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Method for Classifying Apple Leaf Diseases Based on Dual Attention and Multi-Scale Feature Extraction

Author

Listed:
  • Jie Ding

    (Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
    School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Cheng Zhang

    (Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
    School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Xi Cheng

    (School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

  • Yi Yue

    (Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
    School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Guohua Fan

    (Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
    School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Yunzhi Wu

    (Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
    School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Youhua Zhang

    (Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Hefei 230036, China
    School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

Abstract

Image datasets acquired from orchards are commonly characterized by intricate backgrounds and an imbalanced distribution of disease categories, resulting in suboptimal recognition outcomes when attempting to identify apple leaf diseases. In this regard, we propose a novel apple leaf disease recognition model, named RFCA ResNet, equipped with a dual attention mechanism and multi-scale feature extraction capacity, to more effectively tackle these issues. The dual attention mechanism incorporated into RFCA ResNet is a potent tool for mitigating the detrimental effects of complex backdrops on recognition outcomes. Additionally, by utilizing the class balance technique in conjunction with focal loss, the adverse effects of an unbalanced dataset on classification accuracy can be effectively minimized. The RFB module enables us to expand the receptive field and achieve multi-scale feature extraction, both of which are critical for the superior performance of RFCA ResNet. Experimental results demonstrate that RFCA ResNet significantly outperforms the standard CNN network model, exhibiting marked improvements of 89.61%, 56.66%, 72.76%, and 58.77% in terms of accuracy rate, precision rate, recall rate, and F1 score, respectively. It is better than other approaches, performs well in generalization, and has some theoretical relevance and practical value.

Suggested Citation

  • Jie Ding & Cheng Zhang & Xi Cheng & Yi Yue & Guohua Fan & Yunzhi Wu & Youhua Zhang, 2023. "Method for Classifying Apple Leaf Diseases Based on Dual Attention and Multi-Scale Feature Extraction," Agriculture, MDPI, vol. 13(5), pages 1-19, April.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:940-:d:1132305
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    References listed on IDEAS

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    1. Bulent Tugrul & Elhoucine Elfatimi & Recep Eryigit, 2022. "Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review," Agriculture, MDPI, vol. 12(8), pages 1-21, August.
    2. Xiaopeng Li & Shuqin Li, 2022. "Transformer Help CNN See Better: A Lightweight Hybrid Apple Disease Identification Model Based on Transformers," Agriculture, MDPI, vol. 12(6), pages 1-16, June.
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    Cited by:

    1. Xiuguo Zou & Zheng Liu & Xiaochen Zhu & Wentian Zhang & Yan Qian & Yuhua Li, 2023. "Application of Vision Technology and Artificial Intelligence in Smart Farming," Agriculture, MDPI, vol. 13(11), pages 1-4, November.

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