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PL-DINO: An Improved Transformer-Based Method for Plant Leaf Disease Detection

Author

Listed:
  • Wei Li

    (School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China)

  • Lizhou Zhu

    (College of Software Engineering, Southeast University, Suzhou 215123, China)

  • Jun Liu

    (Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China)

Abstract

Agriculture is important for ecology. The early detection and treatment of agricultural crop diseases are meaningful and challenging tasks in agriculture. Currently, the identification of plant diseases relies on manual detection, which has the disadvantages of long operation time and low efficiency, ultimately impacting the crop yield and quality. To overcome these disadvantages, we propose a new object detection method named “Plant Leaf Detection transformer with Improved deNoising anchOr boxes (PL-DINO)”. This method incorporates a Convolutional Block Attention Module (CBAM) into the ResNet50 backbone network. With the assistance of the CBAM block, the representative features can be effectively extracted from leaf images. Next, an EQualization Loss (EQL) is employed to address the problem of class imbalance in the relevant datasets. The proposed PL-DINO is evaluated using the publicly available PlantDoc dataset. Experimental results demonstrate the superiority of PL-DINO over the related advanced approaches. Specifically, PL-DINO achieves a mean average precision of 70.3%, surpassing conventional object detection algorithms such as Faster R-CNN and YOLOv7 for leaf disease detection in natural environments. In brief, PL-DINO offers a practical technology for smart agriculture and ecological monitoring.

Suggested Citation

  • Wei Li & Lizhou Zhu & Jun Liu, 2024. "PL-DINO: An Improved Transformer-Based Method for Plant Leaf Disease Detection," Agriculture, MDPI, vol. 14(5), pages 1-14, April.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:5:p:691-:d:1384917
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    References listed on IDEAS

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    1. Yuzhe Bai & Fengjun Hou & Xinyuan Fan & Weifan Lin & Jinghan Lu & Junyu Zhou & Dongchen Fan & Lin Li, 2023. "A Lightweight Pest Detection Model for Drones Based on Transformer and Super-Resolution Sampling Techniques," Agriculture, MDPI, vol. 13(9), pages 1-23, September.
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