The varieties of tomato leaf diseases are of small differences and are hard to distinguish with the naked eye. Given that the classical convolutional neural network is exposed to various problems, such as a large number of parameters, heavy computation burden, a low identification rate of the model, and a large prediction error, this study proposes a disease identification method based on an improved MobileNetV2 network. A channel and a spatial attention mechanism are added to the right network layer to enhance the ability of the network to specify the features of diseased leaves and reduce the interference of irrelevant features. The Ghost module is used to replace some of the inverted residual blocks in the original model to ensure the accuracy of the model and reduce the number of parameters. The LeakyReLU activation function is employed to retain more positive and negative feature information in the feature map and thereby enhance the robustness of the model. Ten tomato leaf diseases, including early blight, late blight, spot blight, bacterial canker, erythema tetranycariasis, leaf mildew, and bacterial spot, are selected from the public dataset PlantVillage to serve as the experimental dataset. The experimental results show that the classification accuracy of the improved MobileNetV2 network reaches 98.57%, which is 2.29% higher than that of the original MobileNetV2, and the model size is reduced by 22.52%, representing a remarkable optimization effect.