IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i15p1670-d1716006.html
   My bibliography  Save this article

Application of Non-Destructive Technology in Plant Disease Detection: Review

Author

Listed:
  • Yanping Wang

    (School of Electrical and Information Engineering, Jiangsu University, Zheniiang 212013, China)

  • Jun Sun

    (School of Electrical and Information Engineering, Jiangsu University, Zheniiang 212013, China)

  • Zhaoqi Wu

    (School of Electrical and Information Engineering, Jiangsu University, Zheniiang 212013, China)

  • Yilin Jia

    (School of Electrical and Information Engineering, Jiangsu University, Zheniiang 212013, China)

  • Chunxia Dai

    (School of Electrical and Information Engineering, Jiangsu University, Zheniiang 212013, China
    Jiangsu Province and Education Ministry Co-Sponsored Synergistic Innovation Center of Modern Agricultural Equipment, Zhenjiang 212013, China)

Abstract

In recent years, research on plant disease detection has combined artificial intelligence, hyperspectral imaging, unmanned aerial vehicle remote sensing, and other technologies, promoting the transformation of pest and disease control in smart agriculture towards digitalization and artificial intelligence. This review systematically elaborates on the research status of non-destructive detection techniques used for plant disease identification and detection, mainly introducing the following two types of methods: spectral technology and imaging technology. It also elaborates, in detail, on the principles and application examples of each technology and summarizes the advantages and disadvantages of these technologies. This review clearly indicates that non-destructive detection techniques can achieve plant disease and pest detection quickly, accurately, and without damage. In the future, integrating multiple non-destructive detection technologies, developing portable detection devices, and combining more efficient data processing methods will become the core development directions of this field.

Suggested Citation

  • Yanping Wang & Jun Sun & Zhaoqi Wu & Yilin Jia & Chunxia Dai, 2025. "Application of Non-Destructive Technology in Plant Disease Detection: Review," Agriculture, MDPI, vol. 15(15), pages 1-27, August.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:15:p:1670-:d:1716006
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/15/1670/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/15/1670/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:15:p:1670-:d:1716006. 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.