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In order to realize the lightweight identification and detection of rice diseases, the ECA attention mechanism was used to improve the MobileNetV3Small model, and shared parameter transfer learning was used to carry out intelligent lightweight identification and detection of rice diseases. Pre-training was performed on the PlantVillage dataset, and the shared parameters obtained from the pre-training were transferred to the rice disease recognition model for fine-tuning and optimization. Experiments were on the open-source rice disease dataset. The experimental results showed that the recognition accuracy rate reached 97.47% under non-transfer learning, and 99.92% under transfer learning, while reducing the number of parameters by 26.69%. Secondly, the Grad-CAM was used for visualization. Compared with other attention mechanisms CBAM and SENET, the results generated by the ECA module were more consistent with the position and color of the disease spots in the image, indicating that the network can better focus on rice diseases. Characteristics, and the causes of misclassification were analyzed through visualization and each rice disease. The proposed method realized the lightweight of the rice disease recognition model, so that it can be deployed in resource-constrained scenarios such as mobile devices, and achieved the purpose of fast, efficient and portable. At the same time, an Android-based rice disease identification system was developed, which can facilitate the identification and analysis of rice diseases at the edge.