IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i12p1190-d687839.html
   My bibliography  Save this article

Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images

Author

Listed:
  • Lifa Fang

    (Shandong Academy of Agricultural Machinery Sciences, Jinan 250100, China
    College of Mechanical & Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China)

  • Yanqiang Wu

    (College of Mechanical & Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
    Shandong Agricultural Equipment Intelligent Engineering Laboratory, Tai’an 271018, China)

  • Yuhua Li

    (College of Mechanical & Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
    Shandong Agricultural Equipment Intelligent Engineering Laboratory, Tai’an 271018, China)

  • Hongen Guo

    (Shandong Academy of Agricultural Machinery Sciences, Jinan 250100, China)

  • Hua Zhang

    (Shandong Academy of Agricultural Machinery Sciences, Jinan 250100, China)

  • Xiaoyu Wang

    (Shandong Academy of Agricultural Machinery Sciences, Jinan 250100, China)

  • Rui Xi

    (College of Mechanical & Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
    Shandong Agricultural Equipment Intelligent Engineering Laboratory, Tai’an 271018, China)

  • Jialin Hou

    (Shandong Academy of Agricultural Machinery Sciences, Jinan 250100, China
    College of Mechanical & Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China)

Abstract

Consistent ginger shoot orientation helps to ensure consistent ginger emergence and meet shading requirements. YOLO v3 is used to recognize ginger images in response to the current ginger seeder’s difficulty in meeting the above agronomic problems. However, it is not suitable for direct application on edge computing devices due to its high computational cost. To make the network more compact and to address the problems of low detection accuracy and long inference time, this study proposes an improved YOLO v3 model, in which some redundant channels and network layers are pruned to achieve real-time determination of ginger shoots and seeds. The test results showed that the pruned model reduced its model size by 87.2% and improved the detection speed by 85%. Meanwhile, its mean average precision ( mAP ) reached 98.0% for ginger shoots and seeds, only 0.1% lower than the model before pruning. Moreover, after deploying the model to the Jetson Nano, the test results showed that its mAP was 97.94%, the recognition accuracy could reach 96.7%, and detection speed could reach 20 frames·s −1 . The results showed that the proposed method was feasible for real-time and accurate detection of ginger images, providing a solid foundation for automatic and accurate ginger seeding.

Suggested Citation

  • Lifa Fang & Yanqiang Wu & Yuhua Li & Hongen Guo & Hua Zhang & Xiaoyu Wang & Rui Xi & Jialin Hou, 2021. "Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images," Agriculture, MDPI, vol. 11(12), pages 1-18, November.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1190-:d:687839
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/12/1190/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/12/1190/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Peichao Cong & Hao Feng & Kunfeng Lv & Jiachao Zhou & Shanda Li, 2023. "MYOLO: A Lightweight Fresh Shiitake Mushroom Detection Model Based on YOLOv3," Agriculture, MDPI, vol. 13(2), pages 1-23, February.
    2. Wei Zhang & Youqiang Sun & He Huang & Haotian Pei & Jiajia Sheng & Po Yang, 2022. "Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism," Agriculture, MDPI, vol. 12(8), pages 1-19, 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:11:y:2021:i:12:p:1190-:d:687839. 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.