Soybean yield prediction using machine learning algorithms under a cover crop management system

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.

Keywords

• Random forests
• Machine learning
• Datapoint threshold
• Soybeans
• Yield prediction
• Cover crops