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US Patent 10338272 Estimating soil properties within a field using hyperspectral remote sensing

Patent 10338272 was granted and assigned to The Climate Corporation on July, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
The Climate Corporation
The Climate Corporation
Current Assignee
The Climate Corporation
The Climate Corporation
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10338272
Date of Patent
July 2, 2019
Patent Application Number
14866160
Date Filed
September 25, 2015
Patent Citations Received
‌
US Patent 11938942 Method and apparatus for automating power take-offs for vehicles and equipment
0
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US Patent 10657372 Systems and methods for identifying and displaying optimal locations for a garden
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US Patent 11568467 Leveraging feature engineering to boost placement predictability for seed product selection and recommendation by field
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US Patent 11593674 Leveraging genetics and feature engineering to boost placement predictability for seed product selection and recommendation by field
Patent Primary Examiner
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Bo Fan
Patent abstract

A method for estimating soil properties within a field using hyperspectral remotely sensed data is provided. In an embodiment, estimating soil properties may be accomplished using a server computer system that receives, via a network, soil spectrum data records that are used to predict soil properties for a specific geo-location. Within the server computer system a soil preprocessing module receives one or more soil spectrum data records that represent a mean soil spectrum of a specific geo-location of a specified area of land. The soil preprocessing module then removes interference signals from the soil spectrum data, creating a set of one or more spectral bands. By removing interference signals, the spectral bands are not erroneously skewed from effects such as baseline drift, particle deviation, and surface heterogeneity. A soil regression module inputs the one or more soil spectral bands and predicts soil property datasets. The soil property datasets include specific soil properties relevant to determining fertility of the soil or soil property levels that may influence soil management at a specific geo-location. The soil regression module then takes the multiple soil property datasets and selects multiple specific soil property datasets that best represent the existing soil properties. Included in the soil property datasets are the multiple soil properties predicted and the spectral band data used to determine the specific soil properties. The soil regression module sends this predicted data to a soil model database.

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