US 12,260,346 B2
Using optical remote sensors and machine learning models to predict agronomic field property data
Angeles Casas, San Francisco, CA (US); Xiaoyuan Yang, Pleasanton, CA (US); Ho Jin Kim, San Francisco, CA (US); and Steven Ward, Moraga, CA (US)
Assigned to CLIMATE LLC, Saint Louis, MO (US)
Filed by Climate LLC, Saint Louis, MO (US)
Filed on Jan. 7, 2021, as Appl. No. 17/143,536.
Claims priority of provisional application 62/958,211, filed on Jan. 7, 2020.
Prior Publication US 2021/0209490 A1, Jul. 8, 2021
Int. Cl. G06N 20/00 (2019.01); G06N 5/04 (2023.01)
CPC G06N 5/04 (2013.01) [G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, at an agricultural intelligence computer system, agronomic training data, wherein the agronomic training data comprises optical remote sensing data generated by optical remote sensors for a plurality of first agronomic fields, precipitation data for the plurality of first agronomic fields, and measured field data for the plurality of first agronomic fields, the optical remote sensing data including short-wave infrared (SWIR) band data indicating reflectance as a function of wavelength;
training a machine learning model, at the agricultural intelligence computer system, using a part of the optical remote sensing data, relative water content (RWC) of crop residue in the plurality of first agronomic fields based on the precipitation data, and the measured data of the agronomic training data, wherein the machine learning model is configured to predict agronomic field property data, wherein the part of the optical remote sensing data is defined by a wavelength range of 2100-2300 nm;
in response to receiving a request from a client computing device for agronomic field property data for one or more agronomic fields, automatically predicting the agronomic field property data for the one or more agronomic fields, using the trained machine learning model, wherein the agronomic field property data comprises crop residue cover (CRC) data indicating one or more percentages of one or more ground surface residue coverages for the one or more agronomic fields;
based on the agronomic field property data, automatically generating a first graphical representation;
causing to display the first graphical representation on the client computing device.