To solve the problem of poor classification effect of convolution neural network on tomato leaf type diseases, a tomato disease identification method based on MobileViT lightweight network was proposed in this paper. Firstly, feature fusion of input and global representation is deleted and local and global representation are fused to make local representation more closely related to global representation. Secondly, in order to avoid the large increase of parameters and FLOPS when the model is scaled, the 3×3 convolution layer is replaced by 1×1 convolution layer in the fusion block. Then, the residual structure of input and fusion blocks is added to optimize the deeper level in the network model. Finally, the model accuracy is further improved by replacing the ReLU6 activation function with the H-Swish activation function. The experimental results showed that the improved MobileViT model can well recognize tomato diseases, with an average recognition accuracy of 99.16%. Compared with other convolution neural network models, it has higher recognition accuracy.