Abstract: Early diagnosis and treatment of tomato diseases can help to improve the yield of tomatoes. The combination of artificial intelligence and agricultural production can achieve real-time non-destructive detection of tomato diseases. In this study, a research method for tomato leaf disease classification and recognition based on deep learning is proposed. Five kinds of common diseases of tomato leaves are selected for experimentation. Improvements are made to the MobileNetV3 model, and the effects of different learning methods, activation functions, and optimization algorithms on the accuracy of the model are analyzed. The model is compared with MobileNetV3, VGG16, ResNet50, and InceptionV3, and the robustness of the model is evaluated by ten-fold cross-validation. The research shows that the model has good classification performance, achieving an average recognition accuracy of 97.29% for common tomato leaf disease images. The model is superior to other models in terms of model size, running time, and classification accuracy, providing a reference for the recognition of common tomato leaf diseases.