Determination of full maturity in soybean using machine-learning models and UAV imagery

Abstract

Abstract This study proposes a machine-learning approach to predict soybean maturity using climatic variables and unmanned aerial vehicle (UAV) data, focusing on Brazilian high-altitude tropical conditions. The objective was to evaluate and compare different models for predicting days to full maturity, first using climatic variables exclusively and then incorporating the Green Leaf Index (GLI). Eight algorithms were initially tested, and the three best-performing models were re-evaluated with inclusion of the GLI. Model performance was assessed using R², mean absolute error (MAE), and root mean square error (RMSE). Random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNN) showed the highest accuracy, with RF outperforming the others. The inclusion of the GLI improved predictions by capturing the decline in canopy greenness. These findings demonstrate that combining machine learning, climatic data, and vegetation indices is an efficient, low-cost approach for predicting soybean maturity, supporting high-throughput phenotyping in breeding programs.