US 12,008,744 B2
Mapping field anomalies using digital images and machine learning models
Boyan Peshlov, Chesterfield, MO (US); and Weilin Wang, Ballwin, MO (US)
Assigned to CLIMATE LLC, St. Louis, MO (US)
Filed by Climate LLC, San Francisco, CA (US)
Filed on Dec. 9, 2019, as Appl. No. 16/707,355.
Claims priority of provisional application 62/777,748, filed on Dec. 10, 2018.
Prior Publication US 2020/0193589 A1, Jun. 18, 2020
Int. Cl. G06T 7/00 (2017.01); A01B 69/04 (2006.01); B64C 39/02 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06V 10/82 (2022.01); G06V 10/94 (2022.01); G06V 20/10 (2022.01); A01G 25/16 (2006.01); B64U 101/30 (2023.01); G06V 10/44 (2022.01)
CPC G06T 7/0004 (2013.01) [A01B 69/008 (2013.01); B64C 39/02 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06V 10/82 (2022.01); G06V 10/955 (2022.01); G06V 20/188 (2022.01); A01G 25/16 (2013.01); B64U 2101/30 (2023.01); G06T 2207/10024 (2013.01); G06T 2207/10036 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30188 (2013.01); G06V 10/454 (2022.01); G06V 20/194 (2022.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating an improved map of field anomalies using digital images and machine learning models, the method comprising:
obtaining a shapefile that defines boundaries of an agricultural plot;
based on the shapefile, obtaining a plurality of ground based plot images from one or more image capturing devices mounted at a fixed ground location or a ground vehicle at the agricultural plot;
calibrating the plurality of ground based plot images;
stitching the plurality of calibrated ground based plot images into a plot map of the agricultural plot at a plot level;
generating a plot grid;
based on the plot grid and the plot map, defining a plurality of plot tiles for the agricultural plot, each of the plurality of plot tiles including multiple pixels of the plurality of calibrated ground based plot images;
classifying the plurality of plot tiles, using a first machine learning model and a plurality of first image classifiers corresponding to one or more first anomalies, into a set of classified plot images that depicts at least one anomaly, wherein each of the plurality of plot tiles is classified into classifications at least corresponding to a crop, a weed, trees, and inter-row damage;
determining, for each image in the set of classified plot images, a probability that the image is correctly classified, and further comparing the probability to an acceptable probability;
based on the set of classified plot images, generating a plot anomaly map for the agricultural plot; and
transmitting the plot anomaly map to one or more controllers that control one or more agricultural machines to perform agricultural functions on the agricultural plot.