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公开(公告)号：US09881214B1
公开(公告)日：2018-01-30
申请号：US15209628
申请日：2016-07-13
Applicant: The Climate Corporation
CPC classification number: G06K9/00657 , G06K9/42 , G06K9/6215
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：US20180018517A1
公开(公告)日：2018-01-18
申请号：US15209628
申请日：2016-07-13
Applicant: The Climate Corporation
CPC classification number: G06K9/00657 , G06K9/42 , G06K9/6215
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：US20160290918A1
公开(公告)日：2016-10-06
申请号：US14675992
申请日：2015-04-01
Applicant: The Climate Corporation
IPC: G01N21/359 , G01N33/00
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.
Abstract translation: 提供了使用区域农业数据确定生长季节国家作物产量的方法。 在一个实施方案中，在生长季节期间确定国家作物产量可以使用服务器计算机系统来实现，所述服务器计算机系统经由网络接收用于预测特定年份的国家作物产量的农业数据记录。 在服务器计算机系统内，农业时间序列模块接收一个或多个农业数据记录，该农业数据记录表示在特定时间在特定地理位置处与植物相关的协变量数据值的类型。 农业时间序列模块然后聚合农业数据记录以创建在指定时间内表示特定地理位置的一个或多个地理特定时间序列。 农业时间序列模块创建一个或多个聚合时间序列，其表示来自一个或多个地理特定时间序列的子集的地理区域。 作物产量估计模块从一个或多个聚合时间序列中选择代表性特征，并为服务器计算机系统的计算机存储器中的每个特定地理区域创建协变矩阵。 作物产量估算模块通过使用线性回归模块从代表该特定年份的特定状态的协变量矩阵计算特定状态作物产量来确定特定年份的特定状态作物产量。 作物估算模块通过使用分布生成模块，根据国家根据国家产量调整模块进行调整的具体国家特定国家作物产量的总和来计算特定年份的国家作物产量 。
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公开(公告)号：US11275197B2
公开(公告)日：2022-03-15
申请号：US16791918
申请日：2020-02-14
Applicant: The Climate Corporation
IPC: G01V99/00 , G01N21/359 , G01N33/00 , G06Q10/04 , G06F30/27 , A01B76/00 , G01N21/31 , G01W1/10 , G01N33/24 , G01N21/17 , A01B79/00 , G06F111/06
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：US11122734B1
公开(公告)日：2021-09-21
申请号：US16841995
申请日：2020-04-07
Applicant: The Climate Corporation
Abstract: In an approach, hyperspectral and/or multispectral remote sensing images are automatically analyzed by a nitrogen analysis subsystem to estimate the value of nitrogen variables of crops or other plant life located within the images. For example, the nitrogen analysis subsystem may contain a data collector module, a function generator module, and a nitrogen estimator module. The data collector module prepares training data which is used by the function generator module to train a mapping function. The mapping function is then used by the nitrogen estimator module to estimate the values of nitrogen variables for a new remote sensing image that is not included in the training set. The values may then be reported and/or used to determine an optimal amount of fertilizer to add to a field of crops to promote plant growth.
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Patent Agency Ranking
26.发明申请公开(公告)号：US20200320647A1
公开(公告)日：2020-10-08
申请号：US16853601
申请日：2020-04-20
Applicant: THE CLIMATE CORPORATION
Inventor： Lijuan Xu , Ying Xu , Ankur Gupta
Abstract: A computer implemented method for generating digital models of relative crop yield based on nitrate values in the soil is provided. In an embodiment, nitrate measurements from soil during a particular portion of a crop's development and corresponding crop yields are received by an agricultural intelligence computing system. Based, at least in part, on the nitrate measurements and corresponding crop yields, the system determines maximum yields for each location of a plurality of locations. The system then converts each crop yield value into a relative crop yield by dividing the crop yield value by the maximum crop yield for the location. Using the relative crop yields and the corresponding nitrate values in the soil, the system generates a digital model of relative crop yield as a function of nitrate in the soil during the particular portion of the crop's development. When the system receives nitrate measurements from soil in a particular field during the particular portion of a crop's development, the system computes a relative yield value for the particular field using the model of relative crop yield.
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公开(公告)号：US20200279179A1
公开(公告)日：2020-09-03
申请号：US16813509
申请日：2020-03-09
Applicant: The Climate Corporation
Inventor： Jiunn-Ren Chen , Ying Xu
Abstract: 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.
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公开(公告)号：US20170213141A1
公开(公告)日：2017-07-27
申请号：US15004820
申请日：2016-01-22
Applicant: The Climate Corporation
Abstract: A method for 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 weather index calculation instructions receive one or more agricultural data records that represent observed agricultural data points for a specific geo-location at a specific time. The observed agricultural data points include, but are not limited to, observed temperature records, soil moisture records, and precipitation records for the specific geo-location. The weather index calculation instructions then calculate one or more weather index values from the one or more agricultural data records. Weather index values represent crop stress that may affect the crop yield of particular plants. The weather index values are then compiled into one or more geo-specific weather indices for one or more geo-locations over a specified period of time. Each of the geo-specific weather indices may contain weather index values representing various types of stress events on crop including, but not limited to, flood stress, drought stress, early drought stress, daytime heat stress, and nighttime heat stress. Within the server computer system, weather indices aggregation instructions aggregate the one or more geo-specific weather indices to create one or more aggregated weather index data series, where each aggregated weather index data series contains weather index values for a specific calculated weather index for one or more specific geo-locations. Each weather index data series represents a specific geographic area from a subset of the one or more geo-specific weather indices. Crop yield estimating instructions, in the server computer system, then select representative features from each aggregated weather index data series within each of the one or more aggregated weather index data series. The crop yield estimating instructions then creates a covariate matrix for each specific geographic area in computer memory of the server computer system. The covariate matrix contains the representative features selected from the one or more aggregate weather index data series. The crop yield for the specific geographic area for the specific year is calculated using linear regression instructions to calculate the specific geographic area crop yield from the covariate matrix that represents the specific geographic area for that specific year. The parameters of the linear regression instructions include regression parameters that are calculated using distribution generation instructions and an error term that is calculated using the distribution generation instructions where a mean parameter for the error term is zero and the variance parameter is a geographic area specific bias coefficient. After determining crop yields for the specific geographic area representing the one or more aggregated weather index data series, the crop estimating instructions determine a national crop yield for the specific year by using the distribution generation instructions to calculate the national crop yield for a specific year from the sum of the crop yields for the specific geographic areas for the specific year nationally adjusted using national yield adjustment instructions. In an embodiment, the crop yield may refer to a specific crop yield such as corn yield.
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29.发明申请公开(公告)号：US20170196171A1
公开(公告)日：2017-07-13
申请号：US14990463
申请日：2016-01-07
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：US20170161627A1
公开(公告)日：2017-06-08
申请号：US14956657
申请日：2015-12-02
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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