数字农科院2.0

ViTKAB: an efficient deep learning network for cotton leaf disease identification

文献类型： 外文期刊

作者： Xu, Laixiang;Song, Hongyun;Yang, Xiaodong;Xu, Peng;Cai, Zhaopeng

作者机构：

关键词： crop diseases;cotton leaf;deep learning;vision transformer;BiFormer

期刊名称： FRONTIERS IN PLANT SCIENCE

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCIE（2025版）

摘要： Introduction Cotton is a vital global economic crop and textile material, yet its yield and quality are threatened by leaf diseases such as brown spot, verticillium wilt, wheel spot, and fusarium wilt.Methods We propose ViTKAB, a cotton disease recognition model based on an enhanced Vision Transformer that integrates a Kolmogorov-Arnold network and a BiFormer module. The model optimizes the Vision Transformer architecture to improve inference speed, employs nonlinear feature representation to better capture complex disease characteristics, and incorporates sparse dynamic attention to enhance robustness and accuracy.Results Experiments show that ViTKAB achieves an average recognition accuracy of 98.05% across four cotton leaf diseases, outperforming models such as CoAtNet-7, CLIP, and PaLI.Conclusions This method offers valuable insights for advancing intelligent crop disease detection systems and exhibits strong potential for deployment on edge devices.

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