Fast Rice Plant Disease Recognition Based on Dual-Attention-Guided Lightweight Network

Chenrui Kang, Lin Jiao (), Kang Liu, Zhigui Liu and Rujing Wang
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Chenrui Kang: School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
Lin Jiao: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Kang Liu: Department of Aeronautical and Aviation Engineering, Hong Kong Polytechnic University, Hong Kong 999077, China
Zhigui Liu: School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
Rujing Wang: Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China

Agriculture, 2025, vol. 15, issue 16, 1-14

Abstract: The yield and quality of rice are severely affected by rice disease, which can result in crop failure. Early and precise identification of rice plant diseases enables timely action, minimizing potential economic losses. Deep convolutional neural networks (CNNs) have significantly advanced image classification accuracy by leveraging powerful feature extraction capabilities, outperforming traditional machine learning methods. In this work, we propose a dual attention-guided lightweight network for fast and precise recognition of rice diseases with small lesions and high similarity. First, to efficiently extract features while reducing computational redundancy, we incorporate FasterNet using partial convolution (PC-Conv). Furthermore, to enhance the network’s ability to capture fine-grained lesion details, we introduce a dual-attention mechanism that aggregates long-range contextual information in both spatial and channel dimensions. Additionally, we construct a large-scale rice disease dataset, named RD-6, which contains 2196 images across six categories, to support model training and evaluation. Finally, the proposed rice disease detection method is evaluated on the RD-6 dataset, demonstrating its superior performance over other state-of-the-art methods, especially in terms of recognition efficiency. For instance, the method achieves an average accuracy of 99.9%, recall of 99.8%, precision of 100%, specificity of 100%, and F1-score of 99.9%. Additionally, the proposed method has only 3.6 M parameters, demonstrating higher efficiency without sacrificing accuracy.

Keywords: rice; disease recognition; deep learning; partial convolution; lightweight network (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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