Enhancing yield prediction in maize breeding using UAV-derived RGB imagery: a novel classification-integrated regression approach

Accurate grain yield prediction is crucial for optimizing agricultural practices and ensuring food security. This study introduces a novel classification-integrated regression approach to improve maize yield prediction using UAV-derived RGB imagery. We compared three classifiers-Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)-to categorize yield data into low, medium, and high classes. Among these, SVM achieved the highest classification accuracy and was selected for classifying data prior to regression. Two methodologies were evaluated: Method 1 (direct RF regression on the full dataset) and Method 2 (SVM classification followed by class-specific RF regression). Multi-temporal vegetation indices (VIs) were analyzed across key growth stages, with the early vegetative phase yielding the lowest prediction errors. Method 2 significantly outperformed Method 1, reducing RMSE by 45.1% in calibration (0.28 t/ha vs. 0.51 t/ha) and 3.3% in validation (0.89 t/ha vs. 0.92 t/ha). This integrated framework demonstrates the advantage of combining classification and regression for precise yield estimation, providing a scalable tool for maize breeding programs. The results highlight the potential of UAV-based phenotyping to enhance agricultural productivity and support global food systems.