Efficient Triple Attention and AttentionMix: A Novel Network for Fine-Grained Crop Disease Classification

Yanqi Zhang, Ning Zhang, Jingbo Zhu, Tan Sun, Xiujuan Chai () and Wei Dong ()
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Yanqi Zhang: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Ning Zhang: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Jingbo Zhu: Agricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230001, China
Tan Sun: Chinese Academy of Agricultural Sciences, Beijing 100081, China
Xiujuan Chai: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Wei Dong: Agricultural Economy and Information Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230001, China

Agriculture, 2025, vol. 15, issue 3, 1-17

Abstract: In the face of global climate change, crop pests and diseases have emerged on a large scale, with diverse species lasting for long periods and exerting wide-ranging impacts. Identifying crop pests and diseases efficiently and accurately is crucial in enhancing crop yields. Nonetheless, the complexity and variety of scenarios render this a challenging task. In this paper, we propose a fine-grained crop disease classification network integrating the efficient triple attention (ETA) module and the AttentionMix data enhancement strategy. The ETA module is capable of capturing channel attention and spatial attention information more effectively, which contributes to enhancing the representational capacity of deep CNNs. Additionally, AttentionMix can effectively address the label misassignment issue in CutMix, a commonly used method for obtaining high-quality data samples. The ETA module and AttentionMix can work together on deep CNNs for greater performance gains. We conducted experiments on our self-constructed crop disease dataset and on the widely used IP102 plant pest and disease classification dataset. The results showed that the network, which combined the ETA module and AttentionMix, could reach an accuracy as high as 98.2% on our crop disease dataset. When it came to the IP102 dataset, this network achieved an accuracy of 78.7% and a recall of 70.2%. In comparison with advanced attention models such as ECANet and Triplet Attention, our proposed model exhibited an average performance improvement of 5.3% and 4.4%, respectively. All of this implies that the proposed method is both practical and applicable for classifying diseases in the majority of crop types. Based on classification results from the proposed network, an install-free WeChat mini program that enables real-time automated crop disease recognition by taking photos with a smartphone camera was developed. This study can provide an accurate and timely diagnosis of crop pests and diseases, thereby providing a solution reference for smart agriculture.

Keywords: crop pests and diseases; CNNs; channel attention; spatial attention; data augmentation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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