ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
This article is part of the Research TopicSmart Plant Pest and Disease Detection Machinery and Technology: Innovations for Sustainable AgricultureView all 11 articles
ViTKAB: An Efficient Deep Learning Network for Cotton Leaf Disease Identification
Provisionally accepted
- 1Henan University of Urban Construction, Pingdingshan, China
- 2Chinese Academy of Agricultural Sciences, Beijing, China
- 3Jiangxi Agricultural University, Nanchang, China
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Abstract 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.
Keywords: Crop diseases, Cotton leaf, deep learning, vision Transformer, BiFormer
Received: 07 Oct 2025; Accepted: 18 Nov 2025.
Copyright: © 2025 Song, Xu, Yang, Xu and Cai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Laixiang Xu, xulaixiang@huuc.edu.cn
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