for the specific geo-location of the agricultural field.
- Weather data includes temperature, humidity, and dew point for a specified period of days.
- the system is programmed to create and store, in computer memory, an equilibrium moisture content time series for the specific geo-location that is based upon weather data..
- Equilibrium moisture content on a particular day represents the expected dry-basis equilibrium moisture content fraction that would be found in the kernel at the specific geo-location if an unlimited amount of time is allowed for moisture in the kernel and in air to exchange and reach equilibrium according to the weather condition of that particular day.
- the equilibrium moisture content is used to determine the rate of grain dry down based upon computer-implemented calculations of how strongly water vapor will dissipate from a kernel to open air for a specific day.
- the computer system is programmed to calculate and store in computer memory R6 moisture content for a specific hybrid seed based on a plurality of hybrid seed data.
- the R6 moisture content is important for determining the estimated starting moisture of the kernels for the grain dry down time series model that is discussed next.
- the computer system uses digitally programmed grain dry down logic to create and store in computer memory a grain dry down time series model for the specific hybrid seed at the specific geo-location that represents the estimated moisture content of the kernel over specified time data points.
- "Model” in this context, refers to a set of computer executable instructions and associated data that can be invoked, called, executed, resolved or calculated to yield digitally stored output data based upon input data that is received in electronic digital form. It is convenient, at times, in this disclosure to specify a model using one or more mathematical equations, but any such model is intended to be implemented in programmed computer-executable instructions that are stored in memory with associated data.
- the grain dry down time series is based upon the equilibrium moisture content time series, the estimated R6 date, the estimated R6 moisture content, and specific hybrid seed properties.
- the computer system is programmed to determine and display a harvest time recommendation for harvesting crop grown from a specific hybrid seed plant based on the grain dry down time series and the desired moisture level of the grower.
- FIG. 1 illustrates an example computer system that is configured to perform the functions described herein, shown in a field environment with other apparatus with which the system may interoperate.
- a user 102 owns, operates or possesses a field manager computing device 104 in a field location or associated with a field location such as a field intended for agricultural activities or a management location for one or more
- the field manager computer device 104 is programmed or configured to provide field data 106 to an agricultural intelligence computer system 130 via one or more networks 109.
- Examples of field data 106 include (a) identification data (for example, acreage, field name, field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, tract number, field number, section, township, and/or range), (b) harvest data (for example, crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, and previous growing season information), (c) soil data (for example, type, composition, pH, organic matter (OM), cation exchange capacity (CEC)), (d) planting data (for example, planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), (e) fertilizer data (for example, nutrient type
- An data server computer 108 is communicatively coupled to agricultural intelligence computer system 130 and is programmed or configured to send external data 110 to agricultural intelligence computer system 130 via the network(s) 109.
- the external data server computer 108 may be owned or operated by the same legal person or entity as the agricultural intelligence computer system 130, or by a different person or entity such as a government agency, non-governmental organization (NGO), and/or a private data service provider. Examples of external data include weather data, imagery data, soil data, or statistical data relating to crop yields, among others.
- External data 110 may consist of the same type of information as field data 106.
- the external data 110 is provided by an external data server 108 owned by the same entity that owns and/or operates the agricultural intelligence computer system 130.
- the agricultural intelligence computer system 130 may include a data server focused exclusively on a type of that might otherwise be obtained from third party sources, such as weather data.
- an external data server 108 may actually be incorporated within the system 130.
- An agricultural apparatus 111 has one or more remote sensors 112 fixed thereon, which sensors are communicatively coupled either directly or indirectly via agricultural apparatus 111 to the agricultural intelligence computer system 130 and are programmed or configured to send sensor data to agricultural intelligence computer system 130.
- Examples of agricultural apparatus 111 include tractors, combines, harvesters, planters, trucks, fertilizer equipment, unmanned aerial vehicles, and any other item of physical machinery or hardware, typically mobile machinery, and which may be used in tasks associated with agriculture.
- a single unit of apparatus 111 may comprise a plurality of sensors 112 that are coupled locally in a network on the apparatus; controller area network (CAN) is example of such a network that can be installed in combines or harvesters.
- CAN controller area network
- the apparatus 111 may comprise a cab computer 115 that is programmed with a cab application, which may comprise a version or variant of the mobile application for device 104 that is further described in other sections herein.
- cab computer 115 comprises a compact computer, often a tablet-sized computer or smartphone, with a color graphical screen display that is mounted within an operator's cab of the apparatus 111.
- Cab computer 115 may implement some or all of the operations and functions that are described further herein for the mobile computer device 104.
- the network(s) 109 broadly represent any combination of one or more data communication networks including local area networks, wide area networks, internetworks or internets, using any of wireline or wireless links, including terrestrial or satellite links.
- the network(s) may be implemented by any medium or mechanism that provides for the exchange of data between the various elements of FIG. 1.
- the various elements of FIG. 1 may also have direct (wired or wireless) communications links.
- the sensors 112, controller 114, external data server computer 108, and other elements of the system each comprise an interface compatible with the network(s) 109 and are programmed or configured to use standardized protocols for communication across the networks such as TCP/IP, Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS, and the like.
- Agricultural intelligence computer system 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 sensor data from remote sensor 112.
- Agricultural intelligence computer system 130 may be further configured to host, use or execute one or more computer programs, other software elements, digitally programmed logic such as FPGAs or ASICs, or any combination thereof to perform translation and storage of data values, construction of digital models of one or more crops on one or more fields, generation of recommendations and notifications, and generation and sending of scripts to application controller 114, in the manner described further in other sections of this disclosure.
- agricultural intelligence computer system 130 is programmed with or comprises a communication layer 132, presentation layer 134, data management layer 140, hardware/virtualization layer 150, and model and field data repository 160.
- Layer in this context, refers to any combination of electronic digital interface circuits,
- microcontrollers firmware such as drivers, and/or computer programs or other software elements.
- Communication layer 132 may be programmed or configured to perform input/output interfacing functions including sending requests to field manager computing device 104, external data server computer 108, and remote sensor 112 for field data, external data, and sensor data respectively.
- Communication layer 132 may be programmed or configured to send the received data to model and field data repository 160 to be stored as field data 106.
- Data management layer 140 may be programmed or configured to manage read operations and write operations involving the repository 160 and other functional elements of the system, including queries and result sets communicated between the functional elements of the system and the repository. Examples of data management layer 140 include JDBC, SQL server interface code, and/or HADOOP interface code, among others.
- Repository 160 may comprise a database.
- database may refer to either a body of data, a relational database management system (RDBMS), or to both.
- RDBMS relational database management system
- a database may comprise any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, and any other structured collection of records or data that is stored in a computer system.
- RDBMS examples include, but are not limited to including, ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases.
- ORACLE® MYSQL
- IBM® DB2 MICROSOFT® SQL SERVER
- SYBASE® SYBASE®
- POSTGRESQL databases any database may be used that enables the systems and methods described herein.
- field data 106 is not provided directly to the agricultural intelligence computer system via one or more agricultural machines or agricultural machine devices that interacts with the agricultural intelligence computer system
- the user may be prompted via one or more user interfaces on the user device (served by the agricultural intelligence computer system) to input such information.
- the user may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system) and selecting specific CLUs that have been graphically shown on the map.
- the user 102 may specify identification data by accessing a map on the user device (served by the agricultural intelligence computer system 130) and drawing boundaries of the field over the map. Such CLU selection or map drawings represent geographic identifiers.
- the user may specify identification data by accessing field identification data (provided as shape files or in a similar format) from the U. S. Department of Agriculture Farm Service Agency or other source via the user device and providing such field identification data to the agricultural intelligence computer system.
- the agricultural intelligence computer system 130 is programmed to generate and cause displaying a graphical user interface comprising a data manager for data input.
- the data manager may provide one or more graphical user interface widgets which when selected can identify changes to the field, soil, crops, tillage, or nutrient practices.
- the data manager may include a timeline view, a spreadsheet view, and/or one or more editable programs.
- FIG. 10 depicts an example embodiment of a timeline view for data entry.
- a user computer can input a selection of a particular field and a particular date for the addition of event.
- Events depicted at the top of the timeline include Nitrogen, Planting, Practices, and Soil.
- a user computer may provide input to select the nitrogen tab. The user computer may then select a location on the timeline for a particular field in order to indicate an application of nitrogen on the selected field.
- the data manager may display a data entry overlay, allowing the user computer to input data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information relating to the particular field. For example, if a user computer selects a portion of the timeline and indicates an application of nitrogen, then the data entry overlay may include fields for inputting an amount of nitrogen applied, a date of application, a type of fertilizer used, and any other information related to the application of nitrogen.
- the data manager provides an interface for creating one or more programs.
- Program in this context, refers to a set of data pertaining to nitrogen applications, planting procedures, soil application, tillage procedures, irrigation practices, or other information that may be related to one or more fields, and that can be stored in digital data storage for reuse as a set in other operations.
- a program After a program has been created, it may be conceptually applied to one or more fields and references to the program may be stored in digital storage in association with data identifying the fields.
- a user computer may create a program that indicates a particular application of nitrogen and then apply the program to multiple different fields. For example, in the timeline view of FIG.
- the top two timelines have the "Fall applied" program selected, which includes an application of 150 lbs N/ac in early April.
- the data manager may provide an interface for editing a program.
- each field that has selected the particular program is edited. For example, in FIG. 10, if the "Fall applied" program is edited to reduce the application of nitrogen to 130 lbs N/ac, the top two fields may be updated with a reduced application of nitrogen based on the edited program.
- the data manager in response to receiving edits to a field that has a program selected, removes the correspondence of the field to the selected program. For example, if a nitrogen application is added to the top field in FIG. 10, the interface may update to indicate that the "Fall applied" program is no longer being applied to the top field. While the nitrogen application in early April may remain, updates to the "Fall applied" program would not alter the April application of nitrogen.
- FIG. 11 depicts an example embodiment of a spreadsheet view for data entry.
- the data manager may include spreadsheets for inputting information with respect to Nitrogen, Planting, Practices, and Soil as depicted in FIG. 11.
- a user computer may select the particular entry in the spreadsheet and update the values.
- FIG. 11 depicts an in-progress update to a target yield value for the second field.
- a user computer may select one or more fields in order to apply one or more programs.
- the data manager may automatically complete the entries for the particular field based on the selected program.
- the data manager may update the entries for each field associated with a particular program in response to receiving an update to the program.
- the data manager may remove the correspondence of the selected program to the field in response to receiving an edit to one of the entries for the field.
- model and field data is stored in model and field data repository 160.
- Model data comprises data models created for one or more fields.
- a crop model may include a digitally constructed model of the development of a crop on the one or more fields.
- Model refers to an electronic digitally stored set of executable instructions and data values, associated with one another, which are capable of receiving and responding to a programmatic or other digital call, invocation, or request for resolution based upon specified input values, to yield one or more stored output values that can serve as the basis of computer-implemented recommendations, output data displays, or machine control, among other things.
- model data may include a model of past events on the one or more fields, a model of the current status of the one or more fields, and/or a model of predicted events on the one or more fields.
- Model and field data may be stored in data structures in memory, rows in a database table, in flat files or spreadsheets, or other forms of stored digital data.
- system 130 and/or external data server computer 108 may be implemented using two or more processors, cores, clusters, or instances of physical machines or virtual machines, configured in a discrete location or co-located with other elements in a datacenter, shared computing facility or cloud computing facility.
- the implementation of the functions described herein using one or more computer programs or other software elements that are loaded into and executed using one or more general-purpose computers will cause the general-purpose computers to be configured as a particular machine or as a computer that is specially adapted to perform the functions described herein.
- each of the flow diagrams that are described further herein may serve, alone or in combination with the descriptions of processes and functions in prose herein, as algorithms, plans or directions that may be used to program a computer or logic to implement the functions that are described.
- Field manager computing device 104 may communicate via a network using a mobile application stored on field manager computing device 104, and in some embodiments, the device may be coupled using a cable 113 or connector to the sensor 112 and/or controller 114.
- a particular user 102 may own, operate or possess and use, in connection with system 130, more than one field manager computing device 104 at a time.
- the mobile application may provide client-side functionality, via the network to one or more mobile computing devices.
- field manager computing device 104 may access the mobile application via a web browser or a local client application or app.
- Field manager computing device 104 may transmit data to, and receive data from, one or more front-end servers, using web-based protocols or formats such as HTTP, XML and/or JSON, or app-specific protocols.
- the data may take the form of requests and user information input, such as field data, into the mobile computing device.
- the mobile application interacts with location tracking hardware and software on field manager computing device 104 which determines the location of 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 methods of mobile positioning.
- location data or other data associated with the device 104, user 102, and/or user account(s) may be obtained by queries to an operating system of the device or by requesting an app on the device to obtain data from the operating system.
- 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 the one or more fields, tillage information for the one or more fields, crops planted in the one or more fields, and soil data extracted from the one or more fields.
- Field manager computing device 104 may send field data 106 in response to user input from user 102 specifying the data values for the one or more fields. Additionally, field manager computing device 104 may automatically send field data 106 when one or more of the data values becomes available to field manager computing device 104.
- field manager computing device 104 may be communicatively coupled to remote sensor 112 and/or application controller 114.
- field manager computing device 104 may send field data 106 to agricultural intelligence computer system 130 indicating that water was released on the one or more fields.
- Field data 106 identified in this disclosure may be input and communicated using electronic digital data that is communicated between computing devices using parameterized URLs over HTTP, or another suitable communication or messaging protocol.
- a commercial example of the mobile application is CLIMATE FIELD VIEW, commercially available from The climate Corporation, San Francisco, California.
- the CLIMATE FIELD VIEW application, or other applications may be modified, extended, or adapted to include features, functions, and programming that have not been disclosed earlier than the filing date of this disclosure.
- the mobile application comprises an integrated software platform that allows a grower to make fact-based decisions for their operation because it combines historical data about the grower's fields with any other data that the grower wishes to compare. The combinations and comparisons may be performed in real time and are based upon scientific models that provide potential scenarios to permit the grower to make better, more informed decisions.
- FIG. 2 illustrates two views of an example logical organization of sets of instructions in main memory when an example mobile application is loaded for execution.
- each named element represents a region of one or more pages of RAM or other main memory, or one or more blocks of disk storage or other non-volatile storage, and the programmed instructions within those regions.
- a mobile computer application 200 comprises account-fields-data ingestion-sharing instructions 202, overview and alert instructions 204, digital map book instructions 206, seeds and planting instructions 208, nitrogen instructions 210, weather instructions 212, field health instructions 214, and performance instructions 216.
- a mobile computer application 200 comprises account-fields- data ingestion-sharing instructions 202 which are programmed to receive, translate, and ingest field data from third party systems via manual upload or APIs.
- Data types may include field boundaries, yield maps, as-planted maps, soil test results, as-applied maps, and/or management zones, among others.
- Data formats may include shape files, native data formats of third parties, and/or farm management information system (FMIS) exports, among others.
- Receiving data may occur via manual upload, e-mail with attachment, external APIs that push data to the mobile application, or instructions that call APIs of external systems to pull data into the mobile application.
- mobile computer application 200 comprises a data inbox. In response to receiving a selection of the data inbox, the mobile computer application 200 may display a graphical user interface for manually uploading data files and importing uploaded files to a data manager.
- digital map book instructions 206 comprise field map data layers stored in device memory and are programmed with data visualization tools and geospatial field notes. This provides growers with convenient information close at hand for reference, logging and visual insights into field performance.
- overview and alert instructions 204 are programmed to provide an operation-wide view of what is important to the grower, and timely recommendations to take action or focus on particular issues. This permits the grower to focus time on what needs attention, to save time and preserve yield throughout the season.
- seeds and planting instructions 208 are programmed to provide tools for seed selection, hybrid placement, and script creation, including variable rate (VR) script creation, based upon scientific models and empirical data. This enables growers to maximize yield or return on investment through optimized seed purchase, placement and population.
- VR variable rate
- script generation instructions 205 are programmed to provide an interface for generating scripts, including variable rate (VR) fertility scripts.
- the interface enables growers to create scripts for field implements, such as nutrient applications, planting, and irrigation.
- a planting script interface may comprise tools for identifying a type of seed for planting.
- mobile computer application 200 may display one or more fields broken into soil zones along with a panel identifying each soil zone and a soil name, texture, and drainage for each zone.
- Mobile computer application 200 may also display tools for editing or creating such, such as graphical tools for drawing soil zones over a map of one or more fields.
- Planting procedures may be applied to all soil zones or different planting procedures may be applied to different subsets of soil zones.
- mobile computer application 200 may make the script available for download in a format readable by an application controller, such as an archived or compressed format. Additionally and/or alternatively, a script may be sent directly to cab computer 115 from mobile computer application 200 and/or uploaded to one or more data servers and stored for further use.
- nitrogen instructions 210 are programmed to provide tools to inform nitrogen decisions by visualizing the availability of nitrogen to crops. This enables growers to maximize yield or return on investment through optimized nitrogen application during the season.
- Example programmed functions include displaying images such as SSURGO images to enable drawing of application zones and/or images generated from subfield soil data, such as data obtained from sensors, at a high spatial resolution (as fine as 10 meters or smaller because of their proximity to the soil); upload of existing grower-defined zones; providing an application graph and/or a map to enable tuning application(s) of nitrogen across multiple zones; output of scripts to drive machinery; tools for mass data entry and adjustment; and/or maps for data visualization, among others.
- images such as SSURGO images to enable drawing of application zones and/or images generated from subfield soil data, such as data obtained from sensors, at a high spatial resolution (as fine as 10 meters or smaller because of their proximity to the soil); upload of existing grower-defined zones; providing an application graph and/or a map to enable tuning application(s) of nitrogen across multiple zones; output of scripts to drive machinery; tools for mass data entry and adjustment; and/or maps for data visualization, among others.
- Mass data entry in this context, may mean entering data once and then applying the same data to multiple fields that have been defined in the system; example data may include nitrogen application data that is the same for many fields of the same grower, but such mass data entry applies to the entry of any type of field data into the mobile computer application 200.
- nitrogen instructions 210 may be programmed to accept definitions of nitrogen planting and practices programs and to accept user input specifying to apply those programs across multiple fields.
- “Nitrogen planting programs,” in this context, refers to a stored, named set of data that associates: a name, color code or other identifier, one or more dates of application, types of material or product for each of the dates and amounts, method of application or incorporation such as injected or knifed in, and/or amounts or rates of application for each of the dates, crop or hybrid that is the subject of the application, among others.
- “Nitrogen practices programs,” in this context, refers to a stored, named set of data that associates: a practices name; a previous crop; a tillage system; a date of primarily tillage; one or more previous tillage systems that were used; one or more indicators of application type, such as manure, that were used.
- Nitrogen instructions 210 also may be programmed to generate and cause displaying a nitrogen graph, which indicates projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted; in some embodiments, different color indicators may signal a magnitude of surplus or magnitude of shortfall.
- a nitrogen graph comprises a graphical display in a computer display device comprising a plurality of rows, each row associated with and identifying a field; data specifying what crop is planted in the field, the field size, the field location, and a graphic representation of the field perimeter; in each row, a timeline by month with graphic indicators specifying each nitrogen application and amount at points correlated to month names; and numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude.
- the nitrogen graph may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen graph. The user may then use his optimized nitrogen graph and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts.
- Nitrogen instructions 210 also may be programmed to generate and cause displaying a nitrogen map, which indicates projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted; in some embodiments, different color indicators may signal a magnitude of surplus or magnitude of shortfall.
- the nitrogen map may display projections of plant use of the specified nitrogen and whether a surplus or shortfall is predicted for different times in the past and the future (such as daily, weekly, monthly or yearly) using numeric and/or colored indicators of surplus or shortfall, in which color indicates magnitude.
- the nitrogen map may include one or more user input features, such as dials or slider bars, to dynamically change the nitrogen planting and practices programs so that a user may optimize his nitrogen map, such as to obtain a preferred amount of surplus to shortfall. The user may then use his optimized nitrogen map and the related nitrogen planting and practices programs to implement one or more scripts, including variable rate (VR) fertility scripts.
- similar instructions to the nitrogen instructions 210 could be used for application of other nutrients (such as phosphorus and potassium) application of pesticide, and irrigation programs.
- weather instructions 212 are programmed to provide field- specific recent weather data and forecasted weather information. This enables growers to save time and have an efficient integrated display with respect to daily operational decisions.
- field health instructions 214 are programmed to provide timely remote sensing images highlighting in-season crop variation and potential concerns.
- Example programmed functions include cloud checking, to identify possible clouds or cloud shadows; determining nitrogen indices based on field images; graphical visualization of scouting layers, including, for example, those related to field health, and viewing and/or sharing of scouting notes; and/or downloading satellite images from multiple sources and prioritizing the images for the grower, among others.
- performance instructions 216 are programmed to provide reports, analysis, and insight tools using on-farm data for evaluation, insights and decisions. This enables the grower to seek improved outcomes for the next year through fact-based conclusions about why return on investment was at prior levels, and insight into yield- limiting factors.
- the performance instructions 216 may be programmed to communicate via the network(s) 109 to back-end analytics programs executed at agricultural intelligence computer system 130 and/or external data server computer 108 and configured to analyze metrics such as yield, hybrid, population, SSURGO, soil tests, or elevation, among others.
- Programmed reports and analysis may include yield variability analysis, benchmarking of yield and other metrics against other growers based on anonymized data collected from many growers, or data for seeds and planting, among others.
- Applications having instructions configured in this way may be implemented for different computing device platforms while retaining the same general user interface appearance.
- the mobile application may be programmed for execution on tablets, smartphones, or server computers that are accessed using browsers at client computers. Further, the mobile application as configured for tablet computers or
- the maps-cab instructions 222 may be programmed to provide map views of fields, farms or regions that are useful in directing machine operation.
- the remote view instructions 224 may be programmed to turn on, manage, and provide views of machine activity in real-time or near real-time to other computing devices connected to the system 130 via wireless networks, wired connectors or adapters, and the like.
- the data collect and transfer instructions 226 may be programmed to turn on, manage, and provide transfer of data collected at machine sensors and controllers to the system 130 via wireless networks, wired connectors or adapters, and the like.
- the machine alerts instructions 228 may be programmed to detect issues with operations of the machine or tools that are associated with the cab and generate operator alerts.
- the script transfer instructions 230 may be configured to transfer in scripts of instructions that are configured to direct machine operations or the collection of data.
- the scouting-cab instructions 230 may be programmed to display location-based alerts and information received from the system 130 based on the location of the agricultural apparatus 111 or sensors 112 in the field and ingest, manage, and provide transfer of location-based scouting observations to the system 130 based on the location of the agricultural apparatus 111 or sensors 112 in the field.
- external data server computer 108 stores external data 110, including soil data representing soil composition for the one or more fields and weather data representing temperature and precipitation on the one or more fields.
- the weather data may include past and present weather data as well as forecasts for future weather data.
- external data server computer 108 comprises a plurality of servers hosted by different entities. For example, a first server may contain soil composition data while a second server may include weather data. Additionally, soil composition data may be stored in multiple servers. For example, one server may store data representing percentage of sand, silt, and clay in the soil while a second server may store data representing percentage of organic matter (OM) in the soil.
- OM organic matter
- remote sensor 112 comprises one or more sensors that are programmed or configured to produce one or more observations.
- Remote sensor 112 may be aerial sensors, such as satellites, vehicle sensors, planting equipment sensors, tillage sensors, fertilizer or insecticide application sensors, harvester sensors, and any other implement capable of receiving data from the one or more fields.
- application controller 114 is programmed or configured to receive instructions from agricultural intelligence computer system 130.
- Application controller 114 may also be programmed or configured to control an operating parameter of an agricultural vehicle or implement.
- an application controller may be programmed or configured to control an operating parameter of a vehicle, such as a tractor, planting equipment, tillage equipment, fertilizer or insecticide equipment, harvester equipment, or other farm implements such as a water valve.
- Other embodiments may use any combination of sensors and controllers, of which the following are merely selected examples.
- the system 130 may obtain or ingest data under user 102 control, on a mass basis from a large number of growers who have contributed data to a shared database system. This form of obtaining data may be termed "manual data ingest" as one or more user-controlled computer operations are requested or triggered to obtain data for use by the system 130.
- the CLEVIATE FIELD VIEW application commercially available from The climate Corporation, San Francisco, California, may be operated to export data to system 130 for storing in the repository 160.
- seed monitor systems can both control planter apparatus components and obtain planting data, including signals from seed sensors via a signal harness that comprises a CAN backbone and point-to-point connections for registration and/or
- Seed monitor systems can be programmed or configured to display seed spacing, population and other information to the user via the cab computer 115 or other devices within the system 130. Examples are disclosed in US Pat. No. 8,738,243 and US Pat. Pub. 20150094916, and the present disclosure assumes knowledge of those other patent disclosures.
- yield monitor systems may contain yield sensors for harvester apparatus that send yield measurement data to the cab computer 115 or other devices within the system 130.
- Yield monitor systems may utilize one or more remote sensors 112 to obtain grain moisture measurements in a combine or other harvester and transmit these measurements to the user via the cab computer 115 or other devices within the system 130.
- sensors 112 that may be used with any moving vehicle or apparatus of the type described elsewhere herein include kinematic sensors and position sensors.
- Kinematic sensors may comprise any of speed sensors such as radar or wheel speed sensors, accelerometers, or gyros.
- Position sensors may comprise GPS receivers or transceivers, or WiFi-based position or mapping apps that are programmed to determine location based upon nearby WiFi hotspots, among others.
- examples of sensors 112 that may be used with tractors or other moving vehicles include engine speed sensors, fuel consumption sensors, area counters or distance counters that interact with GPS or radar signals, PTO (power take-off) speed sensors, tractor hydraulics sensors configured to detect hydraulics parameters such as pressure or flow, and/or and hydraulic pump speed, wheel speed sensors or wheel slippage sensors.
- examples of controllers 114 that may be used with tractors include hydraulic directional controllers, pressure controllers, and/or flow controllers;
- hydraulic pump speed controllers ; speed controllers or governors; hitch position controllers; or wheel position controllers provide automatic steering.
- examples of sensors 112 that may be used with seed planting equipment such as planters, drills, or air seeders include seed sensors, which may be optical, electromagnetic, or impact sensors; downforce sensors such as load pins, load cells, pressure sensors; soil property sensors such as reflectivity sensors, moisture sensors, electrical conductivity sensors, optical residue sensors, or temperature sensors; component operating criteria sensors such as planting depth sensors, downforce cylinder pressure sensors, seed disc speed sensors, seed drive motor encoders, seed conveyor system speed sensors, or vacuum level sensors; or pesticide application sensors such as optical or other electromagnetic sensors, or impact sensors.
- seed sensors which may be optical, electromagnetic, or impact sensors
- downforce sensors such as load pins, load cells, pressure sensors
- soil property sensors such as reflectivity sensors, moisture sensors, electrical conductivity sensors, optical residue sensors, or temperature sensors
- component operating criteria sensors such as planting depth sensors, downforce cylinder pressure sensors, seed disc speed sensors, seed drive motor encoders, seed conveyor system speed sensors, or vacuum level sensors
- pesticide application sensors such as optical or other electromagnetic sensors, or impact sensors.
- examples of sensors 112 that may be used with tillage equipment include position sensors for tools such as shanks or discs; tool position sensors for such tools that are configured to detect depth, gang angle, or lateral spacing; downforce sensors; or draft force sensors.
- examples of controllers 114 that may be used with tillage equipment include downforce controllers or tool position controllers, such as controllers configured to control tool depth, gang angle, or lateral spacing.
- sensors 112 that may be used in relation to apparatus for applying fertilizer, insecticide, fungicide and the like, such as on-planter starter fertilizer systems, subsoil fertilizer applicators, or fertilizer sprayers, include: fluid system criteria sensors, such as flow sensors or pressure sensors; sensors indicating which spray head valves or fluid line valves are open; sensors associated with tanks, such as fill level sensors; sectional or system-wide supply line sensors, or row-specific supply line sensors; or kinematic sensors such as accelerometers disposed on sprayer booms.
- fluid system criteria sensors such as flow sensors or pressure sensors
- sensors associated with tanks such as fill level sensors
- sectional or system-wide supply line sensors, or row-specific supply line sensors or kinematic sensors such as accelerometers disposed on sprayer booms.
- controllers 114 that may be used with such apparatus include pump speed controllers; valve controllers that are programmed to control pressure, flow, direction, PWM and the like; or position actuators, such as for boom height, subsoiler depth, or boom position.
- controllers 114 that may be used with harvesters include header operating criteria controllers for elements such as header height, header type, deck plate gap, feeder speed, or reel speed; separator operating criteria controllers for features such as concave clearance, rotor speed, shoe clearance, or chaffer clearance; or controllers for auger position, operation, or speed.
- examples of sensors 112 that may be used with grain carts include weight sensors, or sensors for auger position, operation, or speed.
- examples of controllers 114 that may be used with grain carts include controllers for auger position, operation, or speed.
- examples of sensors 112 and controllers 114 may be installed in unmanned aerial vehicle (UAV) apparatus or "drones.”
- UAV unmanned aerial vehicle
- sensors may include cameras with detectors effective for any range of the electromagnetic spectrum including visible light, infrared, ultraviolet, near-infrared (NIR), and the like; accelerometers; altimeters; temperature sensors; humidity sensors; pitot tube sensors or other airspeed or wind velocity sensors; battery life sensors; or radar emitters and reflected radar energy detection apparatus.
- Such controllers may include guidance or motor control apparatus, control surface controllers, camera controllers, or controllers programmed to turn on, operate, obtain data from, manage and configure any of the foregoing sensors. Examples are disclosed in US Pat. App. No. 14/831,165 and the present disclosure assumes knowledge of that other patent disclosure.
- sensors 112 and controllers 114 may be affixed to soil sampling and measurement apparatus that is configured or programmed to sample soil and perform soil chemistry tests, soil moisture tests, and other tests pertaining to soil.
- soil sampling and measurement apparatus that is configured or programmed to sample soil and perform soil chemistry tests, soil moisture tests, and other tests pertaining to soil.
- the apparatus disclosed in US Pat. No. 8,767, 194 and US Pat. No. 8,712,148 may be used, and the present disclosure assumes knowledge of those patent disclosures.
- the agricultural intelligence computer system 130 is programmed or configured to create an agronomic model.
- an agronomic model is a data structure in memory of the agricultural intelligence computer system 130 that comprises field data 106, such as identification data and harvest data for one or more fields.
- the agronomic model may also comprise calculated agronomic properties which describe either conditions which may affect the growth of one or more crops on a field, or properties of the one or more crops, or both.
- an agronomic model may comprise recommendations based on agronomic factors such as crop recommendations, irrigation recommendations, planting recommendations, and harvesting recommendations.
- the agronomic factors may also be used to estimate one or more crop related results, such as agronomic yield.
- the agronomic yield of a crop is an estimate of quantity of the crop that is produced, or in some examples the revenue or profit obtained from the produced crop.
- the agricultural intelligence computer system 130 may use a preconfigured agronomic model to calculate agronomic properties related to currently received location and crop information for one or more fields.
- the preconfigured agronomic model is based upon previously processed field data, including but not limited to,
- the preconfigured agronomic model may have been cross validated to ensure accuracy of the model.
- Cross validation may include comparison to ground truthing that compares predicted results with actual results on a field, such as a comparison of precipitation estimate with a rain gauge at the same location or an estimate of nitrogen content with a soil sample measurement.
- the agricultural intelligence computer system 130 is configured or programmed to perform data subset selection using the preprocessed field data in order to identify datasets useful for initial agronomic model generation.
- the agricultural intelligence computer system 130 may implement data subset selection techniques including, but not limited to, a genetic algorithm method, an all subset models method, a sequential search method, a stepwise regression method, a particle swarm optimization method, and an ant colony optimization method.
- a genetic algorithm selection technique uses an adaptive heuristic search algorithm, based on evolutionary principles of natural selection and genetics, to determine and evaluate datasets within the preprocessed agronomic data.
- the agricultural intelligence computer system 130 is configured or programmed to store the preconfigured agronomic data models for future field data evaluation.
- the agricultural intelligence computer system 130 includes a harvest time estimation subsystem 170.
- the harvest time estimation subsystem 170 is configured to provide a harvest time recommendation for harvesting planted crop using agricultural data values from one or more sources.
- the harvest time estimation subsystem 170 uses field data 106 and external data 110 to create digital models of grain moisture dry down rates for specific hybrid seeds of corn.
- the grain dry down logic 174 is generally configured or programmed to construct a grain dry down time series based upon the EMC time series, the grain moisture content of a specific hybrid seed at R6, relative maturity of a specific hybrid seed, and calculated drying coefficients based upon historical data of hybrid seed varieties.
- the harvest recommendation logic 175 is generally configured or programmed to evaluate the grain dry down time series and calculate the optimal harvest date.
- grain dry down logic 174, and harvest recommendation logic 175 may be implemented using one or more computer programs or other software elements that are loaded into and executed using one or more general-purpose computers, logic implemented in field programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). While FIG. 1 depicts harvest time application logic 171, grain dry down logic 174, EMC logic 172, grain moisture logic 173, and harvest recommendation logic 175 in one computing system, in various embodiments, logics 171, 172, 173, 174, and 175 may operate on multiple computing systems.
- FPGAs field programmable gate arrays
- ASICs application-specific integrated circuits
- external data server computer 108 stores external data 110, including historical grain moisture for a variety of hybrid seeds and weather data representing daily temperatures and humidity on one or more fields. Historical grain moisture may include, but is not limited to, estimated R6 dates for hybrid seed varieties, relative maturity for hybrid seed varieties, observed grain moisture at harvest, and geo-location specific data for each hybrid seed variety recorded.
- the weather data may include past and present daily temperatures including highs, lows, and dew point temperatures.
- external data server 108 comprises a plurality of servers hosted by different entities.
- a first server may contain hybrid seed property data while a second server may include weather data.
- FIG. 4 is a block diagram that illustrates a computer system 400 upon which an embodiment of the invention may be implemented.
- Computer system 400 includes a bus 402 or other communication mechanism for communicating information, and a hardware processor 404 coupled with bus 402 for processing information.
- Hardware processor 404 may be, for example, a general purpose microprocessor.
- Computer system 400 also includes a main memory 406, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 402 for storing information and instructions to be executed by processor 404.
- Main memory 406 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404.
- Such instructions when stored in non-transitory storage media accessible to processor 404, render computer system 400 into a special-purpose machine that is customized to perform the operations specified in the instructions.
- Computer system 400 further includes a read only memory (ROM) 408 or other static storage device coupled to bus 402 for storing static information and instructions for processor 404.
- a storage device 410 such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to bus 402 for storing information and instructions.
- Computer system 400 may be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT), for displaying information to a computer user.
- a display 412 such as a cathode ray tube (CRT)
- An input device 414 is coupled to bus 402 for communicating information and command selections to processor 404.
- cursor control 416 is Another type of user input device
- cursor control 416 such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 404 and for controlling cursor movement on display 412.
- This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.
- Computer system 400 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 400 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 400 in response to processor 404 executing one or more sequences of one or more instructions contained in main memory 406. Such instructions may be read into main memory 406 from another storage medium, such as storage device 410. Execution of the sequences of instructions contained in main memory 406 causes processor 404 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
- Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid- state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.
- Storage media is distinct from but may be used in conjunction with transmission media.
- Transmission media participates in transferring information between storage media.
- transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 402.
- transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.
- Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 404 for execution.
- the instructions may initially be carried on a magnetic disk or solid-state drive of a remote computer.
- the remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem.
- a modem local to computer system 400 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal.
- An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 402.
- Bus 402 carries the data to main memory 406, from which processor 404 retrieves and executes the instructions.
- the instructions received by main memory 406 may optionally be stored on storage device 410 either before or after execution by processor 404.
- Computer system 400 also includes a communication interface 418 coupled to bus 402.
- Communication interface 418 provides a two-way data communication coupling to a network link 420 that is connected to a local network 422.
- communication interface 418 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line.
- ISDN integrated services digital network
- communication interface 418 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN.
- LAN local area network
- Wireless links may also be implemented.
- communication interface 418 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
- Network link 420 typically provides data communication through one or more networks to other data devices.
- network link 420 may provide a connection through 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 data communication services through the world wide packet data communication network now commonly referred to as the "Internet" 428.
- Internet 428 uses electrical, electromagnetic or optical signals that carry digital data streams.
- the signals through the various networks and the signals on network link 420 and through communication interface 418, which carry the digital data to and from computer system 400, are example forms of transmission media.
- Computer system 400 can send messages and receive data, including program code, through the network(s), network link 420 and communication interface 418.
- a server 430 might transmit a requested code for an application program through Internet 428, ISP 426, local network 422 and communication interface 418.
- the received code may be executed by processor 404 as it is received, and/or stored in storage device 410, or other non-volatile storage for later execution.
- crop seed data and weather data related to a field are received by the agricultural intelligence computer system 130.
- the agricultural intelligence computer system 130 may receive field data 106 from the field manager computing device 104 and external data 110 from the external data server 108.
- Field data 106 may include, but is not limited to, crop seed data that identifies which specific hybrid has been planted by the user 102 and geo-location information related to the user's 102 field.
- the harvest time application logic 171 determines whether external data 110 is needed from the external data server 108. For example, specific external data 110 related to the weather conditions and properties of the specific hybrid seed type are required to accurately predict optimal harvest times. In embodiment, the harvest time application logic 171 may first query the model and field data repository 160, to determine if and to what extent external data 110 is required. For example, if the model and field data repository 160 has previously stored weather and seed property data for the specific hybrid seed, then the harvest time application logic 171 may not need any new external data 110. In another example, the harvest time application logic 171 may query the model and field data repository 160 and determine that only external data 110 related to the previous week's weather conditions is required from the external data server 108.
- the harvest time application logic 171 creates a query for an external data server 108.
- the harvest time application logic 171 requests, using the created query, external data 110 from the external data server 108.
- the harvest time application logic 171 stores the received field data 106 and external data 110 in the model and field data repository 160.
- the harvest time application logic 171 determines a query for external data 110 is not required, then the received field data 106 is stored in the model and field data repository 160 (block 610).
- the EMC logic 172 creates an EMC time series using external data 110 stored in the model and field data repository 160.
- the EMC logic 172 calculates dry-basis EMC values for each day based on available daily weather data points from the stored external data 1 10.
- a dry -basis EMC value represents a percentage of the moisture content of a given sample divided by the dry mass of the given sample.
- the EMC logic 172 uses the external data 1 10 including:
- the EMC logic 172 determines the EMC value at a specific time using the Chung-Pfost equation:
- M eq (t) E - F * ⁇ n[-(T avg (t) + C) * ln tfH(t)]
- M eq (t) equals the average daily dry -basis EMC fraction at time t
- T avg (t) equals the average daily temperature at time t, in Celsius
- RH(t) equals the average daily relative humidity fraction at time t
- E, F, and C are Chung-Pfost equation constants specific to corn.
- relative humidity, RH(t) may be calculated using the following equation:
- T dew (t), T max (t), T min (t) equal the dew point, maximum, and minimum temperatures at time t, in Celsius.
- P V (T) equals the saturated vapor pressure, in kPa, for a given temperature ⁇ , where P V (T) is calculated for a given temperature using the following equation:
- relative humidity may be calculated using either Modified- Oswin equation, Strohman-Yoerger equation, Modified-Halsey equation, Chen-Clayton equation, or Modified Henderson equation.
- the EMC logic 172 calculates the dry -basis EMC value for each day in the time series starting at an estimated R6 date and ending at the last date for which weather data is provided.
- the estimated R6 date for the specific to the hybrid seed is included in the external data 1 10 stored in the model and field data repository 160.
- the EMC logic 172 sends the EMC time series to the harvest time application logic 171 for storage in the model and field data repository 160.
- the grain moisture logic 173 calculates the grain moisture content at R6 based upon the relative maturity of the specific hybrid seed, a starting moisture coefficient, an adjustment coefficient, and the average relative maturity of corn seeds.
- the purpose calculating a specific hybrid seed moisture content at R6 is that it provides a grain moisture starting point for determining the grain moisture dry down rate and optimal harvest time.
- the grain moisture logic 173 determines the start date of R6 based upon external data 110, where the external data 110 includes an estimated R6 date for the specific hybrid seed.
- the estimated R6 date may be based upon a phenology database of observed lifecycles of hybrid corn varieties.
- R6 start date may be retrieved from an internal phenology database maintained in the model and field data repository 160.
- the grain moisture logic 173 is programmed to use the following parameters to determine specific hybrid seed grain moisture at R6:
- the grain moisture logic 173 uses the mean value of the posterior distribution of grain moisture at R6 as the starting grain moisture at R6. In an alternative embodiment, the grain moisture logic 173 may use the median value of the posterior distribution of grain moisture at R6 as the starting grain moisture at R6. In yet another embodiment, the grain moisture logic 173 uses the entire posterior distribution dataset of grain moisture at R6 to create a set of R6 grain moisture values to be evaluated.
- the grain moisture logic 173 creates a posterior distribution of an R6 adjustment factor, where the R6 adjustment factor is a calculated value for how much the relative maturity of each hybrid seed variety needs to be adjusted based upon the observed grain moisture at harvest.
- the grain moisture logic 173 may calculate the observed relative maturity of each hybrid seed sample and then determine how much the estimated relative maturity would need to be adjusted in order to align with the observed value.
- the grain moisture logic 173 may use Markov chain Monte Carlo methods for sampling the observed adjustment factor into a posterior distribution for a relative maturity adjustment coefficient.
- the grain moisture logic 173 uses the median value of the posterior distribution for the relative maturity adjustment coefficient as the relative maturity adjustment coefficient.
- the grain moisture logic 173 may use the mean value of the posterior distribution for the relative maturity adjustment coefficient as the relative maturity adjustment coefficient.
- the grain moisture logic 173 sets a baseline relative maturity as the average relative maturity for all observed corn seed varieties.
- the baseline relative maturity value is used for determining how much to adjust the moisture of a given hybrid seed based upon the difference of the given hybrid seed's relative maturity to the baseline relative maturity.
- the baseline relative maturity may be set as the average relative maturity of all hybrid seed varieties.
- the grain moisture logic 173 calculates the hybrid seed grain moisture at R6 as a function of the hybrid seed's relative maturity versus the baseline relative maturity of all observed hybrid seed varieties. In an embodiment, the grain moisture logic 173 uses the following equation to determine hybrid seed grain moisture at R6:
- the grain moisture logic 173 sends the calculated the grain moisture content at R6 of the hybrid seed to the harvest time application logic 171 to be stored in the model and field data repository 160.
- the grain dry down logic 174 creates a grain dry down time series for the specific hybrid seed.
- the grain dry down time series is a set of dry down rates and hybrid seed moisture content corresponding to a specific day during the dry down process.
- Grain dry down refers to the exchange of moisture from the hybrid seed kernel to the outside air.
- the grain dry down logic 174 calculates the rate of daily grain dry down as the difference between the kernel moisture and the moisture of ambient air at that specific date, multiplied by a drying coefficient.
- the grain dry down logic 174 calculates the grain dry down rate starting from the R6 date of the specific hybrid seed.
- the daily dry down rate is calculated using the following equation:
- EMC(t) equals the equilibrium moisture content of the ambient air at time t.
- FIG. 8 depicts an example of calculating the daily dry down rate starting at R6.
- the grain dry down logic 174 calculates the daily dry down rate at time t using the daily dry down rate equation described above. For example, if the parameters are:
- the grain dry down logic 174 adds the calculated dry down rate at time t (block 804) and the hybrid seed moisture content M(t) to the grain dry down time series.
- the grain dry down logic 174 determines whether there are more data points available to calculated additional daily dry down rates.
- the grain dry down logic 174 calculates daily dry down rates for each data point within the EMC time series because the EMC time series represents each measured day up until the latest measured day. By calculating the latest data, the grain dry down time series will reflect the most accurate information for calculating current moisture levels.
- the grain dry down logic 174 determines that there are more data points then the grain dry down logic 174 proceeds to block 802, where time t equals "t+1".
- the harvest recommendation logic 175 graphs the hybrid seed moisture content values in the grain dry down time series and extrapolates future moisture content values based upon a trend line.
- extrapolation of future moisture content may be based upon forecasted weather data that is used to calculate EMC values and the changing rate of grain dry down, where the rate of grain dry down steadily decreases as the moisture content value nears the EMC value.
- the harvest recommendation logic 175 may extrapolate moisture content values based upon historical EMC data values for the time of year and geo-location and EMC values based on forecasted weather data.
- the harvest recommendation logic 175 returns a
- the recommendation data model includes, but is not limited to, an extrapolated graph of the moisture content values of the hybrid seed, including predicted values, and a recommended harvest date that is based upon the desired moisture content value of the hybrid seed. By providing both the recommendation date and the moisture content dry down graph, the user 102 may better understand the grain dry down trend of his crop.
- FIG. 9 depicts a sample recommendation data model, where graph 902 depicts a the recommendation data model for field X.
- Line 904 is the extrapolated trend line based upon calculated moisture content values from the grain dry down time series.
- Point 906 is the predicted date (day R6+50) where the moisture content of the hybrid seed reaches 15% wet- basis moisture.
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Abstract
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Priority Applications (6)
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AU2016344427A AU2016344427B2 (en) | 2015-10-28 | 2016-10-07 | Computer-implemented calculation of corn harvest recommendations |
BR112018008750-5A BR112018008750B1 (en) | 2015-10-28 | 2016-10-07 | COMPUTER-IMPLEMENTED METHOD OF CORN HARVEST RECOMMENDATIONS AND NON-TRANSITIONAL STORAGE MEDIA |
EP16860492.4A EP3367783A4 (en) | 2015-10-28 | 2016-10-07 | Computer-implemented calculation of corn harvest recommendations |
CA3002706A CA3002706A1 (en) | 2015-10-28 | 2016-10-07 | Computer-implemented calculation of corn harvest recommendations |
ZA2018/03007A ZA201803007B (en) | 2015-10-28 | 2018-05-08 | Computer-implemented calculation of corn harvest recommendations |
AU2021266286A AU2021266286A1 (en) | 2015-10-28 | 2021-11-11 | Computer-implemented calculation of corn harvest recommendations |
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US14/925,797 US10586158B2 (en) | 2015-10-28 | 2015-10-28 | Computer-implemented calculation of corn harvest recommendations |
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US20220277208A1 (en) | 2022-09-01 |
EP3367783A4 (en) | 2019-05-22 |
AU2016344427B2 (en) | 2021-08-12 |
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US20170124463A1 (en) | 2017-05-04 |
US20200279179A1 (en) | 2020-09-03 |
ZA201803007B (en) | 2019-07-31 |
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CA3002706A1 (en) | 2017-05-04 |
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AU2021266286A1 (en) | 2021-12-09 |
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AR106474A1 (en) | 2018-01-17 |
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