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

ABSTRACT: 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 vegetati

SUBMITTER: Ge H 

PROVIDER: S-EPMC11965364 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature