A deep learning model for accurate tomato leaf disease identification

Recent advances in machine learning and deep learning have greatly improved how we detect plant diseases, making diagnoses more accurate, faster, and easier to scale. However, many existing solutions depend on large, pretrained models that need powerful hardware, which limits their use in the field, especially in areas with limited resources. To tackle this, we designed a custom lightweight convolutional neural network (CNN) built from scratch using 20,000 carefully selected images from the PlantVillage tomato dataset. Our model uses Squeeze-and-Excitation (SE) blocks and Swish activation functions to boost performance, reaching an accuracy of 97.7% while using far fewer computing resources than many standard models. We applied strong image preprocessing steps like resizing, normalization, and various augmentations, along with regularization methods such as dropout and weight decay to reduce overfitting. Compared to popular models, our network performs better than object detection model (91.9% accuracy) and is nearly on par with the transformer-based model (98.0% accuracy), but is far more practical for mobile devices and edge applications. In the future, we plan to add features for grading the severity of diseases, create an easy-to-use interface, and expand our model to cover more crops.