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Classification of Typical Pests and Diseases of Rice Based on the ECA Attention Mechanism

Author

Listed:
  • Hongjun Ni

    (School of Mechanical Engineering, Nantong University, Nantong 226019, China)

  • Zhiwei Shi

    (School of Mechanical Engineering, Nantong University, Nantong 226019, China)

  • Stephen Karungaru

    (Graduate School of Advanced Technology and Science, Tokushima University, Tokushima 770-8506, Japan)

  • Shuaishuai Lv

    (School of Mechanical Engineering, Nantong University, Nantong 226019, China)

  • Xiaoyuan Li

    (School of Mechanical Engineering, Nantong University, Nantong 226019, China)

  • Xingxing Wang

    (School of Mechanical Engineering, Nantong University, Nantong 226019, China)

  • Jiaqiao Zhang

    (School of Mechanical Engineering, Southeast University, Nanjing 211189, China)

Abstract

Rice, a staple food crop worldwide, is pivotal in agricultural productivity and public health. Automatic classification of typical rice pests and diseases is crucial for optimizing rice yield and quality in practical production. However, infrequent occurrences of specific pests and diseases lead to uneven dataset samples and similar early-stage symptoms, posing challenges for effective identification methods. In this study, we employ four image enhancement techniques—flipping, modifying saturation, modifying contrast, and adding blur—to balance dataset samples throughout the classification process. Simultaneously, we enhance the basic RepVGG model by incorporating the ECA attention mechanism within the Block and after the Head, resulting in the proposal of a new classification model, RepVGG_ECA . The model successfully classifies six categories: five types of typical pests and diseases, along with healthy rice plants, achieving a classification accuracy of 97.06%, outperforming ResNet34 , ResNeXt50 , Shufflenet V2 , and the basic RepVGG by 1.85%, 1.18%, 3.39%, and 1.09%, respectively. Furthermore, the ablation study demonstrates that optimal classification results are attained by integrating the ECA attention mechanism after the Head and within the Block of RepVGG . As a result, the classification method presented in this study provides a valuable reference for identifying typical rice pests and diseases.

Suggested Citation

  • Hongjun Ni & Zhiwei Shi & Stephen Karungaru & Shuaishuai Lv & Xiaoyuan Li & Xingxing Wang & Jiaqiao Zhang, 2023. "Classification of Typical Pests and Diseases of Rice Based on the ECA Attention Mechanism," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:1066-:d:1148243
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    References listed on IDEAS

    as
    1. Fulian Li & Wuwei Zhang, 2023. "Research on the Effect of Digital Economy on Agricultural Labor Force Employment and Its Relationship Using SEM and fsQCA Methods," Agriculture, MDPI, vol. 13(3), pages 1-17, February.
    2. Caifeng Tan & Jianping Tao & Lan Yi & Juan He & Qi Huang, 2022. "Dynamic Relationship between Agricultural Technology Progress, Agricultural Insurance and Farmers’ Income," Agriculture, MDPI, vol. 12(9), pages 1-17, August.
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    Cited by:

    1. Lin, Weiwen & Qin, Shan & Zhou, Xinzhu & Guan, Xin & Zeng, Yanzhao & Wang, Zeyu & Shen, Yaohan, 2024. "Three-dimensional quantitative mineral prediction from convolutional neural network model in developing intelligent cleaning technology," Resources Policy, Elsevier, vol. 88(C).

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