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公开(公告)号：US20210406745A1
公开(公告)日：2021-12-30
申请号：US17369835
申请日：2021-07-07
Applicant: The Climate Corporation
Abstract: A method for predicting field specific crop yield recommendations for a field may be accomplished using a server computer system that is configured and programmed to receive over a digital communication network, electronic digital data representing agricultural data records, including remotely sensed spectral property of plant records and soil moisture records. Using digitally programmed data record aggregation instructions, the computer system is programmed to receive digital data representing including remotely sensed spectral property of plant records and soil moisture records. Using the digitally programmed data record aggregation instructions, the computer system is programmed to aggregate the one or more digital agricultural records to create and store, in computer memory, one or more geo-specific time series over a specified time. Using the digitally programmed data record aggregation instructions, the computer system is programmed to select one or more representative features from the one or more geo-specific time series and create, for each specific geographic area, a covariate matrix in computer memory comprising the representative features selected from the one or more geo-specific time series. Using mixture linear regression instructions, the computer system is programmed to assign a probability value to a component group in a set of parameter component groups, where each component group within the set of parameter component groups includes one or more regression coefficients calculated from a probability distribution and an error term calculated from a probability distribution. Using distribution generation instructions, the computer system is programmed to generate the probability distributions used to determine the one or more regression coefficients and the error term, the probability distribution used to generate the error term is defined with a mean parameter set at zero and a variance parameter set to a field specific bias coefficient.
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2.发明申请公开(公告)号：US20200329650A1
公开(公告)日：2020-10-22
申请号：US16916022
申请日：2020-06-29
Applicant: The Climate Corporation
Inventor： Ying Xu , Erik Andrejko
Abstract: A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.
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公开(公告)号：US20200226374A1
公开(公告)日：2020-07-16
申请号：US16828633
申请日：2020-03-24
Applicant: The Climate Corporation
Abstract: Systems and methods for scalable comparisons between two pixel maps are provided. In an embodiment, an agricultural intelligence computer system generates pixel maps from non-image data by transforming a plurality of values and location values into pixel values and pixel locations. The non-image data may include data relating to a particular agricultural field, such as nutrient content in the soil, pH values, soil moisture, elevation, temperature, and/or measured crop yields. The agricultural intelligence computer system converts each pixel map into a vector of values. The agricultural intelligence computer system also generates a matrix of metric coefficients where each value in the matrix of metric coefficients is computed using a spatial distance between to pixel locations in one of the pixel maps. Using the vectors of values and the matrix of metric coefficients, the agricultural intelligence computer system generates a difference metric identifying a difference between the two pixel maps. In an embodiment, the difference metric is normalized so that the difference metric is scalable to pixel maps of different sizes. The difference metric may then be used to select particular images that best match a measured yield, identify relationships between field values and measured crop yields, identify and/or select management zones, investigate management practices, and/or strengthen agronomic models of predicted yield.
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4.发明授权公开(公告)号：US10694686B2
公开(公告)日：2020-06-30
申请号：US16375589
申请日：2019-04-04
Applicant: The Climate Corporation
Inventor： Ying Xu , Erik Andrejko
Abstract: A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.
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公开(公告)号：US20200151831A1
公开(公告)日：2020-05-14
申请号：US16734813
申请日：2020-01-06
Applicant: The Climate Corporation
IPC: G06Q50/02
Abstract: A method for determining national crop yields during a growing season is accomplished using a server computer system that receives observed agricultural data records for a specific geo-location at a specific time. The server calculates weather index values from the agricultural data records that represent crop stress on plants. Geo-specific weather indices are generated from the weather index values, which then are aggregated to generate aggregated weather index data series. Representative features are selected from each aggregated weather index data series to create a covariate matrix for each geographic area. Crop yield for the geographic area is calculated using a linear regression model based on the covariate matrix for the specific geographic area. The server determines a national crop yield for the specific year as a sum of the crop yields for the specific geographic areas nationally adjusted using national yield adjustment instructions.
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Patent Agency Ranking
公开(公告)号：US10599927B2
公开(公告)日：2020-03-24
申请号：US15831269
申请日：2017-12-04
Applicant: The Climate Corporation
Abstract: Systems and methods for scalable comparisons between two pixel maps are provided. In an embodiment, an agricultural intelligence computer system generates pixel maps from non-image data by transforming a plurality of values and location values into pixel values and pixel locations. The non-image data may include data relating to a particular agricultural field, such as nutrient content in the soil, pH values, soil moisture, elevation, temperature, and/or measured crop yields. The agricultural intelligence computer system converts each pixel map into a vector of values. The agricultural intelligence computer system also generates a matrix of metric coefficients where each value in the matrix of metric coefficients is computed using a spatial distance between to pixel locations in one of the pixel maps. Using the vectors of values and the matrix of metric coefficients, the agricultural intelligence computer system generates a difference metric identifying a difference between the two pixel maps. In an embodiment, the difference metric is normalized so that the difference metric is scalable to pixel maps of different sizes. The difference metric may then be used to select particular images that best match a measured yield, identify relationships between field values and measured crop yields, identify and/or select management zones, investigate management practices, and/or strengthen agronomic models of predicted yield.
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公开(公告)号：US11062223B2
公开(公告)日：2021-07-13
申请号：US14956657
申请日：2015-12-02
Applicant: The Climate Corporation
Abstract: A method for predicting field specific crop yield recommendations is disclosed. A computer system receives data records, including remotely sensed spectral property of plant records and soil moisture records. The system aggregates the records to create geo-specific time series over a specified time. The system selects representative features from the geo-specific time series and creates, for each specific geographic area, a covariate matrix in computer memory comprising the representative features. The system assigns a probability value to a component group in a set of parameter component groups, where each component group includes one or more regression coefficients and an error term calculated from probability distributions. The system is programmed to generate generates the probability distributions used to determine the regression coefficients and the error term, the probability distribution used to generate the error term is defined with a mean parameter set at zero and a variance parameter set to a field specific bias coefficient.
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公开(公告)号：US10769733B2
公开(公告)日：2020-09-08
申请号：US16734813
申请日：2020-01-06
Applicant: The Climate Corporation
Abstract: A method for determining national crop yields during a growing season is accomplished using a server computer system that receives observed agricultural data records for a specific geo-location at a specific time. The server calculates weather index values from the agricultural data records that represent crop stress on plants. Geo-specific weather indices are generated from the weather index values, which then are aggregated to generate aggregated weather index data series. Representative features are selected from each aggregated weather index data series to create a covariate matrix for each geographic area. Crop yield for the geographic area is calculated using a linear regression model based on the covariate matrix for the specific geographic area. The server determines a national crop yield for the specific year as a sum of the crop yields for the specific geographic areas nationally adjusted using national yield adjustment instructions.
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公开(公告)号：US20200183048A1
公开(公告)日：2020-06-11
申请号：US16791918
申请日：2020-02-14
Applicant: The Climate Corporation
Abstract: A method for determining national crop yields during the growing season is provided. In an embodiment, a server computer system receives agricultural data records for a particular year that represent covariate data values related to plants at a specific geo-location at a specific time. The system aggregates the records to create geo-specific time series for a geo-location over a specified time. The system creates aggregated time series from a subset of the geo-specific time series. The system selects a representative feature from the aggregated time series and creates a covariate matrix for each specific geographic area in computer memory. The system determines a specific state crop yield for a specific year using linear regression to calculate the specific state crop yield from the covariate matrix. The system determines a national crop yield for the specific year using the sum of the state crop yields for the specific year nationally adjusted.
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公开(公告)号：US10564316B2
公开(公告)日：2020-02-18
申请号：US14675992
申请日：2015-04-01
Applicant: The Climate Corporation
IPC: G01V99/00 , G01N21/359 , G01N33/00 , G06Q10/04 , G01W1/10 , G01N33/24 , G01N21/17 , G01N21/31 , A01B79/00
Abstract: A method for determining national crop yields during the growing season using regional agricultural data is provided. In an embodiment, determining national crop yields during the growing season may be accomplished using a server computer system that receives, via a network, agricultural data records that are used to forecast a national crop yield for a particular year. Within the server computer system an agricultural time series module receives one or more agricultural data records that represent a type of covariate data value related to plants at a specific geo-location at a specific time. The agricultural time series module then aggregates the agricultural data records to create one or more geo-specific time series that represent a specific geo-location over a specified time. The agricultural time series module creates one or more aggregated time series that represent geographic areas from a subset of the one or more geo-specific time series. A crop yield estimating module selects a representative feature from the one or more aggregated time series and creates a covariate matrix for each specific geographic area in computer memory of the server computer system. The crop yield estimating module determines a specific state crop yield for a specific year by using a linear regression module to calculate the specific state crop yield from the covariate matrix that represents the specific state for that specific year. The crop estimation module determines a national crop yield for the specific year by using the distribution generation module to calculate the national crop yield for a specific year from the sum of the specific state crop yields for the specific year nationally adjusted using a national yield adjustment module.
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