US 11,756,232 B2
Edge-based crop yield prediction
Kathleen Watson, Sunnyvale, CA (US); Jie Yang, Sunnyvale, CA (US); and Yueqi Li, San Jose, CA (US)
Assigned to MINERAL EARTH SCIENCES LLC, Mountain View, CA (US)
Filed by Mineral Earth Sciences LLC, Mountain View, CA (US)
Filed on Oct. 5, 2022, as Appl. No. 17/960,432.
Application 17/960,432 is a continuation of application No. 16/715,285, filed on Dec. 16, 2019, granted, now 11,508,092.
Prior Publication US 2023/0028706 A1, Jan. 26, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01)
CPC G06T 7/97 (2017.01) [G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30188 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for making a real time prediction of an agricultural metric for a crop grown in a field in real time, comprising:
retrieving a superset of high-resolution images that depict a plurality of plants in the field, wherein the superset of high-resolution images are acquired using one or more vision sensors carried by one or more robots;
sampling, by one or more edge computing devices associated with the field, from the acquired superset of high-resolution images, a subset of discrete high-resolution images;
applying, by one or more of the edge computing devices, data indicative of the sampled subset of discrete high-resolution images across a first machine learning model, along with local weather data, to generate output indicative of the real time prediction of the agricultural metric for the field, without applying data indicative of other acquired images of the superset outside of the sampled subset as input across the first machine learning model; and
generating, based on the output using one or more of the edge computing devices, for presentation at one or more computing devices, the real time prediction of the agricultural metric.