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 |
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.
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