US 11,704,581 B1
Determining crop-yield drivers with multi-dimensional response surfaces
John A. McEntire, Park City, UT (US); and Thomas A. Dye, Austin, TX (US)
Assigned to ARVA INTELLIGENCE CORP., Salt Lake City, UT (US)
Filed by ARVA INTELLIGENCE CORP, Park City, UT (US)
Filed on Mar. 16, 2021, as Appl. No. 17/203,670.
Application 17/203,670 is a continuation in part of application No. 17/180,695, filed on Feb. 19, 2021.
Application 17/180,695 is a continuation in part of application No. 17/171,887, filed on Feb. 9, 2021.
Application 17/171,887 is a continuation in part of application No. 17/160,286, filed on Jan. 27, 2021.
Claims priority of provisional application 63/100,545, filed on Mar. 17, 2020.
Claims priority of provisional application 62/995,948, filed on Feb. 20, 2020.
Claims priority of provisional application 62/995,674, filed on Feb. 7, 2020.
Claims priority of provisional application 62/995,484, filed on Jan. 29, 2020.
Int. Cl. G06F 11/30 (2006.01); G06N 5/04 (2023.01); G06Q 50/02 (2012.01); G06N 7/00 (2023.01); G06F 16/2457 (2019.01); G06F 16/29 (2019.01); G01N 33/24 (2006.01); G01N 27/04 (2006.01); G06N 20/20 (2019.01); G06Q 10/0637 (2023.01); G06Q 30/0601 (2023.01); G06N 5/01 (2023.01)
CPC G06N 5/04 (2013.01) [G01N 27/041 (2013.01); G01N 33/24 (2013.01); G06F 16/24578 (2019.01); G06F 16/29 (2019.01); G06N 7/00 (2013.01); G06N 20/20 (2019.01); G06Q 50/02 (2013.01); G01N 2033/245 (2013.01); G06N 5/01 (2023.01); G06Q 10/06375 (2013.01); G06Q 30/0631 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method for visualizing one or more crop response surfaces, the method comprising:
providing a geospatial database associated with a crop prediction engine, wherein the geospatial database receives a plurality of soil composition information for each of a plurality of plots of land;
accessing the plurality of soil composition information for each of the plurality of plots of land, in which the soil composition information includes at least one of a plurality of measured soil sample results, a plurality of environmental results, and a plurality of soil conductivity results;
identifying a plurality of covariates from the soil composition information having at least one feature matrix, in which the feature matrix includes an input feature-set of independent variables that affect the estimated output dependent variables;
generating a multi-dimensional covariate training data set from the plurality of covariates;
applying the multi-dimensional covariate training data set to a machine learning training model to generate at least one predictive crop-yield predictive model;
removing one or more covariates from the plurality of covariates;
ranking covariates having one or more feature set interaction;
determining a dominant crop-yield feature set interaction from the ranked covariates having one or more feature set interaction;
generating a crop response surface from the dominant crop-yield feature set interaction;
visualizing the crop response surface;
applying the crop response surface to a Generalized Additive Model (GAM) training model to generate a linear equation having one or more non-linear term; and
wherein the GAM training model is configured to predict an improved crop performance by predicting at least one of a chemical application, a nutrient application, and a seed-type application.