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

Authors

Letícia Bernabé Santos, Louisiana State University
Donna Gentry, Louisiana State University Agricultural Center
Alex Tryforos, Louisiana State University
Lisa Fultz, Louisiana State University
Jeffrey Beasley, Louisiana State University
Thanos Gentimis, Louisiana State University

Document Type

Article

Publication Date

8-1-2024

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.

Publication Source (Journal or Book title)

Smart Agricultural Technology

Recommended Citation

Santos, L., Gentry, D., Tryforos, A., Fultz, L., Beasley, J., & Gentimis, T. (2024). Soybean yield prediction using machine learning algorithms under a cover crop management system. Smart Agricultural Technology, 8 https://doi.org/10.1016/j.atech.2024.100442