IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i6p961-d1418026.html
   My bibliography  Save this article

WH-DETR: An Efficient Network Architecture for Wheat Spike Detection in Complex Backgrounds

Author

Listed:
  • Zhenlin Yang

    (College of Computer Science and Mathematics, Central South University of Forestry and Technology, Changsha 410004, China)

  • Wanhong Yang

    (College of Computer Science and Mathematics, Central South University of Forestry and Technology, Changsha 410004, China)

  • Jizheng Yi

    (College of Advanced Interdisciplinary Studies, Central South University of Forestry and Technology, Changsha 410004, China)

  • Rong Liu

    (College of Advanced Interdisciplinary Studies, Central South University of Forestry and Technology, Changsha 410004, China)

Abstract

Wheat spike detection is crucial for estimating wheat yields and has a significant impact on the modernization of wheat cultivation and the advancement of precision agriculture. This study explores the application of the DETR (Detection Transformer) architecture in wheat spike detection, introducing a new perspective to this task. We propose a high-precision end-to-end network named WH-DETR, which is based on an enhanced RT-DETR architecture. Initially, we employ data augmentation techniques such as image rotation, scaling, and random occlusion on the GWHD2021 dataset to improve the model’s generalization across various scenarios. A lightweight feature pyramid, GS-BiFPN, is implemented in the network’s neck section to effectively extract the multi-scale features of wheat spikes in complex environments, such as those with occlusions, overlaps, and extreme lighting conditions. Additionally, the introduction of GSConv enhances the network precision while reducing the computational costs, thereby controlling the detection speed. Furthermore, the EIoU metric is integrated into the loss function, refined to better focus on partially occluded or overlapping spikes. The testing results on the dataset demonstrate that this method achieves an Average Precision (AP) of 95.7%, surpassing current state-of-the-art object detection methods in both precision and speed. These findings confirm that our approach more closely meets the practical requirements for wheat spike detection compared to existing methods.

Suggested Citation

  • Zhenlin Yang & Wanhong Yang & Jizheng Yi & Rong Liu, 2024. "WH-DETR: An Efficient Network Architecture for Wheat Spike Detection in Complex Backgrounds," Agriculture, MDPI, vol. 14(6), pages 1-22, June.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:6:p:961-:d:1418026
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/6/961/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/6/961/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Ivan Malashin & Vadim Tynchenko & Andrei Gantimurov & Vladimir Nelyub & Aleksei Borodulin & Yadviga Tynchenko, 2024. "Predicting Sustainable Crop Yields: Deep Learning and Explainable AI Tools," Sustainability, MDPI, vol. 16(21), pages 1-29, October.

    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:6:p:961-:d:1418026. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.