IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i7p713-d1621656.html
   My bibliography  Save this article

Recognition of Cordyceps Based on Machine Vision and Deep Learning

Author

Listed:
  • Zihao Xia

    (School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
    Hebei Technology Innovation Center for Intelligent Production Line of Prefabricated Building Components, Zhangjiakou 075051, China)

  • Aimin Sun

    (School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
    Hebei Technology Innovation Center for Intelligent Production Line of Prefabricated Building Components, Zhangjiakou 075051, China)

  • Hangdong Hou

    (School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
    Hebei Technology Innovation Center for Intelligent Production Line of Prefabricated Building Components, Zhangjiakou 075051, China)

  • Qingfeng Song

    (School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
    Hebei Technology Innovation Center for Intelligent Production Line of Prefabricated Building Components, Zhangjiakou 075051, China)

  • Hongli Yang

    (School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
    Hebei Technology Innovation Center for Intelligent Production Line of Prefabricated Building Components, Zhangjiakou 075051, China)

  • Liyong Ma

    (School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
    Hebei Technology Innovation Center for Intelligent Production Line of Prefabricated Building Components, Zhangjiakou 075051, China)

  • Fang Dong

    (Light Alloy Research Institute, Central South University, Changsha 410083, China)

Abstract

In a natural environment, due to the small size of caterpillar fungus, its indistinct features, similar color to surrounding weeds and background, and overlapping instances of caterpillar fungus, identifying caterpillar fungus poses significant challenges. To address these issues, this paper proposes a new MRAA network, which consists of a feature fusion pyramid network (MRFPN) and the backbone network N-CSPDarknet53. MRFPN is used to solve the problem of weak features. In N-CSPDarknet53, the Da-Conv module is proposed to address the background and color interference problems in shallow feature maps. The MRAA network significantly improves accuracy, achieving an accuracy rate of 0.202 AP S for small-target recognition, which represents a 12% increase compared to the baseline of 0.180 AP S . Additionally, the model size is small (9.88 M), making it lightweight. It is easy to deploy in embedded devices, which greatly promotes the development and application of caterpillar fungus identification.

Suggested Citation

  • Zihao Xia & Aimin Sun & Hangdong Hou & Qingfeng Song & Hongli Yang & Liyong Ma & Fang Dong, 2025. "Recognition of Cordyceps Based on Machine Vision and Deep Learning," Agriculture, MDPI, vol. 15(7), pages 1-26, March.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:7:p:713-:d:1621656
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/7/713/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/7/713/
    Download Restriction: no
    ---><---

    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:15:y:2025:i:7:p:713-:d:1621656. 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.