RLCB-Net: A Lightweight Attention-Based Network for Rice Leaf Disease Detection and Classification

This study proposes Hybrid RLCB-Net, a lightweight and computationally efficient deep learning model for rice leaf disease detection and classification. The model is evaluated using single-, dual-, and multi-source datasets and compared with established architectures, including ResNet50, EfficientNet-B0, MobileNetV2, SqueezeNet, and VGG16. A comprehensive evaluation is conducted using accuracy, precision, recall, F1-score, inference latency, throughput, model size, parameters, and GFLOPs. In addition, SHAP-based explainable AI is employed to interpret model predictions and identify influential disease-related regions. The proposed model demonstrates a strong balance between classification accuracy, computational efficiency, and inference speed, making it suitable for real-time rice disease detection in resource-constrained agricultural applications.