Systems and methods for use in identifying a set of candidate seeds for a target field based on a prediction model are provided. One example method includes accessing, by a computing device, data from a data server, the data including data representative of seeds harvested from at least one of a research growing space, a development growing space, and a field growing space; generating a yield delta prediction model, based on at least a portion of the accessed data; for each of a plurality of candidate seeds, automatically generating a probability of a yield delta for the candidate seed, relative to a target seed, exceeding a performance threshold, based on the generated model; identifying, by the computing device, a set of the candidate seeds, based on the probability of the respective candidate seed satisfying a defined threshold; and outputting, by the computing device, the identified set of seeds to a user.
Systems and methods are provided for use in identifying candidate seeds. An example computer-implemented method includes receiving, from a user, at a computer system, a request for a seed recommendation for a plurality of fields; identifying, by the computer system, multiple seeds and volumes for the multiple seeds to be planted in the plurality of fields, the multiple seeds included in a rank-ordered listing of ones of the multiple seeds for each of the plurality of fields; generating, by the computer system, a planting recommendation to plant the multiple seeds from the listing in the plurality of fields as a planting recommendation, based on an objective function, the objective function indicative of yield, multiple relative maturity thresholds, and one or more operational rules; and outputting, by the computer system, the planting recommendation of the seeds to the user associated with the fields, in response to the request.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
Systems and methods are provided for use in identifying candidate seeds. An example computer-implemented method includes receiving, from a user, at a computer system, a request for a seed recommendation for a plurality of fields; identifying, by the computer system, multiple seeds and volumes for the multiple seeds to be planted in the plurality of fields, the multiple seeds included in a rank-ordered listing of ones of the multiple seeds for each of the plurality of fields; generating, by the computer system, a planting recommendation to plant the multiple seeds from the listing in the plurality of fields as a planting recommendation, based on an objective function, the objective function indicative of yield, multiple relative maturity thresholds, and one or more operational rules; and outputting, by the computer system, the planting recommendation of the seeds to the user associated with the fields, in response to the request.
Systems and methods are provided for use in applying treatments to crops in fields. One example computer-implemented method includes determining a growth stage vector indicative of a growth stage of a crop in a field, using a GRU-based phenology model, based on a planting date of the crop and weather data for the field. The method also includes determining a disease risk for the crop in the field based on a disease risk model and the growth stage vector, determining a residual protection of the field for a prior treatment of the field, and determining whether application of the treatment is recommended for the field based on the disease risk and the determined residual protection. The method then includes, in response to determining that application of the treatment is recommended, identifying application intervals for the treatment based on the weather data for the application intervals.
Systems and methods are provided for use in applying treatments to crops in fields. One example computer-implemented method includes determining a growth stage vector indicative of a growth stage of a crop in a field, using a GRU-based phenology model, based on a planting date of the crop and weather data for the field. The method also includes determining a disease risk for the crop in the field based on a disease risk model and the growth stage vector, determining a residual protection of the field for a prior treatment of the field, and determining whether application of the treatment is recommended for the field based on the disease risk and the determined residual protection. The method then includes, in response to determining that application of the treatment is recommended, identifying application intervals for the treatment based on the weather data for the application intervals.
Methods and systems provided for generating a soil data map. One example method includes instructing, by a computing device, a user to draw a field boundary in a map of a geographic area and receiving an input, from the user, at the map of the geographic area, where the input includes multiple vertices of the field boundary. The method also includes appending, by the computing device, a shape of the field boundary to the map, based on the multiple vertices, and overlaying at least one management zone within the field boundary on the map. The at least one management zone is associated with specific soil data for the geographic area corresponding to the at least one management zone within the field boundary.
A system for implementing a trial a field is provided. In an embodiment, the system is configured to generate a trial recommendation for a field and, based on field data for the field, compute a yield probabilities for the field. The system is also configured to generate a plurality of outcome-based values for the field based on the yield probabilities, compute crop values for each of the outcome-based values and a bushel per acre value, and cause display of an interface that dynamically displays each of the plurality of outcome-based values for the field based on a selected bushel per acre value. The system is further configured to receive user input changing a position of an interactive sliding widget in the interface, to change the bushel per acre value, and in response, compute a crop value for each of the outcome-based values based on the changed bushel per acre value.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
8.
SYSTEMS AND METHODS FOR BIOCHAR-BASED SOIL CARBON SEQUESTRATION
Systems and methods are provided for carbon sequestration. Disclosed in one embodiment is a carbon sequestration system that includes a crop residue pickup configured to ingest crop residue disposed on a field; a pyrolyzer configured to receive the crop residue output from the crop residue pickup and process the crop residue into biochar; a biochar/water or slurry mixing system configured to receive the biochar output from the pyrolyzer and quench the biochar in water and/or slurry; a soil injection system configured to receive the quenched biochar from the biochar/water or slurry mixing system and inject the quenched biochar into the field; and one or more sensors configured to detect one or more operations states of the crop residue pickup, the pyrolyzer, the biochar/water or slurry mixing system, and/or the soil injection system.
A01C 23/02 - Special arrangements for delivering the liquid directly into the soil
C09K 17/02 - Soil-conditioning materials or soil-stabilising materials containing inorganic compounds only
C10B 53/02 - Destructive distillation, specially adapted for particular solid raw materials or solid raw materials in special form of cellulose-containing material
G01M 99/00 - Subject matter not provided for in other groups of this subclass
Systems and methods arc provided for carbon sequestration. Disclosed in one embodiment is a carbon sequestration system that includes a crop residue pickup configured to ingest crop residue disposed on a field; a pyrolyzer configured to receive the crop residue output from the crop residue pickup and process the crop residue into biochar; a biochar/water or slurry mixing system configured to receive the biochar output from the pyrolyzer and quench the biochar in water and/or slurry; a soil injection system configured to receive the quenched biochar from the biochar/water or slurry mixing system and inject the quenched biochar into the field; and one or more sensors configured to detect one or more operations states of the crop residue pickup, the pyrolyzer, the biochar/water or slurry mixing system, and/or the soil injection system.
C10B 53/02 - Destructive distillation, specially adapted for particular solid raw materials or solid raw materials in special form of cellulose-containing material
A method includes monitoring, by sensors, a drive path of a farming machine in an agricultural field, where the drive path includes multiple tracks each including a planting row, and detecting, based on data from the sensors, an end of drive path planting pattern, which includes multiple plants in at least a first one of the multiple tracks as a track extension, but no plants in at least a second one of the tracks. The method also includes, based on the detected end of drive path planting pattern, determining, by a computing device, location data of an edge of a previous drive path and generating alignment guidance data for the farming machine based on a current location of the farming machine and the location data of the edge of the previous drive path. The method further includes outputting, at the farming machine, the alignment guidance data.
A method for controlling application of agrichemical products, comprises acquiring remotely sensed digital image data; developing a prescription to apply at least one agrichemical product in a variable manner based on at least the digital image data, wherein the prescription describes a plurality of passes of a particular autonomous vehicle over a field to apply the at least one agrichemical product; applying the at least one agrichemical product to a crop in the variable manner by the particular autonomous vehicle according to the prescription.
Systems and methods for planting specified seed products in target growing spaces. An example method includes receiving a request for a planting recommendation related to seeding of a target growing space and, in response, determining, using one or more seed placement prediction models, a prediction output including a predicted yield for multiple seed products at the target growing space at each of one or more different weather conditions. The method also includes determining, using an optimization model, a seed planting recommendation output, based on at least the prediction output and at least one grower constraint parameter associated with the target growing space, where the seed planting recommendation output includes at least one of the multiple seed products, and then directing planting of the at least one of the multiple seed products at the target growing space based on the seed planting recommendation output.
Systems and methods for determining a degree or rate of error for treatments at a field are disclosed herein. In some embodiments, a system receives first data corresponding to a first agronomic treatment at a field and second data corresponding to a second agronomic treatment at the field. The system uses the first and second data to determine differences and overlaps between characteristics of the first and second data to quantify an error rate at individual subsets of the field. Based on the quantified error rate, a number of metrics and yield data may be interpreted differently or modified to fit the result of the quantified error rate. The system uses the quantified error rate to predict the effectiveness of treatments, modify the rate of experienced yields at a field, and recommend real-time and subsequent corrective actions to take at the field.
G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
Disclosed herein are systems and methods for translating and verifying text in a variety of different languages for the same software application. When the text in the application changes. embodiments of the disclosure may include translating and/or verifying only the text that has changed. The system may compare the new screenshot(s) with previously accepted screenshot(s) to locate the text that has changed. The text that has not changed (since the last accepted translation) may not be translated and/or verified once accepted by a translator. The system may highlight the text that has changed so that the translator may focus only on the relevant portions of the user interface and not have to search for the text that has changed. For rejected translations. the system may repeat the process. translating and/or verifying only the rejected text (instead of translating/verifying all text again).
Systems and methods for use in assessing a treatment trial in an agricultural field. One example method includes, for each of multiple treatment trials in agricultural fields, generating, by a computing device, a post-harvest trial block, based on multiple data layers associated with the agricultural field, where the post-harvest trial block defines an area of the agricultural field associated with the treatment trial; clustering, by a computing device, a training set of the post¬ harvest trial block based on one or more geospatial features of the areas of the post-harvest trial blocks; training, by the computing device, a model to indicate a label for a target post-harvest trial block based on the one or more geospatial features of an area of the target post-harvest trial block; and storing the label in a memory.
Systems and methods for use in assessing a treatment trial in an agricultural field. One example method includes, for each of multiple treatment trials in agricultural fields, generating, by a computing device, a post-harvest trial block, based on multiple data layers associated with the agricultural field, where the post-harvest trial block defines an area of the agricultural field associated with the treatment trial; clustering, by a computing device, a training set of the post-harvest trial block based on one or more geospatial features of the areas of the post-harvest trial blocks; training, by the computing device, a model to indicate a label for a target post-harvest trial block based on the one or more geospatial features of an area of the target post-harvest trial block; and storing the label in a memory.
Systems and methods are provided for directing crop disease treatments to agricultural fields. An example computer-implemented method includes receiving a user input from a user at a communication device associated with an interface indicative of likelihood of a first disease in a plurality of agricultural fields and, in response, determining a likelihood of occurrence of the first disease for ones of the plurality of agricultural fields. The method then also includes generating an interface indicative of the likelihood of occurrence of the first disease in the ones of the plurality of agricultural fields and causing the interface to be displayed at the communication device associated with the user.
A01G 7/06 - Treatment of growing trees or plants, e.g. for preventing decay of wood, for tingeing flowers or wood, for prolonging the life of plants
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
18.
SYSTEMS AND METHODS FOR RENDERING DISEASE DATA FOR AGRICULTURAL FIELDS THROUGH IMPROVED INTERFACES
Systems and methods are provided for directing crop disease treatments to agricultural fields. An example computer-implemented method includes receiving a user input from a user at a communication device associated with an interface indicative of likelihood of a first disease in a plurality of agricultural fields and, in response, determining a likelihood of occurrence of the first disease for ones of the plurality of agricultural fields. The method then also includes generating an interface indicative of the likelihood of occurrence of the first disease in the ones of the plurality of agricultural fields and causing the interface to be displayed at the communication device associated with the user.
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
A01N 25/00 - Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of applicationSubstances for reducing the noxious effect of the active ingredients to organisms other than pests
A computer-implemented method of managing data related to an agricultural process is disclosed. The computer-implemented method comprises causing display of a first interface including a first map of one or more agricultural fields and receiving a selection input, by a user, to the first map designating a boundary of a region of the one or more agricultural fields. The computer-implemented method then also includes, in response to the selection input, performing an analysis of one or more of a plurality of types of farming data specific to the region of the one or more agricultural fields having a plurality of components, and displaying a second interface, overlayed on the first interface of the first map of the one or more agricultural fields. The second interface indicates a first component of the plurality of components of the analysis specific to the region of the one or more agricultural fields.
A connector is provided for use with a communication apparatus to facilitate communication of data, via the communication apparatus, between a vehicle or agricultural implement and a computing device. The connector includes a first portion first portion configured to couple the connector to the vehicle or agricultural implement, and a second portion configured to couple the connector to the communication apparatus. The second portion of the connector includes a substrate having at least one circuit trace defined along a surface of the substrate and configured to conductively transfer electronic signals between the connector and the communication apparatus.
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
21.
SYSTEMS AND METHODS FOR IMAGE CAPTURE AND ANALYSIS OF AGRICULTURAL FIELDS
Described herein are systems and methods for capturing images of a field and performing agricultural data analysis of the images. In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural image data including images of at least one stage of crop development that are captured with at least one of an apparatus and a remote sensor moving through a field. The computer includes at least one processing unit that is coupled to the database. The at least one processing unit is configured to execute instructions to analyze the captured images, to determine relevant images that indicate a change in at least one condition of the crop development, and to generate a localized view map layer for viewing the field at the at least one stage of crop development based on at least the relevant captured images.
G06F 16/58 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 16/587 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
Systems and methods for improving the training of machine learning models to generate probability distributions of yield values are presented. In an embodiment, a system stores a machine learning system trained to compute parameters for a probability distribution of yield values based on seeding density, seed type, and information specific to a field. The system receives inputs for a particular field and computes parameters for a probability distribution of yield. The system generates a probability distribution of yield using the parameters and uses the probability distribution to generate a yield guarantee value. The system supplies the yield guarantee value to a field manager computing device with a seed type and/or seed density recommendation. When the system receives input accepting the recommendation, the system generates one or more scripts which, when executed by an application controller, causes the application controller to control an agricultural implement to cause the agricultural implement to plant a seed on the field according to the recommendation.
Systems and methods for predicting likelihoods of multiple crop disease types for target plots. An example computer-implemented method includes receiving a request for a crop disease prediction related to treatment of a target plot for one or more crop diseases and accessing a multiple disease joint model consistent with location data included in the request. The computer-implemented method also includes determining, via the multiple disease joint model, first and second disease likelihood output based on at least the location data, where the first and second disease likelihood outputs are each associated with a different one of the multiple disease types, and generating a treatment recommendation based on the first and second disease likelihood outputs. The computer-implemented method then includes directing application of at least one treatment to the target plot, based on the treatment recommendation output.
Systems and methods for predicting likelihoods of multiple crop disease types for target plots. An example computer-implemented method includes receiving a request for a crop disease prediction related to treatment of a target plot for one or more crop diseases and accessing a multiple disease joint model consistent with location data included in the request. The computer- implemented method also includes determining, via the multiple disease joint model, first and second disease likelihood output based on at least the location data, where the first and second disease likelihood outputs are each associated with a different one of the multiple disease types, and generating a treatment recommendation based on the first and second disease likelihood outputs. The computer-implemented method then includes directing application of at least one treatment to the target plot, based on the treatment recommendation output.
Described herein are methods and systems for generating shared collaborative maps for planting or harvesting operations. An example computer-implemented method includes generating a shared collaborative map for a first and/or second agricultural machine(s), based on data gathered from the first and second agricultural machines. The map indicates locations in a field where the first and second agricultural machines have already planted seed. The method also includes providing the map to a cab monitor of the first and/or second agricultural machine(s), generating a trigger based on the map for the first agricultural machine, and communicating the trigger to the first agricultural machine, based on a current location of the first agricultural machine relative to the map, to prevent the first agricultural machine from replanting regions that have been already planted by the first agricultural machine or the second agricultural machine.
A computer-implemented method for providing variable rate application of crop inputs to an agricultural field includes determining an index value of biomass health for multiple regions in the agricultural field and determining, based on the index value for each of the multiple regions, a number of unique soil types in the agricultural field. The computer-implemented method also includes determining a distinctness of pairs of the unique soil types in the agricultural field, determining a statistical variation based on the distinctness of the pairs, and calculating, based on the statistical variation, a variable rate suitability score for the agricultural field relating to spatially varying a rate of application of one or more crop inputs to the agricultural field. The computer-implemented method then includes displaying the agricultural field along with the variable rate suitability score for the agricultural field.
A01B 79/02 - Methods for working soil combined with other agricultural processing, e.g. fertilising, planting
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
27.
Highly responsive farming systems with extraordinary in-season optimization
A method for controlling application of agrichemical products, comprises acquiring remotely sensed digital image data; developing a prescription to apply at least one agrichemical product in a variable manner based on at least the digital image data, wherein the prescription describes a plurality of passes of a particular autonomous vehicle over a field to apply the at least one agrichemical product; applying the at least one agrichemical product to a crop in the variable manner by the particular autonomous vehicle according to the prescription.
Systems and methods are provided for use in mapping irrigation in fields based on remote data. One example computer-implemented method includes accessing, by a computing device, at least one image of one or more fields; applying, by the computing device, a trained model to identity at least one irrigation segment in the at least one image; compiling a map of the one or more fields including the at least one identified irrigation segment; and storing, by the computing device, the map of the at least one identified irrigation segment for the one or more fields in a memoiy; and/or causing display of the map of the at least one identified irrigation segment for the one or more fields at an output device.
Systems and methods are provided for defining field regions within agricultural fields. An example computer-implemented method includes identifying a field, retrieving data for the identified field, and displaying a graphical display including the identified field and at least some of the retrieved data. The method also includes receiving, via the graphical display, an input to create a field region within the identified field based on passes of an agricultural apparatus in the identified field and retrieving pass data for the passes of the agricultural apparatus in the field. The method further includes displaying, on the graphical display, the pass data, defining the field region based on the pass data, so that a boundary of the field region corresponds to a start point and an end point of each of the passes of the agricultural apparatus, and displaying, on the graphical display, field performance data constrained to the field region.
Systems and methods are provided for use in assessing disease threat in a field. An example computer-implemented method includes accessing weather data for a field where the field includes a crop and the weather data includes a weather condition for the field during a time period and identifying multiple intervals within the time period as threat intervals, based on the weather condition of the field during each of the multiple intervals being with a first range. The method also includes aggregating the multiple threat intervals into a damaging factor, based, in part, on ones of the multiple intervals being consecutive intervals during the time period, and comparing the damaging factor to a threat threshold. The method then includes, in response to the damaging factor satisfying the threat threshold, generating and transmitting, by the computing device, an output indicative of the damaging factor.
Systems and methods are provided for use in assessing disease threat in a field. An example computer-implemented method includes accessing weather data for a field where the field includes a crop and the weather data includes a weather condition for the field during a time period and identifying multiple intervals within the time period as threat intervals, based on the weather condition of the field during each of the multiple intervals being with a first range. The method also includes aggregating the multiple threat intervals into a damaging factor, based, in part, on ones of the multiple intervals being consecutive intervals during the time period, and comparing the damaging factor to a threat threshold. The method then includes, in response to the damaging factor satisfying the threat threshold, generating and transmitting, by the computing device, an output indicative of the damaging factor.
G06F 18/2132 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on discrimination criteria, e.g. discriminant analysis
G06F 18/2135 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
32.
Methods for generating soil maps and application prescriptions
Methods and systems provided for generating an irrigation map. In one computer-implemented method, a user is instructed, by a computing device, to draw an irrigation boundary in a map of an agricultural field. In response, the computing device receives a first input, from the user, at the map of the agricultural field displayed to the user, where the input includes a point of the irrigation boundary. A second input is solicited from the user and, in turn, the computing device receives the second input, which defines a dimension of the irrigation boundary. The computing device then appends a shape of the irrigation boundary to the map, based on the point and the dimension, wherein the irrigation boundary is positioned based on the point indicated by the user.
Systems and methods for identifying operational abnormalities based on data received from an agricultural implement performing a task in an agricultural field are described herein. In an embodiment, a system receives time-series data captured from an agricultural implement performing an agronomic activity on an agricultural field, the time-series data including, for each of a plurality of timestamps, a location of the agricultural implement. The system identifies a plurality of passes in the time-series data and using the identified plurality of passes, identifies a plurality of location on the agricultural field in which the activity performed by the agricultural implement included a particular operational abnormality. The system generates a map of operational abnormalities for the agricultural field, the map of operational abnormalities including the plurality of locations on the agricultural field in which the activity performed by the agricultural implement included the particular operational abnormality.
Systems and methods are provided for updating an agricultural prescription for a geographic region. An example computer-implemented method includes executing an agricultural prescription, via farming equipment, in a geographic region and receiving, during execution of the prescription, sensor data from the farming equipment and operating data representing local operating settings for the farming equipment. Task execution data for the agricultural prescription is generated based on the received sensor data and received operating data for the farming equipment, and compared to expected data for execution of the prescription by the farming equipment. Then, in response to the comparison exceeding a threshold, a corrective action for the farming equipment is initiated wherein an alert is generated and, in response, an updated agricultural prescription is executed, via the farming equipment, by overriding the local operating settings for the farming equipment with corrective operating settings included in the updated prescription.
A01D 41/127 - Control or measuring arrangements specially adapted for combines
G05B 15/02 - Systems controlled by a computer electric
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/063 - Operations research, analysis or management
G08G 1/00 - Traffic control systems for road vehicles
H04W 4/70 - Services for machine-to-machine communication [M2M] or machine type communication [MTC]
35.
METHODS AND SYSTEMS FOR USE IN MAPPING TILLAGE BASED ON REMOTE DATA
Systems and methods are provided for use in mapping tillage in fields based in remote data. One example computer-implemented method includes accessing, by a computing device, an image of one or more fields, where the image includes multiple pixels and where each of the pixels includes a value for each of multiple bands. The method also includes deriving, by the computing device, at least one index value the image and generating a map of tillage for the one or more fields using a trained model and the at least one index value for each of the pixels of the image. The method further includes storing the map of tillage for the one or more fields in a memory and causing display of the map of tillage for the one or more fields at an output device.
Systems and methods are provided for use in mapping irrigation in fields based on remote data. One example computer-implemented method includes accessing, by a computing device, at least one image of one or more fields; applying, by the computing device, a trained model to identity at least one irrigation segment in the at least one image; compiling a map of the one or more fields including the at least one identified irrigation segment; and storing, by the computing device, the map of the at least one identified irrigation segment for the one or more fields in a memory; and/or causing display of the map of the at least one identified irrigation segment for the one or more fields at an output device.
Systems and methods are provided for use in mapping tillage in fields based in remote data. One example computer-implemented method includes accessing, by a computing device, an image of one or more fields, where the image includes multiple pixels and where each of the pixels includes a value for each of multiple bands. The method also includes deriving, by the computing device, at least one index value the image and generating a map of tillage for the one or more fields using a trained model and the at least one index value for each of the pixels of the image. The method further includes storing the map of tillage for the one or more fields in a memory and causing display of the map of tillage for the one or more fields at an output device.
Systems and methods are provided for managing agricultural activities in a field region. In one example, a computer-implemented method includes identifying temperature grids for the field region, and identifying weather stations for the temperature grids. Each weather station is located at a weather station location in the temperature grids. The computer-implemented method also includes computing weight values based on the weather station locations of the weather stations, such that the weather stations that are more proximate to their respective grids have higher weights than weather stations that are less proximate to their respective grids, and receiving temperature readings from the weather stations. The computer-implemented method then includes computing a plurality of weighted temperatures based on the weight values, computing field condition data for the field region based on the weighted temperatures, and determining at least one field activity for the field region based on the field condition data.
G01W 1/02 - Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Techniques for recommending side-by-side plantings of pairs of seeds include a server computer receiving agricultural data records that represent crop seed data describing seed and yield properties of seeds and first data for agricultural fields where the seeds were planted. The server receives second data for available seeds and automatically calculates a dataset of success probability scores that describe the probability of a successful yield on the target fields. Data is organized as pairs to facilitate comparison of actual plantings to optimized plantings that have a probability of success (POS), in terms of yield lift or increased yield season-over-season, for different yield values. Confidence values are generated and stored in association with the POS values and can be used as a basis of visual output to support planting and/or field management decisions as part of an automated intelligent agricultural decision support system.
Autonomous vehicles with global positioning systems are used for field inspection. A vehicle may be programmed to traverse a field, while using sensors to detect objects in the field, and then capture low-resolution images of the objects. Machine vision techniques are used with the low-resolution images to recognize the objects as crops, non-crop plant material or undefined objects. Location data is used to correlate recognized objects with digitally stored field maps to resolve whether a particular object is in a location at which crop planting is expected or not expected. Depending on whether an object in a low-resolution digital image is recognized as a crop, and whether the object is in an expected geo-location for crops, the vehicle may switch to a second image capture mode, for example, capturing a high-resolution image of the object, and/or execute a disease analysis and/or weed analysis on the images of the objects.
H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
G05D 1/00 - Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
A server computer system receives a dataset of candidate hybrid seeds for planting on one or more target fields, which comprises data regarding one or more hybrid seeds, probability of success values associated with each of the one or more hybrid seeds that describe a probability of a successful yield, and historical agricultural data associated with each of the one or more hybrid seeds further receives one or more properties for one or more target fields, and selects a subset of the one or more hybrid seeds based on the probabilities of success values. The server computer system generates representative yield values for hybrid seeds in the subset based on the historical agricultural data, generates a dataset of risk values for the subset, which describes an amount of risk associated with the representative yield values for each hybrid seed in the subset based on the historical agricultural data associated with each hybrid seed in the subset, and selects a list of target hybrid seeds from the subset for planting on the one or more target fields based on the dataset of risk values, the representative yield values for the subset, and the one or more properties for the one or more target fields. In addition, the server computer system generates a prescription related to planting the list of target hybrid seeds in the one or more target fields for a next period determines a safety stock for the list for the next period with respect to a supply chain based on the prescription, and causes displaying values of the safety stock, thereby causing production of the safety stock.
A system for measuring soil properties has at least one sensor coupled to a row unit of an agricultural implement and a camera coupled to the row unit of the agricultural implement. The at least one sensor is configured to measure at least one soil property of soil worked by the row unit and/or of soil in a trench opened by an opening assembly of the row unit. And, the camera is arranged, on the row unit, to capture images of the soil worked by the row unit and/or the trench opened by the opening assembly of the row unit.
G01N 21/359 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
G06V 10/60 - Extraction of image or video features relating to illumination properties, e.g. using a reflectance or lighting model
G01N 21/3554 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for determining moisture content
Systems and methods are provided for identifying candidate seeds for a grower. An example computer-implemented method includes identifying multiple seeds for a grower suitable to be selected for planting in a growing space associated with the grower, and accessing data from a data server including seed data representative of each of the multiple seeds. The method also includes identifying candidate seeds from the multiple seeds for the grower, based on a model specific to the grower, wherein the model is trained on historical selections of the candidate seeds by the grower and/or at least one other grower in a region of said grower, independent of historical performance of the candidate seeds in the growing space. The method then includes outputting the identified candidate seeds to the grower and including, based on a selection by the grower, at least one seed from the identified candidate seeds in the growing space.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
Systems and methods for use in identifying locations and/or sizes of trials in fields are provided. One example computer-implemented method includes defining a bounding box for a field based on a boundary line of the field and imposing multiple strips to the bounding box, where each strip has a dimension consistent with a desired planting pass for a trial in the field. The method also includes rotating the bounding box, with the strips, to an orientation consistent with a planting direction of the field and cropping the multiple strips consistent with one or more headlands of the field. The method then includes generating multiple candidate trials for the field, based on the multiple strips, calculating metrics for the candidate trials based on areas and shapes of the candidate trials, and selecting and publishing one or more of the candidate trials based on the metric as for implementation in the field.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
51.
SYSTEMS AND METHODS FOR USE IN IDENTIFYING TRIALS IN FIELDS
Systems and methods for use in identifying a trial in a target field are provided. One example computer-implemented method includes accessing, for a target field, planting data for the target field from a data structure and identifying one or multiple segment(s) of the target field having a feature distinct from a remainder of the field. The method also includes applying a first geometric threshold to the identified segment(s) and, in response to the identified segment(s) satisfying the first geometric threshold: validating a strip including the segment(s), based on a length and width of the strip, and building a trial based on the strip, wherein the trial includes the strip and at least one duplicate strip disposed along a long side of the strip, where the trial is identified as a location of the trial in the target field.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
52.
SYSTEMS AND METHODS FOR USE IN ASSESSING TRIALS IN FIELDS
Systems and methods are provided for use in assessing regions (for example, trials) in fields. One example computer-implemented method includes identifying, by an agricultural computer system, two regions in a field for assessment, where at least one of the two regions is associated with an agricultural trial in the field, and generating an aggregate fitness metric for the two regions. In doing so, the aggregate fitness metric is indicative of a similarity between the two regions in the field. The method also includes determining, by the agricultural computer system, whether the aggregate fitness metric satisfies a defined threshold and, in response to determining that the aggregate fitness metric fails to satisfy the defined threshold, automatically discarding the trial.
Systems and methods are provided for identifying candidate seeds for a grower. An example computer-implemented method includes identifying multiple seeds for a grower suitable to be selected for planting in a growing space associated with the grower, and accessing data from a data server including seed data representative of each of the multiple seeds. The method also includes identifying candidate seeds from the multiple seeds for the grower, based on a model specific to the grower, wherein the model is trained on historical selections of the candidate seeds by the grower and/or at least one other grower in a region of said grower, independent of historical performance of the candidate seeds in the growing space. The method then includes outputting the identified candidate seeds to the grower and including, based on a selection by the grower, at least one seed from the identified candidate seeds in the growing space.
Systems and methods for use in identifying locations and/or sizes of trials in fields are provided. One example computer-implemented method includes defining a bounding box for a field based on a boundary line of the field and imposing multiple strips to the bounding box, where each strip has a dimension consistent with a desired planting pass for a trial in the field. The method also includes rotating the bounding box, with the strips, to an orientation consistent with a planting direction of the field and cropping the multiple strips consistent with one or more headlands of the field. The method then includes generating multiple candidate trials for the field, based on the multiple strips, calculating metrics for the candidate trials based on areas and shapes of the candidate trials, and selecting and publishing one or more of the candidate trials based on the metric as for implementation in the field.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
Systems and methods for use in identifying a trial in a target field are provided. One example computer-implemented method includes accessing, for a target field, planting data for the target field from a data structure and identifying one or multiple segment(s) of the target field having a feature distinct from a remainder of the field. The method also includes applying a first geometric threshold to the identified segment(s) and, in response to the identified segment(s) satisfying the first geometric threshold: validating a strip including the segment(s), based on a length and width of the strip, and building a trial based on the strip, wherein the trial includes the strip and at least one duplicate strip disposed along a long side of the strip, where the trial is identified as a location of the trial in the target field.
Systems and methods are provided for use in assessing regions (for example, trials) in fields. One example computer-implemented method includes identifying, by an agricultural computer system, two regions in a field for assessment, where at least one of the two regions is associated with an agricultural trial in the field, and generating an aggregate fitness metric for the two regions. In doing so, the aggregate fitness metric is indicative of a similarity between the two regions in the field. The method also includes determining, by the agricultural computer system, whether the aggregate fitness metric satisfies a defined threshold and, in response to determining that the aggregate fitness metric fails to satisfy the defined threshold, automatically discarding the trial.
A method is provided for determining soil properties for an area of land, from soil spectrum data. In an embodiment, the method includes receiving soil spectrum data records from hyperspectral sensors that represent a mean soil spectrum of a specific geo-location of the area of land and removing interference signals from the spectrum data records to create soil spectral bands. The method also includes predicting a plurality of soil property datasets based on a partial least-square regression and the soil spectral bands and selecting specific soil property datasets from the plurality of soil property datasets to represent soil properties of the specific geo-location, wherein the specific soil property datasets include property data and spectral band data for spectral bands used to determine the property data. The specific soil property datasets may then be used to generate a crop prescription of recommended hybrid seeds or population densities for the specific geo-location.
G01V 99/00 - Subject matter not provided for in other groups of this subclass
G01N 21/359 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
A01B 76/00 - Parts, details or accessories of agricultural machines or implements, not provided for in groups
G01N 21/31 - Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
G01W 1/10 - Devices for predicting weather conditions
A system for implementing a trial a field is provided. In an embodiment, the system is configured to generate a trial recommendation for a field and, based on field data for the field, compute a yield probabilities for the field. The system is also configured to generate a plurality of outcome-based values for the field based on the yield probabilities, compute crop values for each of the outcome-based values and a bushel per acre value, and cause display of an interface that dynamically displays each of the plurality of outcome-based values for the field based on a selected bushel per acre value. The system is further configured to receive user input changing a position of an interactive sliding widget in the interface, to change the bushel per acre value, and in response, compute a crop value for each of the outcome-based values based on the changed bushel per acre value.
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
A computer-implemented method for improving crop yield in fields includes accessing data representative of predicted performance of seeds for a field and creating an experiment for the field based on the predicted performance of the seeds. The experiment includes a specific prescription to plant the seeds in the field. The method also includes transmitting the prescription to a planter and/or a harvester associated with the field and collecting, in real time, planting data for the seeds as the seeds are planted in the field and harvesting data for a crop grown in the field from the planted seeds. The method further includes validating that execution of the experiment in the field is consistent with the prescription and generating a recommendation based on a comparison of the collected harvesting data for the crop grown in the field to the predicted performance of the seeds included in the prescription.
An apparatus for communicating data between a vehicle or agricultural implement and a computing device includes a first housing, a second housing, and a connector sub-assembly. The second housing and the first housing define a first interior region, and the second housing and the connector sub-assembly define a second interior region. A first integrated circuit is disposed in the first interior region, and a second integrated circuit is disposed in the second interior region. A ground clip is coupled to the connector sub-assembly. The ground clip includes a first wall orthogonal to the first integrated circuit and a second wall coupled to an end portion of the first wall. The first wall includes a plurality of outwardly protruding resilient fingers configured to contact the second housing. A mating connector is coupled to the connector sub-assembly and configured to communicatively couple with a connector of the vehicle or agricultural implement.
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
61.
Leveraging genetics and feature engineering to boost placement predictability for seed product selection and recommendation by field
An example computer-implemented method includes receiving agricultural data records comprising a first set of yield properties for a first set of seeds grown in a first set of environments, and receiving genetic feature data related to a second set of seeds. The method further includes generating a second set of yield properties for the second set of seeds associated with a second set of environments by applying a model using the genetic feature data and the agricultural data records. In addition, the method includes determining predicted yield performance for a third set of seeds associated with one or more target environments by applying the second set of yield properties, and generating seed recommendations for the one or more target environments based on the predicted yield performance for the third set of seeds. In the present example, the method also includes causing display of the seed recommendations.
G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
62.
Detection of plant diseases with multi-stage, multi-scale deep learning
A computer system is provided comprising a classification model management server computer configured, by instructions, to: receive a new image from a user device; apply a first digital model to first regions within the new image for classifying each of the first regions into a particular class; apply a second digital model to second regions within the new image for classifying each of the second regions into a particular class; and transmit classification data related to the class of the first regions and the class of the second regions to the user device. In connection therewith, the second regions each generally correspond to a combination of multiple first regions.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
A method and system for decontaminating raw yield maps by combining filters with spatial outlier detectors is provided. In an embodiment, the method comprises receiving over a computer network electronic digital data comprising first yield data representing crop yields harvested from an agricultural field; applying one or more filters to the first yield data to identify, from the first yield data, first outlier data; generating first filtered data from the first yield data by removing the first outlier data from the first yield data; identifying, in the first filtered data, second outlier data representing outlier values based on one or more outlier characteristics; generating second outlier data from the first filtered data by removing the second outlier data from the first filtered data; generating and causing displaying on a mobile computing device a graphical representation of the crop yields harvested from the agricultural field using only the second outlier data.
A system for implementing a trial in one or more fields is provided. In an embodiment, a agricultural intelligence computing system receives field data for a plurality of agricultural fields. Based, at least in part, on the field data for the plurality of agricultural fields, the agricultural intelligence computing system identifies one or more target agricultural fields. The agricultural intelligence computing system sends, to a field manager computing device associated with the one or more target agricultural fields, a trial participation request. The server receives data indicating acceptance of the trial participation request from the field manager computing device. The server determines one or more locations on the one or more target agricultural fields for implementing a trial and sends data identifying the one or more locations to the field manager computing device.
F24F 11/49 - Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring ensuring correct operation, e.g. by trial operation or configuration checks
G01C 11/02 - Picture-taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures
Systems and methods are provided for generating a set of target seeds with optimal yield and risk performance. One example computer-implemented method includes generating, by a server, representative yield values for a group of seeds based on historical agricultural data, generating a dataset of risk values for the seeds, and generating a dataset of target seeds from the seeds for planting in one or more target fields based on: the dataset of risk values, the representative yield values, and properties for the target field(s). The method also includes generating, by the server, allocation instructions for the target seeds included in the dataset of target seeds, where the allocation instructions are indicative of, for each target seed, a planting quantity for the target seed and a planting location for the target seed within the target field(s).
An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of one or more products grown in a given field and continuous data indicative of multiple raw field features and specific to the given field. The method also includes transforming the raw field features into distinct feature classes and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between the one or more products. Further, the method includes generating, based at least in part on the genomic-by-environmental relationships, predicted yield performance for a set of products associated with one or more target environments, generating product recommendations for the one or more target environments based on the predicted yield performance for the set of products, and providing one or more instructions configured to cause display of the product recommendations.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
electronic controllers and monitors for agricultural equipment, implements and machinery; computer hardware and software for monitoring the operation of agricultural equipment, implements and machinery during operations; vehicle-mounted apparatus namely computer hardware and computer software sensors, transmitters, receivers, and global positioning satellite receivers, all for use in connection with collection, organizing, tracking, and analyzing field data with real time agronomic and weather data for farm and crop productivity, and tracking and monitoring agricultural equipment, implements and machinery. providing a secure website featuring use of non-downloadable software allowing web site users to input, collect, organize, track, estimate, and analyze field data with real time agronomic and weather data for farm and crop productivity; agricultural services, namely, soil sampling and crop observing for analysis purposes; agricultural services, namely, research and field testing; agricultural and farming field mapping services. providing agricultural information and advice via a website on a global computer network; agricultural advice, namely, providing data, data analysis, information, and recommendations to growers and farmers for the purposes of making informed seed purchases and other agronomic decisions; agricultural services, namely providing custom agronomic analysis and recommendations to agricultural producers, growers and farmers.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
electronic controllers and monitors for agricultural equipment, implements and machinery; computer hardware and software for monitoring the operation of agricultural equipment, implements and machinery during operations; vehicle-mounted apparatus namely computer hardware and computer software sensors, transmitters, receivers, and global positioning satellite receivers, all for use in connection with collection, organizing, tracking, and analyzing field data with real time agronomic and weather data for farm and crop productivity, and tracking and monitoring agricultural equipment, implements and machinery. providing a secure website featuring use of non-downloadable software allowing web site users to input, collect, organize, track, estimate, and analyze field data with real time agronomic and weather data for farm and crop productivity; agricultural services, namely, soil sampling and crop observing for analysis purposes; agricultural services, namely, research and field testing; agricultural and farming field mapping services. providing agricultural information and advice via a website on a global computer network; agricultural advice, namely, providing data, data analysis, information, and recommendations to growers and farmers for the purposes of making informed seed purchases and other agronomic decisions; agricultural services, namely providing custom agronomic analysis and recommendations to agricultural producers, growers and farmers.
69.
HYBRID SEED SELECTION AND SEED PORTFOLIO OPTIMIZATION BY FIELD
Systems and methods are provided for managing hybrid seeds for planting. One example computer-implemented method includes receiving a first dataset of hybrid seeds for planting on a target field, where the first dataset includes probability of success values and historical agricultural data for the hybrid seeds, and selecting a subset of hybrid seeds of the first dataset based on the probability of success values. The method also includes generating representative yield values for the subset of hybrid seeds based on the historical agricultural data, generating risk values for the subset of hybrid seeds based on the historical agricultural data, and generating a second dataset of hybrid seeds for planting based on the risk values, the representative yield values, and properties for the target field. The method further includes causing displaying the representative yield values and the risk values related to the second dataset of hybrid seeds for planting.
Disclosed herein are systems and methods for translating and verifying text in a variety of different languages for the same software application. When the text in the application changes, embodiments of the disclosure may include translating and/or verifying only the text that has changed. The system may compare the new screenshot(s) with previously accepted screenshot(s) to locate the text that has changed. The text that has not changed (since the last accepted translation) may not be translated and/or verified once accepted by a translator. The system may highlight the text that has changed so that the translator may focus only on the relevant portions of the user interface and not have to search for the text that has changed. For rejected translations, the system may repeat the process, translating and/or verifying only the rejected text (instead of translating/verifying all text again).
Described herein are methods and systems for generating shared collaborative maps for planting or harvesting operations. A method of generating a collaborative shared map between machines includes generating a first map for a first machine based on a first set of data and generating a second map for a second machine based on a second set of data. The method further includes generating at least one shared collaborative map for at least one of the first and second machines based on the first and second maps.
Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field having received a first treatment, and second yield data for a second portion of the agronomic field having received a second treatment different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, where the yield value indicates an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment and generates a prescription map including the second treatment.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G06F 17/18 - Complex mathematical operations for evaluating statistical data
A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for planting seeds in a field are provided. In an embodiment, a computer-implemented method includes receiving digital data representing planting parameters including seed type information and planting row width, and retrieving a set of seeding models based upon the planting parameters, where each of the seeding models includes a regression model defining a relationship between plant yield and seeding rate on a specific field. The method also includes generating an empirical mixture model as a composite distribution of the set of seeding models, generating a seeding rate distribution for the planting parameters based upon the empirical mixture model, and calculating a seeding rate recommendation based on the seed rate distribution. The method then also includes planting plant seeds in the specific field consistent with the seeding rate recommendation.
Techniques for providing improvements in agricultural science by optimizing irrigation treatment placements for testing are provided, including analyzing a plurality of digital images of a field to determine vegetation density changes in a sector of the field. The techniques proceed by comparing a distribution of pixel characteristics in the digital images for each field sector to determine sectors in which minimal differences are present. Instructions for irrigation placements and testing may then be displayed or modified based on the results of the sector determinations.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
Systems and methods are provided for use in applying treatments to crops in fields. One example computer-implemented method includes calculating a growth stage of a crop in a field on a defined date based on planting data and weather data for the field and/or crop, and then, in response to the growth stage being within a spray window for the crop, defining a plurality of synthetic sprays within the spray window for the field. The method then includes, for each one of the synthetic sprays, calculating at least one disease risk for the crop in the field and calculating a response to the synthetic spray. The method then further includes compiling a report including a selected one or more of the responses, based on yield differences of the responses, as a recommendation for applying the treatment to the crop consistent with the synthetic spray associated with the selected one or more of the responses.
Techniques for recommending side-by-side plantings of pairs of hybrids or seeds include a server computer receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first data for agricultural fields where the hybrid seeds were planted. The server receives second data for available hybrids and seeds and automatically calculates a dataset of success probability scores that describe the probability of a successful yield on the target fields. Data is organized as pairs to facilitate comparison of actual plantings to optimized plantings that have a probability of success (POS), in terms of yield lift or increased yield season-over-season, for different yield values. Confidence values are generated and stored in association with the POS values and can be used as a basis of visual output to support planting and/or field management decisions as part of an automated intelligent agricultural decision support system.
Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field, the first portion of the agronomic field having received a first treatment, and second yield data, for a second portion of the agronomic field, the second portion of the agronomic field having received a second treatment that is different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment. In an embodiment, in response to selecting the second treatment, the system generates a prescription map, the prescription map including the second treatment. The system may also generate one or more scripts which, when executed by an application controller, cause the application controller to control an operating parameter of an agricultural implement to apply the second treatment.
G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
G06F 17/18 - Complex mathematical operations for evaluating statistical data
Systems and methods are provided for use in applying treatments to crops in fields. One example computer-implemented method includes calculating a growth stage of a crop in a field on a defined date based on planting data and weather data for the field and/or crop, and then, in response to the growth stage being within a spray window for the crop, defining a plurality of synthetic sprays within the spray window for the field. The method then includes, for each one of the synthetic sprays, calculating at least one disease risk for the crop in the field and calculating a response to the synthetic spray. The method then further includes compiling a report including a selected one or more of the responses, based on yield differences of the responses, as a recommendation for applying the treatment to the crop consistent with the synthetic spray associated with the selected one or more of the responses.
Systems, methods, and devices are provided for monitoring precipitation. An example rain gauge device for use in such monitoring generally includes a first basin including at least one outlet for forming and releasing droplets of moisture, and at least two electrical contacts disposed proximate to the at least one outlet. A closed circuit is formed between the at least two electrical contacts when a droplet of moisture, released by the at least one outlet, contacts the at least two electrical contacts. The rain gauge device then also includes a processor communicatively coupled to the at least two electrical contacts. The processor is configured to determine presence of a moisture event based on the closed circuit formed by the droplet and the at least two electrical contacts and, in response to the determination, transmit an indication of the moisture event to a computing device.
Systems, methods, and devices are provided for monitoring precipitation. An example rain gauge device for use in such monitoring generally includes a first basin including at least one outlet for forming and releasing droplets of moisture, and at least two electrical contacts disposed proximate to the at least one outlet. A closed circuit is formed between the at least two electrical contacts when a droplet of moisture, released by the at least one outlet, contacts the at least two electrical contacts. The rain gauge device then also includes a processor communicatively coupled to the at least two electrical contacts. The processor is configured to determine presence of a moisture event based on the closed circuit formed by the droplet and the at least two electrical contacts and, in response to the determination, transmit an indication of the moisture event to a computing device.
Systems, methods, and devices are provided for monitoring precipitation. An example rain gauge device for use in such monitoring generally includes a first basin including at least one outlet for forming and releasing droplets of moisture, and at least two electrical contacts disposed proximate to the at least one outlet. A closed circuit is formed between the at least two electrical contacts when a droplet of moisture, released by the at least one outlet, contacts the at least two electrical contacts. The rain gauge device then also includes a processor communicatively coupled to the at least two electrical contacts. The processor is configured to determine presence of a moisture event based on the closed circuit formed by the droplet and the at least two electrical contacts and, in response to the determination, transmit an indication of the moisture event to a computing device.
Systems and methods for determining a risk of damage to a crop on a field are described. In an example embodiment, a method for limiting such damage to a crop includes receiving, for multiple hours, weather data identifying temperature values and humidity values for a geographic location of the field, determining, for the multiple hours, that a temperature value is within a first range of values and a humidity value is within a second range of values and, identifying each of the multiple hours as a risk hour for a disease. The method also includes computing a risk value for the field based on the identified risk hours, determining that the risk value is above a threshold, and determining that the crop on the field is at risk for the disease. The method then includes spraying the crop on the field with a damage mitigating chemical specific to the disease.
G06Q 10/0635 - Risk analysis of enterprise or organisation activities
G01W 1/06 - Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed giving a combined indication of weather conditions
84.
SYSTEMS AND METHODS FOR USE IN PLANTING SEEDS IN GROWING SPACES
Systems and methods for use in identifying a set of candidate seeds for a target field based on a prediction model are provided. One example method includes accessing, by a computing device, data from a data server, the data including data representative of seeds harvested from at least one of a research growing space, a development growing space, and a field growing space; generating a yield delta prediction model, based on at least a portion of the accessed data; for each of a plurality of candidate seeds, automatically generating a probability of a yield delta for the candidate seed, relative to a target seed, exceeding a performance threshold, based on the generated model; identifying, by the computing device, a set of the candidate seeds, based on the probability of the respective candidate seed satisfying a defined threshold; and outputting, by the computing device, the identified set of seeds to a user.
A display device includes a first area in which a foreign substance is disposed and a second area in which the foreign substance is not disposed. The display device includes a lower panel including a base layer, a display element layer, and an encapsulation layer, an upper panel disposed on the lower panel and including a light control layer and a color filter layer, and a filling layer filling a space between the lower panel and the upper panel. A distance between the display element layer and the light control layer in the first area is greater than a distance between the display element layer and the light control layer in the second area. Accordingly, defects caused by the foreign substance are prevented, and a display quality and a reliability of the display device are increased.
H01L 51/52 - Solid state devices using organic materials as the active part, or using a combination of organic materials with other materials as the active part; Processes or apparatus specially adapted for the manufacture or treatment of such devices, or of parts thereof specially adapted for light emission, e.g. organic light emitting diodes (OLED) or polymer light emitting devices (PLED) - Details of devices
H01L 27/32 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including components using organic materials as the active part, or using a combination of organic materials with other materials as the active part with components specially adapted for light emission, e.g. flat-panel displays using organic light-emitting diodes
H01L 51/56 - Processes or apparatus specially adapted for the manufacture or treatment of such devices or of parts thereof
86.
SYSTEMS AND METHODS FOR USE IN PLANTING SEEDS IN GROWING SPACES
Systems and methods for use in identifying a set of candidate seeds for a target field based on a prediction model are provided. One example method includes accessing, by a computing device, data from a data server, the data including data representative of seeds harvested from at least one of a research growing space, a development growing space, and a field growing space; generating a yield delta prediction model, based on at least a portion of the accessed data; for each of a plurality of candidate seeds, automatically generating a probability of a yield delta for the candidate seed, relative to a target seed, exceeding a performance threshold, based on the generated model; identifying, by the computing device, a set of the candidate seeds, based on the probability of the respective candidate seed satisfying a defined threshold; and outputting, by the computing device, the identified set of seeds to a user.
Systems and methods for use in identifying a set of candidate seeds for a target field based on a prediction model are provided. One example method includes accessing, by a computing device, data from a data server, the data including data representative of seeds harvested from at least one of a research growing space, a development growing space, and a field growing space; generating a yield delta prediction model, based on at least a portion of the accessed data; for each of a plurality of candidate seeds, automatically generating a probability of a yield delta for the candidate seed, relative to a target seed, exceeding a performance threshold, based on the generated model; identifying, by the computing device, a set of the candidate seeds, based on the probability of the respective candidate seed satisfying a defined threshold; and outputting, by the computing device, the identified set of seeds to a user.
In an embodiment, a method comprises determining, in received yield data, one or more passes, each pass including a plurality of observations. For each pass of the one or more passes, one or more discrete derivatives are determined, and based on the one or more discrete derivatives first outlier data is generated. First filtered data is generated by removing the first outlier data from the yield data. Furthermore, for each observation in the yield data, a plurality of nearest neighbor observations is determined, and used to determine a plurality of absolute differences in yield values. Based on the plurality of absolute differences, second outlier data is determined. Second filtered data is generated by removing the second outlier data from the first filtered data. Using a presentation layer of a computer system, a graphical representation of the second filtered data is generated and displayed on the computing system.
Methods are provided for improving performance of a computing system used to model potential crop yield. In one example embodiment, a computer-implemented method includes generating a model of potential crop yield, as a function of planting date and relative maturity based, at least in part, on one or more relative maturity maps, one or more planting date maps, and one or more actual production history maps, and storing the model in a memory of the server computer system. The method also includes receiving, via an interface at a field manager computing device, a selection of a particular field and computing, from the model of potential crop yield, a potential yield for the particular field based, at least in part, on a planting date for the particular field, a relative maturity value, and values representing actual production history for the particular field.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
H04L 43/045 - Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
H04L 67/10 - Protocols in which an application is distributed across nodes in the network
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
The present disclosure relates generally to agronomic modeling, and more specifically to determining uncertainty associated with agronomic predictions (e.g., agricultural yield of a field). An exemplary method comprises: receiving information associated with a location; providing the information to one or more trained machine-learning models; determining, based on the trained machine-learning models: a probabilistic distribution of the predicted crop yield of the location, wherein the probabilistic distribution is defined by a plurality of parameters; and an uncertainty measure associated with a moment of the probabilistic distribution of the predicted crop yield.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The present disclosure relates generally to agronomic modeling, and more specifically to determining uncertainty associated with agronomic predictions (e.g., agricultural yield of a field). An exemplary method comprises: receiving information associated with a location; providing the information to one or more trained machine-learning models; determining, based on the trained machine-learning models: a probabilistic distribution of the predicted crop yield of the location, wherein the probabilistic distribution is defined by a plurality of parameters; and an uncertainty measure associated with a moment of the probabilistic distribution of the predicted crop yield.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The present disclosure relates generally to agronomic modeling, and more specifically to determining uncertainty associated with agronomic predictions (e.g., agricultural yield of a field). An exemplary method comprises: receiving information associated with a location; providing the information to one or more trained machine-learning models; determining, based on the trained machine-learning models: a probabilistic distribution of the predicted crop yield of the location, wherein the probabilistic distribution is defined by a plurality of parameters; and an uncertainty measure associated with a moment of the probabilistic distribution of the predicted crop yield.
In an embodiment, an integrated sensor system with modular sensors and wireless connectivity components for monitoring properties of field soil is described. In an embodiment, an integrated sensor system comprises one or more sensors that are configured to determine one or more measures of at least one property of soil. The integrated sensor system also includes one or more processing units that are configured to receive, from the sensors, the measures of at least one property of soil and calculate soil property data based on the measures. The system further includes a transmitter that is configured to receive the soil property data from the processing units, establish a communications connection with at least one computer device, and automatically transmit the soil property data to the at least one computer device via the communications connection. In an embodiment, the communications connection is a wireless connection established between the transmitter and a smart hub or a LoRA-enabled device. In an embodiment, the computer sensors, the processors, and the transmitter are installed inside a portable probe.
H04W 4/80 - Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
A computer system and computer-implemented techniques for determining crop harvest times during a growing season based upon hybrid seed properties, weather conditions, and geo-location of planted fields is provided. In an embodiment, determining crop harvest times for corn fields may be accomplished using a server computer system that receives 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. Using digitally programmed equilibrium moisture content logic within the computer system to create and store, in computer memory, an equilibrium moisture content time series for the specific geo-location that is based upon weather data. The equilibrium moisture content is used to determine the rate of grain dry down because it gives a basis for how strongly water vapor will dissipate from a kernel to open air. Using digitally programmed grain moisture logic of the computer system to calculate and store in computer memory R6 moisture content for a specific hybrid seed based on a plurality of hybrid seed data. Using digitally programmed grain dry down logic of the computer system 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. The grain dry down time series is based upon the equilibrium moisture content time series, the estimated R6 date, the estimated R6 moisture content value, and specific hybrid seed properties. Using digitally programmed harvest recommendation logic of the computer system 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.
In an embodiment, autonomous vehicles with global positioning systems (GPS) are used for field inspection to reduce fuel and labor costs and improve reliability with increased consistency in field crop inspection. A vehicle may be programmed to traverse a field while using sensors to detect objects and operating in a first image capture mode, for example, capturing low-resolution images of objects in the field, typically crops. Under program control, machine vision techniques are used with the low-resolution images to recognize crops, non-crop plant material or undefined objects. Under program control, location data is used to correlate recognized objects with digitally stored field maps to resolve whether a particular object is in a location at which crop planting is expected or not expected. Under program control, depending on whether an object in a low-resolution digital image is recognized as a crop, and whether the object is in an expected geo-location for crops, the vehicle may cease traversing temporarily and switch to a second image capture mode, for example, capturing a high-resolution image of the object, for use in disease analysis or classification, weed analysis or classification, alert notifications or other messages, or other processing. In this manner, a field may be rapidly traversed and imaged using coarse-level, rapid techniques that require lower processing resources, storage or memory, while automatically switching to execute special processing only when necessary to resolve unexpected objects or to perform operations such as disease classification that benefit from high-resolution images and more intensive use of processing resources, storage or memory.
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G06V 30/24 - Character recognition characterised by the processing or recognition method
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
96.
Methods for generating soil maps and application prescriptions
Methods are provided for generating a prescription map for the application of crop inputs. In one method, the user draws a boundary on a map within a user interface and the system identifies relevant soil data and generates a soil map overlay and legend for changing the application prescription for various soils and soil conditions. In another method, the user instead drives a field boundary which is recorded on a planter monitor using a global positioning receiver, and the system generates a soil map and legend for changing the application prescription.
In one embodiment, a computer-implemented method includes receiving digital field data from an agricultural field representing one or more parameters of the field, soil, or crops in the field; retrieving historical data for the same field from one or more field databases; training and/or applying machine learning models to the field data and the historical data to derive representations of causality of one or more agronomic processes pertaining to the field; receiving user input specifying an anomaly to address via treatment, application or experiment; automatically adjusting the treatment, application or experiment to create a modified treatment, application or experiment that is most likely to generate result data that is usable to train machine learning models in an optimal manner.
G06F 7/48 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state deviceMethods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices
A01B 79/02 - Methods for working soil combined with other agricultural processing, e.g. fertilising, planting
Subfield moisture model improvement in generating overland flow modeling using shallow water calculations and kinematic wave calculations is disclosed. In an embodiment, a computer-implemented data processing method comprises: receiving precipitation data and infiltration data for an agricultural field; obtaining surface water depth data, surface water velocity data, and surface water discharge data for the same agricultural field; determining subfield geometry data for the agricultural field; executing a plurality of water calculations and wave calculations using the subfield geometry data to generate an overland flow model that includes moisture levels for the agricultural field; based on, at least in part, the overland flow model, generating and causing displaying a visual graphical image of the agricultural field comprising a plurality of color pixels having color values corresponding to the moisture levels determined for the agricultural field. Output of the overland flow model is provided to control computers of seeders, planters, fertilizer spreaders, harvesters, or combines to control seeding, planting, fertilizing or irrigation activities in the field.
Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
100.
Systems and methods for image capture and analysis of agricultural fields
Described herein are systems and methods for capturing images of a field and performing agricultural data analysis of the images. In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural image data including images of at least one stage of crop development that are captured with at least one of an apparatus and a remote sensor moving through a field. The computer includes at least one processing unit that is coupled to the database. The at least one processing unit is configured to execute instructions to analyze the captured images, to determine relevant images that indicate a change in at least one condition of the crop development, and to generate a localized view map layer for viewing the field at the at least one stage of crop development based on at least the relevant captured images.
G06F 16/58 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 16/587 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
