IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i9p1812-d1239768.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/9/1812/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/9/1812/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Dana Čirjak & Ivan Aleksi & Darija Lemic & Ivana Pajač Živković, 2023. "EfficientDet-4 Deep Neural Network-Based Remote Monitoring of Codling Moth Population for Early Damage Detection in Apple Orchard," Agriculture, MDPI, vol. 13(5), pages 1-20, April.
    5. Xinyu Jia & Xueqin Jiang & Zhiyong Li & Jiong Mu & Yuchao Wang & Yupeng Niu, 2023. "Application of Deep Learning in Image Recognition of Citrus Pests," Agriculture, MDPI, vol. 13(5), pages 1-19, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaomei Gao & Gang Wang & Jiangtao Qi & Qingxia (Jenny) Wang & Meiqi Xiang & Kexin Song & Zihao Zhou, 2024. "Improved YOLO v7 for Sustainable Agriculture Significantly Improves Precision Rate for Chinese Cabbage ( Brassica pekinensis Rupr.) Seedling Belt (CCSB) Detection," Sustainability, MDPI, vol. 16(11), pages 1-20, June.
    2. Juanli Jing & Menglin Zhai & Shiqing Dou & Lin Wang & Binghai Lou & Jichi Yan & Shixin Yuan, 2024. "Optimizing the YOLOv7-Tiny Model with Multiple Strategies for Citrus Fruit Yield Estimation in Complex Scenarios," Agriculture, MDPI, vol. 14(2), pages 1-16, February.
    3. Jozsef Suto, 2023. "Hardware and Software Support for Insect Pest Management," Agriculture, MDPI, vol. 13(9), pages 1-2, September.
    4. Yaxin Wang & Xinyuan Liu & Fanzhen Wang & Dongyue Ren & Yang Li & Zhimin Mu & Shide Li & Yongcheng Jiang, 2023. "Self-Attention-Mechanism-Improved YoloX-S for Briquette Biofuels Object Detection," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
    5. Wenji Yang & Xiaoying Qiu, 2024. "A Novel Crop Pest Detection Model Based on YOLOv5," Agriculture, MDPI, vol. 14(2), pages 1-23, February.
    6. Shenghao Ye & Xinyu Xue & Shuning Si & Yang Xu & Feixiang Le & Longfei Cui & Yongkui Jin, 2023. "Design and Testing of an Elastic Comb Reciprocating a Soybean Plant-to-Plant Seedling Avoidance and Weeding Device," Agriculture, MDPI, vol. 13(11), pages 1-23, November.
    7. Obed Appiah & Kwame Oppong Hackman & Belko Abdoul Aziz Diallo & Kehinde O. Ogunjobi & Son Diakalia & Ouedraogo Valentin & Damoue Abdoul-Karim & Gaston Dabire, 2024. "PlanteSaine: An Artificial Intelligent Empowered Mobile Application for Pests and Disease Management for Maize, Tomato, and Onion Farmers in Burkina Faso," Agriculture, MDPI, vol. 14(8), pages 1-23, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1812-:d:1239768. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.