Enhancing yield prediction in maize breeding using UAV-derived RGB imagery: a novel classification-integrated regression approach - Ge, Haixiao; Zhang, Qi; Shen, Min; Qin, Yang; Wang, Lin; Yuan, Cansheng (Frontiers in Plant Science, 2025)
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.