Tomatoes

as an important economic crop in China

suffer from frequent diseases that seriously affect yield and quality. Traditional disease identification methods rely on manual experience and have problems such as low efficiency and accuracy. This article proposes a deep learning based method for accurate recognition of tomato diseases

aiming to address the limitations of traditional methods. A tomato disease image dataset is constructed

covering common disease types and healthy leaf samples. Data augmentation processing is performed to improve the model's generalization ability. A suitable deep learning network is selected as the basic model

and the model is improved by adjusting the network structure and optimizing hyperparameters. The results show that the proposed method can quickly and accurately identify common tomato diseases

with higher recognition accuracy than traditional methods

providing technical support for early warning and precise prevention of tomato diseases.