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A Lightweight Pest Detection Model for Drones Based on Transformer and Super-Resolution Sampling Techniques

Author

Listed:
  • Yuzhe Bai

    (China Agricultural University, Beijing 100083, China
    These authors contributed equally to this work.)

  • Fengjun Hou

    (China Agricultural University, Beijing 100083, China
    These authors contributed equally to this work.)

  • Xinyuan Fan

    (China Agricultural University, Beijing 100083, China)

  • Weifan Lin

    (China Agricultural University, Beijing 100083, China)

  • Jinghan Lu

    (China Agricultural University, Beijing 100083, China)

  • Junyu Zhou

    (China Agricultural University, Beijing 100083, China)

  • Dongchen Fan

    (School of Computer Science and Engineering, Beihang University, Beijing 100191, China)

  • Lin Li

    (China Agricultural University, Beijing 100083, China)

Abstract

With the widespread application of drone technology, the demand for pest detection and identification from low-resolution and noisy images captured with drones has been steadily increasing. In this study, a lightweight pest identification model based on Transformer and super-resolution sampling techniques is introduced, aiming to enhance identification accuracy under challenging conditions. The Transformer model was found to effectively capture spatial dependencies in images, while the super-resolution sampling technique was employed to restore image details for subsequent identification processes. The experimental results demonstrated that this approach exhibited significant advantages across various pest image datasets, achieving Precision, Recall, mAP, and FPS scores of 0.97, 0.95, 0.95, and 57, respectively. Especially in the presence of low resolution and noise, this method was capable of performing pest identification with high accuracy. Furthermore, an adaptive optimizer was incorporated to enhance model convergence and performance. Overall, this study offers an efficient and accurate method for pest detection and identification in practical applications, holding significant practical value.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1812-:d:1239768
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    References listed on IDEAS

    as
    1. Zijia Yang & Hailin Feng & Yaoping Ruan & Xiang Weng, 2023. "Tea Tree Pest Detection Algorithm Based on Improved Yolov7-Tiny," Agriculture, MDPI, vol. 13(5), pages 1-22, May.
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    4. Liangquan Jia & Tao Wang & Yi Chen & Ying Zang & Xiangge Li & Haojie Shi & Lu Gao, 2023. "MobileNet-CA-YOLO: An Improved YOLOv7 Based on the MobileNetV3 and Attention Mechanism for Rice Pests and Diseases Detection," Agriculture, MDPI, vol. 13(7), pages 1-18, June.
    5. Zahid Ullah & Najah Alsubaie & Mona Jamjoom & Samah H. Alajmani & Farrukh Saleem, 2023. "EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images," Agriculture, MDPI, vol. 13(3), pages 1-13, March.
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