In response to the growing demand for early and accurate detection of rice leaf diseases, this study proposes an improved deep learning model based on MobileNetV3-Small, enhanced with an integrated ECA-CBAM attention module. This module combines Efficient Channel Attention ECA to model inter-channel dependencies and a modified spatial attention mechanism using dilated convolutions to capture broader contextual information without increasing computational complexity. The model is trained on a dataset of 10,407 manually labeled rice leaf images, employing selective fine-tuning and agriculture-specific data augmentation strategies. Experimental results show that the proposed ECA-CBAM-MobileNetV3-Small model achieves an accuracy of 95.05% and an F1-score of 94.62%, significantly outperforming both the baseline MobileNetV3-Small and the CBAM-only variant. These findings highlight the effectiveness of combining lightweight attention mechanisms with dilation-based enhancements for improving plant disease classification performance.