A Hybrid Framework Combining U-Net, Ant Colony Optimization, and CNN for Rice Leaf Disease Classification under Class Imbalance

Accurate classification of rice plant diseases is essential for early intervention and precision agriculture. However, real-world datasets often suffer from complex backgrounds, high-dimensional features, and severe class imbalances, which compromise classification performance. This study proposes an integrated framework combining image segmentation using U-Net, feature selection via Ant Colony Optimization (ACO), hybrid sampling to handle class imbalance, and final classification using a Convolutional Neural Network (CNN). Segmentation isolates disease-affected areas, ACO optimizes feature subsets, and hybrid sampling balances class distribution using undersampling and SMOTE. The proposed method was tested on four rice leaf disease datasets—Brown Spot, Leaf Blast, Leaf Blight, and Leaf Scald—exhibiting significant class imbalance. Experimental results show that the proposed approach outperforms baseline models (SegNet, PspNet, and E-Net) across multiple metrics: Accuracy, IoU, Precision, and Recall. This indicates the framework’s robustness and potential for real-world deployment in precision agriculture. Future work will focus on model compression and real-time implementation in IoT systems.