A Comparative Machine Learning Framework for Location-Specific Maize Yield Prediction in the U.S. Corn Belt Using SVM, Random Forest, and Decision Tree Models: Integrating Agronomic, Genetic, and Spatial Factors
Prince Michael Akwabeng · Journal of Agriculture and Ecology Research International · 2026
This paper fills in the key gaps in the field of agricultural machine learning by comparing the performance of the Support Vector Machine (SVM), the Random Forest (RF) and the Decision Tree (DT) algorithms to predict the yield of maize in various locations in the U.S. Corn Belt....
Open access
Research Article
10.9734/jaeri/2026/v27i1733