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