IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i3p490-d1358847.html
   My bibliography  Save this article

Enhancing Fruit Fly Detection in Complex Backgrounds Using Transformer Architecture with Step Attention Mechanism

Author

Listed:
  • Lexin Zhang

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

  • Kuiheng Chen

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

  • Liping Zheng

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

  • Xuwei Liao

    (China Agricultural University, Beijing 100083, China)

  • Feiyu Lu

    (China Agricultural University, Beijing 100083, China)

  • Yilun Li

    (China Agricultural University, Beijing 100083, China)

  • Yuzhuo Cui

    (China Agricultural University, Beijing 100083, China)

  • Yaze Wu

    (China Agricultural University, Beijing 100083, China)

  • Yihong Song

    (China Agricultural University, Beijing 100083, China
    Tsinghua University, Beijing 100083, China)

  • Shuo Yan

    (China Agricultural University, Beijing 100083, China)

Abstract

This study introduces a novel high-accuracy fruit fly detection model based on the Transformer structure, specifically aimed at addressing the unique challenges in fruit fly detection such as identification of small targets and accurate localization against complex backgrounds. By integrating a step attention mechanism and a cross-loss function, this model significantly enhances the recognition and localization of fruit flies within complex backgrounds, particularly improving the model’s effectiveness in handling small-sized targets and its adaptability under varying environmental conditions. Experimental results demonstrate that the model achieves a precision of 0.96, a recall rate of 0.95, an accuracy of 0.95, and an F1-score of 0.95 on the fruit fly detection task, significantly outperforming leading object detection models such as YOLOv8 and DETR. Specifically, this research delves into and optimizes for challenges faced in fruit fly detection, such as recognition issues under significant light variation, small target size, and complex backgrounds. Through ablation experiments comparing different data augmentation techniques and model configurations, the critical contributions of the step attention mechanism and cross-loss function to enhancing model performance under these complex conditions are further validated. These achievements not only highlight the innovativeness and effectiveness of the proposed method, but also provide robust technical support for solving practical fruit fly detection problems in real-world applications, paving new paths for future research in object detection technology.

Suggested Citation

  • Lexin Zhang & Kuiheng Chen & Liping Zheng & Xuwei Liao & Feiyu Lu & Yilun Li & Yuzhuo Cui & Yaze Wu & Yihong Song & Shuo Yan, 2024. "Enhancing Fruit Fly Detection in Complex Backgrounds Using Transformer Architecture with Step Attention Mechanism," Agriculture, MDPI, vol. 14(3), pages 1-27, March.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:3:p:490-:d:1358847
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/3/490/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/3/490/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beatrice W. Muriithi & Nancy G. Gathogo & Gracious M. Diiro & Samira A. Mohamed & Sunday Ekesi, 2020. "Potential Adoption of Integrated Pest Management Strategy for Suppression of Mango Fruit Flies in East Africa: An Ex Ante and Ex Post Analysis in Ethiopia and Kenya," Agriculture, MDPI, vol. 10(7), pages 1-23, July.
    Full references (including those not matched with items on IDEAS)

    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. Charity M. Wangithi & Beatrice W. Muriithi & Raphael Belmin, 2021. "Adoption and Dis-Adoption of Sustainable Agriculture: A Case of Farmers’ Innovations and Integrated Fruit Fly Management in Kenya," Agriculture, MDPI, vol. 11(4), pages 1-18, April.
    2. Fridah Chepchirchir & Beatrice W. Muriithi & Jackson Langat & Samira A. Mohamed & Shepard Ndlela & Fathiya M. Khamis, 2021. "Knowledge, Attitude, and Practices on Tomato Leaf Miner, Tuta absoluta on Tomato and Potential Demand for Integrated Pest Management among Smallholder Farmers in Kenya and Uganda," Agriculture, MDPI, vol. 11(12), pages 1-20, December.
    3. Samuel Jeff Otieno & Cecilia Nyawira Ritho & Jonathan Makau Nzuma & Beatrice Wambui Muriithi, 2023. "Determinants of Adoption and Dis-Adoption of Integrated Pest Management Practices in the Suppression of Mango Fruit Fly Infestation: Evidence from Embu County, Kenya," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    4. Saliou Niassy & Beatrice Murithii & Evanson R. Omuse & Emily Kimathi & Henri Tonnang & Shepard Ndlela & Samira Mohamed & Sunday Ekesi, 2022. "Insight on Fruit Fly IPM Technology Uptake and Barriers to Scaling in Africa," Sustainability, MDPI, vol. 14(5), pages 1-25, March.
    5. Hazem S. Kassem & Bader Alhafi Alotaibi & Ali Ahmed & Fahd O. Aldosri, 2020. "Sustainable Management of the Red Palm Weevil: The Nexus between Farmers’ Adoption of Integrated Pest Management and Their Knowledge of Symptoms," Sustainability, MDPI, vol. 12(22), pages 1-16, November.

    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:14:y:2024:i:3:p:490-:d:1358847. 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.