A Lightweight Mobile Model Based on Improved EfficientNet-Lite for Tea Plant Disease Recognition in Complex Backgrounds
This paper proposed a lightweight real-time detection model for tea diseases,EGNet,using EfficientNet-Lite0 as the back-bone network,introducing ECA attention mechanism and Ghost module,and optimizing parameters through AdaBelief optimization algorithm.The experimental results showed that the recognition accuracy on the self built tea disease dataset reaches 97.25%,which was better than other baseline models.At the same time,it also performed well on the Mini ImageNet and IP102 datasets,proving that the model has good robustness.On this basis,a tea disease recognition platform and mobile App have been further developed,which could perform offline and online detection on smartphones respectively,and have practical application value.
万方数据
维普
