The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 12, 2022

Filed:

Feb. 03, 2020
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Mukul Tewari, Lafayette, CO (US);

Jitendra Singh, Noida, IN;

Kuntal Dey, Vasant Kunj, IN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A01C 14/00 (2006.01); G06Q 50/02 (2012.01); G06V 20/10 (2022.01); B64G 1/10 (2006.01); B64C 39/02 (2006.01); G06V 20/68 (2022.01);
U.S. Cl.
CPC ...
A01C 14/00 (2013.01); G06Q 50/02 (2013.01); G06V 20/188 (2022.01); B64C 39/024 (2013.01); B64C 2201/123 (2013.01); B64G 1/1021 (2013.01); B64G 2001/1028 (2013.01); G06V 20/68 (2022.01);
Abstract

Method, apparatus, and computer program product are provided for estimating crop type and/or sowing date. In some embodiments, a historical crop growth time series and a plurality of simulated crop growth time series are determined, and the historical time series is matched against each simulated time series to determine an estimated crop type and/or sowing date. For example, one simulated time series may be determined for each crop type/sowing date combination within a set of one or more crop types and one or more sowing dates based on historical crop data. Each time series represents crop growth in an area of interest and comprises element(s) including crop-specific parameter(s), such as leaf area index (LAI). The historical time series may be determined based on remote sensor data. Each simulated time series may be determined using a crop growth simulation model and based on historical crop data, geospatial data, and weather data.


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