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An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture

Author

Listed:
  • Sen Lin

    (College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Yucheng Xiu

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Jianlei Kong

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Chengcai Yang

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Chunjiang Zhao

    (National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
    Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Abstract

In modern agriculture and environmental protection, effective identification of crop diseases and pests is very important for intelligent management systems and mobile computing application. However, the existing identification mainly relies on machine learning and deep learning networks to carry out coarse-grained classification of large-scale parameters and complex structure fitting, which lacks the ability in identifying fine-grained features and inherent correlation to mine pests. To solve existing problems, a fine-grained pest identification method based on a graph pyramid attention, convolutional neural network (GPA-Net) is proposed to promote agricultural production efficiency. Firstly, the CSP backbone network is constructed to obtain rich feature maps. Then, a cross-stage trilinear attention module is constructed to extract the abundant fine-grained features of discrimination portions of pest objects as much as possible. Moreover, a multilevel pyramid structure is designed to learn multiscale spatial features and graphic relations to enhance the ability to recognize pests and diseases. Finally, comparative experiments executed on the cassava leaf, AI Challenger, and IP102 pest datasets demonstrates that the proposed GPA-Net achieves better performance than existing models, with accuracy up to 99.0%, 97.0%, and 56.9%, respectively, which is more conducive to distinguish crop pests and diseases in applications for practical smart agriculture and environmental protection.

Suggested Citation

  • Sen Lin & Yucheng Xiu & Jianlei Kong & Chengcai Yang & Chunjiang Zhao, 2023. "An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture," Agriculture, MDPI, vol. 13(3), pages 1-20, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:567-:d:1081221
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    References listed on IDEAS

    as
    1. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Min Zuo & Qing-Chuan Zhang & Seng Lin, 2021. "Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse," Agriculture, MDPI, vol. 11(8), pages 1-25, August.
    2. Jianlei Kong & Hongxing Wang & Chengcai Yang & Xuebo Jin & Min Zuo & Xin Zhang, 2022. "A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition," Agriculture, MDPI, vol. 12(4), pages 1-30, March.
    3. Feng Qin & Dongxia Liu & Bingda Sun & Liu Ruan & Zhanhong Ma & Haiguang Wang, 2016. "Identification of Alfalfa Leaf Diseases Using Image Recognition Technology," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-26, December.
    4. Jinzhu Lu & Lijuan Tan & Huanyu Jiang, 2021. "Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification," Agriculture, MDPI, vol. 11(8), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

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