Soybean yield prediction using machine learning algorithms under a cover crop management system
Letícia Bernabé Santos, Donna Gentry, Alex Tryforos, Lisa M. Fultz, Jeffrey S. Beasley, Thanos Gentimis
Abstract
This research explores the predictive capabilities of random forests algorithm on datasets coming from standard experiments on crop management systems in soybeans. This is a secondary analysis of a dataset from a project evaluating the relationship of cover crop systems to soybean yield prediction. The purpose of this paper is to compare a random forest algorithm to standard statistical techniques such as linear regression on a clean information rich agronomic experiment. The main findings include an estimate of the hyperparameters for optimal predictions using random forests, a threshold for data for optimal results and a general description of comparison methodologies for AI based techniques.