公开(公告)号：US11930743B2

公开(公告)日：2024-03-19

申请号：US17856929

申请日：2022-07-01

Applicant: CLIMATE LLC

Inventor： Erik Andrejko ,  Ying Xu

IPC: A01G22/00 ,  A01G7/00 ,  G06Q10/04 ,  G06Q50/02 ,  H04L43/045 ,  H04L67/10 ,  H04L67/12

CPC classification number: A01G22/00 ,  A01G7/00 ,  G06Q10/04 ,  G06Q50/02 ,  H04L43/045 ,  H04L67/10 ,  H04L67/12

Abstract: 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.

公开(公告)号：US11375674B2

公开(公告)日：2022-07-05

申请号：US16916022

申请日：2020-06-29

Applicant: CLIMATE LLC

Inventor： Ying Xu ,  Erik Andrejko

IPC: A01G22/00 ,  G06Q50/02 ,  A01G7/00 ,  G06Q10/04 ,  H04L43/045 ,  H04L67/10 ,  H04L67/12

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.

公开(公告)号：US20220196877A1

公开(公告)日：2022-06-23

申请号：US17693158

申请日：2022-03-11

Applicant: CLIMATE LLC

Inventor： Lijuan Xu ,  Ying Xu

IPC: G01V99/00 ,  G01N21/359 ,  G01N33/00 ,  G06Q10/04 ,  G06F30/27 ,  A01B76/00 ,  G01N21/31 ,  G01W1/10 ,  G01N33/24

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 crop yield for a specific year using linear regression to calculate the specific crop yield from the covariate matrix. The system determines a forecasted crop yield for the specific year using a sum of the specific crop yields for the specific year, as adjusted.

公开(公告)号：US11762125B2

公开(公告)日：2023-09-19

申请号：US17693158

申请日：2022-03-11

Applicant: CLIMATE LLC

Inventor： Lijuan Xu ,  Ying Xu

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

CPC classification number: G01V99/005 ,  A01B76/00 ,  G01N21/31 ,  G01N21/359 ,  G01N33/0098 ,  G01N33/24 ,  G01W1/10 ,  G06F30/27 ,  G06Q10/04 ,  A01B79/005 ,  G01N2021/1793 ,  G01N2021/3155 ,  G01N2201/0616 ,  G01N2201/129 ,  G06F2111/06

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 crop yield for a specific year using linear regression to calculate the specific crop yield from the covariate matrix. The system determines a forecasted crop yield for the specific year using a sum of the specific crop yields for the specific year, as adjusted.

公开(公告)号：US11557116B2

公开(公告)日：2023-01-17

申请号：US16828633

申请日：2020-03-24

Applicant: Climate LLC

Inventor： Hao Zhong ,  Ying Xu

IPC: G06V20/10 ,  G06K9/62 ,  G06V10/32

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.