Identifying early tomato leaf diseases and pests is one of the key steps in preventing tomato diseases and pests and increasing yield. This paper is based on the improved ResNet50 to identify tomato leaf pests and diseases. Five different tomato pest datasets were created according to different pest and disease categories, and the data were preprocessed by data augmentation. Based on the original model ResNet50, the SE attention mechanism module is added to the network model structure to enable the model to identify the target to be detected more accurately. In addi- tion, in order to reduce the number of parameters of the model and realize a lighter model, the traditional convolution is replaced by deep separable convolution. In order to illustrate the effectiveness of the improved model, the perform- ance of the improved model on the tomato leaf pest dataset was analyzed, and it was compared with the traditional con- volutional neural networks ResNet50, AlexNet, VGG16, and GoogLeNet. The experimental results show that the im- proved model reduces the number of parameters by 37. 5% compared with the original model, and the accuracy reaches 97. 4%, and the accuracy rate is increased by 4. 4% compared with the original model. In summary, this model a- chieves a good balance between performance and parameter quantity, which provides a possibility for the subsequent deployment of tomato leaf pest identification system in the actual environment.