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:
Jun. 13, 2023

Filed:

Dec. 18, 2018
Applicant:

Mineral Earth Sciences Llc, Mountain View, CA (US);

Inventors:

Cheng-en Guo, Santa Clara, CA (US);

Wilson Zhao, Fremont, CA (US);

Jie Yang, Sunnyvale, CA (US);

Zhiqiang Yuan, San Jose, CA (US);

Assignee:

MINERAL EARTH SCIENCES LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 3/40 (2006.01); A01D 41/127 (2006.01); G06T 5/50 (2006.01); G06T 7/143 (2017.01); G06N 3/08 (2023.01); G06Q 10/04 (2023.01); G06Q 50/02 (2012.01); G06V 20/10 (2022.01); G06N 3/047 (2023.01); G06V 10/82 (2022.01); G06V 20/13 (2022.01);
U.S. Cl.
CPC ...
G06T 3/4007 (2013.01); A01D 41/127 (2013.01); G06N 3/047 (2023.01); G06N 3/08 (2013.01); G06Q 10/04 (2013.01); G06Q 50/02 (2013.01); G06T 5/50 (2013.01); G06T 7/0016 (2013.01); G06T 7/143 (2017.01); G06V 10/82 (2022.01); G06V 20/13 (2022.01); G06V 20/188 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30181 (2013.01); G06T 2207/30188 (2013.01); G06V 20/194 (2022.01);
Abstract

Implementations relate to crop yield prediction at the field- and pixel-level. In various implementations, a first temporal sequence of high-elevation digital images may be obtained that capture a first geographic area and are acquired over a first predetermined time interval while the first geographic area includes a particular crop. A first plurality of other data points may also be obtained that influence a ground truth crop yield of the first geographic area after the first predetermined time interval. The first plurality of other data points may be grouped into temporal chunks corresponding temporally with respective images of the first temporal sequence. The first temporal sequence and the temporal chunks of the first plurality of other data points may be applied, e.g., iteratively, as input across a machine learning model to estimate a crop yield of the first geographic area at the end of the first predetermined time interval.


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