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FATDNet: A fusion adversarial network for tomato leaf disease segmentation under complex backgrounds

文献类型： 外文期刊

作者： Yang, Zaichun 1 ; Sun, Lixiang 1 ; Liu, Zhihuan 1 ; Deng, Jinsheng 1 ; Zhang, Liangji 1 ; Huang, Hongxu 1 ; Zhou, Guoxiong 1 ; Hu, Yahui 2 ; Li, Liujun 3 ;

作者机构： 1.Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China

2.Hunan Acad Agr Sci, Inst Plant Protect, Changsha 410125, Hunan, Peoples R China

3.Univ Idaho, Dept Soil & Water Syst, Moscow, ID 83844 USA

关键词： Tomato leaf disease segmentation; Fusion adversarial network; Multi-dimensional attention; Gaussian weighted algorithm

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 234 卷

页码：

收录情况： SCI

摘要： Tomatoes are an important global crop, and automating the segmentation of leaf diseases is essential for agricultural security. Effective segmentation of these diseases is vital for timely intervention, which can significantly enhance crop yield and reduce pesticide usage. However, challenges such as background interference, tiny diseases, and blurred disease edges pose immense obstacles to the segmentation of tomato leaf diseases. To address these issues effectively, we propose a fusion adversarial segmentation network for tomato disease segmentation named FATDNet. Firstly, to eliminate background interference effectively, we introduce a dual-path fusion adversarial algorithm (DFAA). This algorithm employs parallel dual-path convolution to extract features of leaf disease regions, merging and adversarially processing complex background noise and disease features. Secondly, to enhance the feature extraction of small lesion regions, we employ a multi-dimensional attention mechanism (MDAM). This mechanism allocates weights in both horizontal and vertical directions, subsequently calculating weights in different channels of the feature map. This enhances the dispersion of semantic information through the adoption of diverse weight calculation strategies. Furthermore, to improve the model's ability to extract features at the edges of leaf diseases, we introduce a Gaussian weighted edge segmentation module (GWESM). This module calculates weight distribution through a Gaussian-weighted function, guiding the model to highlight features at different scales and reduce information loss caused by pooling operations. To demonstrate the superiority of FATDNet, we conduct comparative experiments using a self-built dataset and a public dataset. Experimental results show that FATDNet outperforms nine state-of-the-art segmentation networks. This validates that FATDNet provides a reliable solution for the automated segmentation of tomato leaf diseases.

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