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ResViT-Rice: A Deep Learning Model Combining Residual Module and Transformer Encoder for Accurate Detection of Rice Diseases

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  • Yujia Zhang

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China)

  • Luteng Zhong

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China)

  • Yu Ding

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China)

  • Hongfeng Yu

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China)

  • Zhaoyu Zhai

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China)

Abstract

Rice is a staple food for over half of the global population, but it faces significant yield losses: up to 52% due to leaf blast disease and brown spot diseases, respectively. This study aimed at proposing a hybrid architecture, namely ResViT-Rice, by taking advantage of both CNN and transformer for accurate detection of leaf blast and brown spot diseases. We employed ResNet as the backbone network to establish a detection model and introduced the encoder component from the transformer architecture. The convolutional block attention module was also integrated to ResViT-Rice to further enhance the feature-extraction ability. We processed 1648 training and 104 testing images for two diseases and the healthy class. To verify the effectiveness of the proposed ResViT-Rice, we conducted comparative evaluation with popular deep learning models. The experimental result suggested that ResViT-Rice achieved promising results in the rice disease-detection task, with the highest accuracy reaching 0.9904. The corresponding precision, recall, and F1-score were all over 0.96, with an AUC of up to 0.9987, and the corresponding loss rate was 0.0042. In conclusion, the proposed ResViT-Rice can better extract features of different rice diseases, thereby providing a more accurate and robust classification output.

Suggested Citation

  • Yujia Zhang & Luteng Zhong & Yu Ding & Hongfeng Yu & Zhaoyu Zhai, 2023. "ResViT-Rice: A Deep Learning Model Combining Residual Module and Transformer Encoder for Accurate Detection of Rice Diseases," Agriculture, MDPI, vol. 13(6), pages 1-17, June.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1264-:d:1174138
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    References listed on IDEAS

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    1. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdulghafor & Ali A. Alwan & Yonis Gulzar, 2023. "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    2. Yichao Gao & Hetong Wang & Man Li & Wen-Hao Su, 2022. "Automatic Tandem Dual BlendMask Networks for Severity Assessment of Wheat Fusarium Head Blight," Agriculture, MDPI, vol. 12(9), pages 1-18, September.
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