Identification of leaf diseases in field crops based on improved ShuffleNetV2

作者：Hanmi Zhou, Jiageng Chen, Xiaoli Niu, Zhiguang Dai, Long Qin, Linshuang Ma, Jichen Li, Yumin Su, Qi Wu · 发表于：Frontiers in Plant Science · 年份：2024 · DOI：10.3389/fpls.2024.1342123 · 被引用次数：29 · 研究领域：Smart Agriculture and AI、Plant Disease Management Techniques、Plant Pathogens and Fungal Diseases

Rapid and accurate identification and timely protection of crop disease is of great importance for ensuring crop yields. Aiming at the problems of large model parameters of existing crop disease recognition methods and low recognition accuracy in the complex background of the field, we propose a lightweight crop leaf disease recognition model based on improved ShuffleNetV2. First, the repetition number and the number of output channels of the basic module of the ShuffleNetV2 model are redesigned to reduce the model parameters to make the model more lightweight while ensuring the accuracy of the model. Second, the residual structure is introduced in the basic feature extraction module to solve the gradient vanishing problem and enable the model to learn more complex feature representations. Then, parallel paths were added to the mechanism of the efficient channel attention (ECA) module, and the weights of different paths were adaptively updated by learnable parameters, and then the efficient dual channel attention (EDCA) module was proposed, which was embedded into the ShuffleNetV2 to improve the cross-channel interaction capability of the model. Finally, a multi-scale shallow feature extraction module and a multi-scale deep feature extraction module were introduced to improve the model's ability to extract lesions at different scales. Based on the above improvements, a lightweight crop leaf disease recognition model REM-ShuffleNetV2 was proposed. Experiments results show that...