DIEC-ViT: Discriminative information enhanced contrastive vision transformer for the identification of plant diseases in complex environments

文献类型： 外文期刊

第一作者： Lin, Jianwu

作者： Lin, Jianwu;Qin, Yongbin;Zhang, Xin;Lin, Jianwu;Qin, Yongbin;Zhang, Xin;Lin, Jianwu;Lou, Lunhong;You, Lin;Zhang, Xin;Lin, Jianwu;Chen, Xiaoyulong;Lin, Jianwu;Chen, Xiaoyulong;Lou, Lunhong;You, Lin;Zhang, Xin;Cernava, Tomislav;Huang, Dahui

作者机构：

关键词： Plant disease recognition; Vision transformers; Discriminative information enhancement; modules; Contrastive learning

期刊名称：EXPERT SYSTEMS WITH APPLICATIONS （ 影响因子：7.5； 五年影响因子：7.8 ）

ISSN： 0957-4174

年卷期： 2025 年 281 卷

页码：

收录情况： SCI

摘要： Recently, vision transformer (ViT)-based methods have made breakthroughs on plant disease recognition tasks and have surpassed convolutional neural network (CNN)-based methods. They are now considered the state-ofthe-art for such methods. However, ViT-based methods usually encode and decode images through global modeling, which introduces a large amount of noise information when dealing with plant disease images in complex environments. In addition, plant disease images in complex environments have significant intra- and inter-class differences, further limiting the performance of ViT-based methods. To address the above limitations, we propose the discriminative information enhanced contrastive vision transformer, in short DIEC-ViT, for plant disease recognition in complex environments. DIEC-ViT contains two key modules, namely, the discriminative information enhancement (DIE) module and the contrastive learning (CL) module. Specifically, the DIE module enhances the perception of discriminative regions of the ViT and suppresses complex backgrounds by counting multi-head self-attention for multi-levels of class tokens. To cope with the problem of intra- and inter-class differences in plant disease images, the CL module is introduced into the ViT to optimize the feature space by reducing the distance between positive pairs and increasing the distance between negative pairs. Extensive experiments verify the effectiveness of the two modules. In addition, DEIC-ViT outperforms state-of-the-art methods with three field plant disease datasets. The obtained results indicate the potential of our approach to drive further development of ViT in the field of plant disease monitoring.

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