文献类型：期刊文献

英文题名：ShuffleNetV2 SSM MLCA: a lightweight recognition network for wheat fungal diseases

作者：Shi, Yuxin Zeng, Cheng Chi, Nan

第一作者：Shi, Yuxin

通信作者：Zeng, C[1];Zeng, C[2];Zeng, C[3];Zeng, C[4]|[1444027956eab4b44a396]曾诚;[14440313afbb7664c3122]曾诚;

机构：[1]Guizhou Univ, Sch Math & Stat, Guiyang, Peoples R China;[2]Guizhou Inst Technol, Coll Sci, Guiyang, Peoples R China;[3]Guizhou Inst Technol, Coll Innovat & Entrepreneurship, Guiyang, Peoples R China;[4]Key Lab New Power Syst Operat Control Guizhou Prov, Guiyang, Peoples R China;[5]Special Key Lab Artificial Intelligence & Intellig, Guiyang, Peoples R China

第一机构：Guizhou Univ, Sch Math & Stat, Guiyang, Peoples R China

通信机构：corresponding author), Guizhou Inst Technol, Coll Sci, Guiyang, Peoples R China;corresponding author), Guizhou Inst Technol, Coll Innovat & Entrepreneurship, Guiyang, Peoples R China;corresponding author), Key Lab New Power Syst Operat Control Guizhou Prov, Guiyang, Peoples R China;corresponding author), Special Key Lab Artificial Intelligence & Intellig, Guiyang, Peoples R China.|贵州理工学院;贵州理工学院理学院;

年份：2026

卷号：17

外文期刊名：FRONTIERS IN PLANT SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001826809200001)】；

基金：The author(s) declared that financial support was received for this work and/or its publication. This work was supported in part by the National Natural Science Foundation of China under Grant 62163008 and 61763004, Qiankehe Platform ZSYS[2025]007, the Guizhou Provincial Science and Technology Projects [2020]1Z054, and PhD start-up fund of Guizhou Institute of Technology under Grant XJGC20150411.

语种：英文

外文关键词：lightweight model; wheat fungal diseases; ShuffleNetV2; SS-Conv-SSM; half convolution; mixed local channel attention

摘要：Introduction Wheat is one of the most widely planted staple crops worldwide and underpins global food security. Fungal diseases severely threaten wheat growth and trigger massive yield losses during cultivation. Traditional manual diagnosis is time-consuming and highly subjective, while existing deep learning models often struggle to achieve high accuracy and robustness in complex field environments. Accurate identification of these fungal diseases is therefore vital to secure grain production.Methods This paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification. First, the original basic blocks of ShuffleNetV2 are substituted with SS-Conv-SSM modules to strengthen the extraction of fine-grained lesion features amid visually analogous fungal disease samples; half convolution is embedded to cut down model computational overhead. Second, a Mixed Local Channel Attention (MLCA) unit is attached to the convolution branch of each SS-Conv-SSM module, which adaptively highlights discriminative disease features and filters irrelevant background noise. Standard training configurations and five-fold cross-validation are adopted for fair model evaluation.Results Comparative experiments reveal that the presented network reaches a classification accuracy of 91.35%, which surpasses the original ShuffleNetV2 baseline by 1.16 percentage points. Controlled ablation tests verify the independent performance gain of each core component: the SS-Conv-SSM module raises overall accuracy by 0.89%, and the MLCA mechanism brings an extra 0.27% accuracy increment.Discussion The proposed ShuffleNetV2_SSM_MLCA architecture strikes a favorable trade-off between model lightweight property and classification performance. It delivers a low-computation, high-precision recognition scheme for wheat fungal diseases and lays a solid technical foundation for real-time disease monitoring in intelligent agricultural scenarios.