Lightweight Loquat Disease Identification Based on Improved MobileNetV3
In order to detect loquat diseases in time and further improve the accuracy of loquat disease rec-ognition,a lightweight loquat disease recognition algorithm is proposed on the basis of MobileNetV3.Firstly,PConv is used to replace DWConv in MobileNetV3 network to design a new block structure.Then the CBAM at-tention module is introduced to increase the feature expression ability from channel and space improve the model accuracy.Finally,the network structure is redesigned to obtain an improved MobileNetV3 model.Experiments show that the accuracy of the improved algorithm is 97.79%,the model parameters is 1.14M,and the detec-tion speed is 21.9 fps.This algorithm achieves the lightweight effect,which can quickly and accurately identify loquat diseases,and provides new technical support for the mobile implementation of loquat disease identifica-tion.
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