Helong Yu, Xianhe Cheng, Ziqing Li, Qi Cai, Chunguang Bi*
CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 711-738, 2022, DOI:10.32604/cmes.2022.020263
Abstract To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application
rate of deep learning recognition networks, a lightweight ResNet (LW-ResNet) model for apple disease recognition
is proposed. Based on the deep residual network (ResNet18), the multi-scale feature extraction layer is constructed
by group convolution to realize the compression model and improve the extraction ability of different sizes of
lesion features. By improving the identity mapping structure to reduce information loss. By introducing the efficient
channel attention module (ECANet) to suppress noise from a complex background. The experimental results show
that the average… More >
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