Diseases affecting tomato leaves represent a major risk to worldwide agricultural output and overall food security. In this study, we propose a innovative, lightweight and efficient deep learning (DL) approach for the classification of tomato leaf disease. Our architecture integrates the MobileNetV3Small backbone to extract multi-level features from input images, while Squeeze-and-Excitation (SE) blocks strengthen the focus on channel-wise features. A key component of our model is the incorporation of a Transformer-based module, which is applied to the fused features to extract long-range spatial interactions and contextual relationships. This hybrid approach enables the model to better distinguish between complex disease patterns in categories. The experimental findings indicate that the proposed model attains a high classification accuracy of 99.02%. The model also exhibits fast convergence and strong generalization, making it highly applicable for real-time deployment and resource-constrained agricultural environments. This work contributes a powerful and efficient solution to intelligent plant disease monitoring in the field of precision agriculture.