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Ultra-lightweight tomatoes disease recognition method based on efficient attention mechanism in complex environment

文献类型： 外文期刊

作者： Sun, Wenbin 1 ; Xu, Zhilong 2 ; Xu, Kang 1 ; Ru, Lin 3 ; Yang, Ranbing 2 ; Wang, Rong 4 ; Xing, Jiejie 2 ;

作者机构： 1.Hainan Univ, Coll Informat & Commun Engn, Haikou, Peoples R China

2.Hainan Univ, Coll Mech & Elect Engn, Haikou, Peoples R China

3.Heilongjiang Bayi Agr Univ, Coll Civil Engn & Water Conservancy, Daqing, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China

关键词： plant disease identification; image classification; attention mechanism; deep separable convolution; deep learning

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 15 卷

页码：

收录情况： SCI

摘要： A variety of diseased leaves and background noise types are present in images of diseased tomatoes captured in real-world environments. However, existing tomato leaf disease recognition models are limited to recognizing only a single leaf, rendering them unsuitable for practical applications in real-world scenarios. Additionally, these models consume significant hardware resources, making their implementation challenging for agricultural production and promotion. To address these issues, this study proposes a framework that integrates tomato leaf detection with leaf disease recognition. This framework includes a leaf detection model designed for diverse and complex environments, along with an ultra-lightweight model for recognizing tomato leaf diseases. To minimize hardware resource consumption, we developed five inverted residual modules coupled with an efficient attention mechanism, resulting in an ultra-lightweight recognition model that effectively balances model complexity and accuracy. The proposed network was trained on a dataset collected from real environments, and 14 contrasting experiments were conducted under varying noise conditions. The results indicate that the accuracy of the ultra-lightweight tomato disease recognition model, which utilizes the efficient attention mechanism, is 97.84%, with only 0.418 million parameters. Compared to traditional image recognition models, the model presented in this study not only achieves enhanced recognition accuracy across 14 noisy environments but also significantly reduces the number of required model parameters, thereby overcoming the limitation of existing models that can only recognize single disease images.

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