US 11,900,560 B2
Generating crop yield predictions of geographic areas
Cheng-en Guo, Santa Clara, CA (US); Wilson Zhao, Fremont, CA (US); Jie Yang, Sunnyvale, CA (US); Zhiqiang Yuan, San Jose, CA (US); and Elliott Grant, Woodside, CA (US)
Assigned to MINERAL EARTH SCIENCES LLC, Mountain View, CA (US)
Filed by Mineral Earth Sciences LLC, Mountain View, CA (US)
Filed on Dec. 27, 2022, as Appl. No. 18/089,337.
Application 18/089,337 is a continuation of application No. 17/160,928, filed on Jan. 28, 2021, granted, now 11,562,486.
Application 17/160,928 is a continuation of application No. 16/236,743, filed on Dec. 31, 2018, granted, now 10,949,972, issued on Mar. 16, 2021.
Claims priority of provisional application 62/748,296, filed on Oct. 19, 2018.
Prior Publication US 2023/0140138 A1, May 4, 2023
Int. Cl. G06T 3/40 (2006.01); G06T 5/50 (2006.01); G06V 20/10 (2022.01); A01D 41/127 (2006.01); G06T 7/143 (2017.01); G06V 10/82 (2022.01); G06T 7/00 (2017.01); G06N 3/08 (2023.01); G06Q 10/04 (2023.01); G06Q 50/02 (2012.01); G06N 3/047 (2023.01); G06V 20/13 (2022.01)
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)] 20 Claims
OG exemplary drawing
 
1. A method implemented using one or more processors, comprising:
obtaining a temporal sequence of high-elevation digital images, wherein the temporal sequence of high elevation digital images capture a geographic area under consideration through at least part of a crop cycle of a selected type of crop growing in the geographic area; and
generating a ground truth crop yield prediction of the geographic area at the end of the crop cycle of the selected type of crop growing in the geographic area by applying the high-elevation digital images of the temporal sequence and ground truth operational data associated with the geographic area under consideration as input across a machine learning model, wherein the ground truth operational data includes data about one or more of
application of pesticide in the geographic area;
application of fertilizer in the geographic area; or
crop rotation implemented in the geographic area.