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For the problems of low accuracy of rice disease identification methods and slow convergence of models, a highperformance lightweight rice disease identification model was proposed, referred to as coordinate attention (CA)-MobileNetV3. The training of the model was optimized by fine-tuning the migration learning strategy, and the convergence speed of the model was improved. Firstly, a ten species dataset was created, containing nine rice diseases and healthy rice leaves. Secondly, the CA module was also used to embed spatial coordinate information in the channel attention to improve the feature extraction and generalization ability of the model. In addition, the improved MobileNetV3 network was used as the feature extraction network and the SVM multi-classifier was added to improve the model accuracy. The experimental results showed that on the rice disease dataset constructed, the initial MobileNetV3 recognition accuracy was only 95.78% and the F1 score was 95.36%, and then the recognition accuracy and F1 score were improved to 96.73% and 96.56%, respectively, after adding the CA module, and then the SVM multiclassifier was added, and the recognition accuracy and F1 scores reached 97.12% and 97.04%, respectively, the number of parameters and the time taken were only 2.99×106 and 0.91s, which were significantly better than that of other models. The experimental results showed that the CA-MobileNetV3 rice disease recognition model proposed can effectively recognize rice leaf diseases and achieve a lightweight, high-performance and easy-to-deploy rice disease classification and recognition algorithm.