基于边缘智能的茶 ? 病害识别

To achieve automatic identification of tea diseases on resource-limited edge devices, a deep learning model deployment method based on edge intelligence is proposed. Firstly, the automated model pruning （AMC） algorithm was used to prune the model of MobileNetV2 on the PlantVillage dataset. Then the model AMC-MobileNetV2 generated at a pruning rate of 90% was used to perform migration learning training on the self-built tea disease dataset. Finally, the obtained tea disease recognition model was deployed on the edge devices. The experimental results show that AMC-MobileNetV2 improves the recognition speed of the model on resource-limited edge devices with a 94.5% reduction in the number of model parameters and 93.4% reduction in storage volume compared with MobileNetV2, and the average accuracy of the recognition of eight tea diseases is as high as 97.42%. The results of this study can be applied to tea garden disease control robots.