US 11,882,784 B2
Predicting soil organic carbon content
Cheng-En Guo, Santa Clara, 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 Mar. 1, 2023, as Appl. No. 18/116,185.
Application 18/116,185 is a continuation of application No. 17/147,048, filed on Jan. 12, 2021, granted, now 11,606,896.
Prior Publication US 2023/0210040 A1, Jul. 6, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 20/10 (2022.01); G01N 33/24 (2006.01); G06T 7/00 (2017.01); A01B 79/00 (2006.01)
CPC A01B 79/005 (2013.01) [G01N 33/24 (2013.01); G06T 7/0004 (2013.01); G06V 20/188 (2022.01); G06T 2207/30188 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method implemented using one or more processors, comprising:
obtaining a plurality of digital images depicting a field over multiple growing seasons;
applying the plurality of digital images as input to one or more machine learning models to generate first output indicative of two or more inferred agricultural management practices implemented in the field across the multiple growing seasons, wherein the two or more inferred agricultural management practices include:
an inferred tillage practice employed in the field during the multiple growing seasons;
a crop rotation employed in the field during the multiple growing seasons; or
a cover crop grown in the field during the multiple growing seasons; and
applying data indicative of the two or more inferred agricultural management practices as input to one or more additional machine learning models to generate second output, wherein the second output represents a predicted measure of soil organic carbon (SOC) of the field.