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With the continuous development of smart agricultural technology, plant disease identification models are increasingly pursuing the goals of accuracy, efficiency, and light weight. Aiming at the problems of large number of parameters, high calculation cost and low accuracy in the current tomato disease recognition model, a lightweight network was proposed based on multi-scale feature fusion and coordinate attention mechanism (Multi-scale feature fusion and coordinate attention MobileNet, MCA-MobileNet) model. Totally ten types of tomato leaf images were collected, and Wasserstein generative adversarial networks (WGAN) based on Wasserstein distance for data enhancement was used, which solved the problem of insufficient and unbalanced sample data and improved the generalization ability of the model. On the basis of the original model MobileNet-V2, an improved multi-scale feature fusion module was introduced to extract features from feature maps of different scales to improve the adaptability of the model to different scales;the lightweight coordinate attention mechanism module (Coordinate attention, CA) embedded in the inverted residual structure, so that the model paid more attention to the disease characteristics in the leaves and improved the recognition accuracy of the disease types. The test results showed that the accuracy rate of MCA-MobileNet for identifying tomato leaf diseases reached 94.11%, which was 2.84 percentage points higher than that of the original model, and the number of parameters was only 1/6 of the original model. This method better balanced the recognition accuracy and calculation cost of the model, and provided ideas and technical support for field deployment and real-time detection of tomato leaf diseases.