IDEAS home Printed from https://ideas.repec.org/a/axf/aidtaa/v2y2025i1p178-185.html

Research on Hyperspectral Imaging Detection Method of Nitrogen in Facility-Grown Lettuce

Author

Listed:
  • Zhang, Yixue
  • Zhi, Jingbo
  • Zheng, Jialiang
  • Li, Zhaowei
  • Li, Tiezhu

Abstract

Rapid non-destructive detection of crop nutrition serves as a crucial basis for water-fertilizer management and environmental regulation. This paper proposes a rapid nitrogen detection method for lettuce based on hyperspectral imaging technology. Hyperspectral image data of lettuce samples were acquired, processed using the SG smoothing algorithm, and analyzed with the RF algorithm to extract nitrogen-specific wavelengths. Finally, a KELM model under the RBF-Kernel function was established to predict lettuce nitrogen content. Results demonstrate that the KELM prediction model based on RF feature extraction achieves excellent performance, with an R 2 value exceeding 0.95 and RMSE below 0.27. This method provides scientific support for water and fertilizer irrigation decisions based on crop nitrogen requirements.

Suggested Citation

  • Zhang, Yixue & Zhi, Jingbo & Zheng, Jialiang & Li, Zhaowei & Li, Tiezhu, 2025. "Research on Hyperspectral Imaging Detection Method of Nitrogen in Facility-Grown Lettuce," Artificial Intelligence and Digital Technology, Scientific Open Access Publishing, vol. 2(1), pages 178-185.
    • Handle: RePEc:axf:aidtaa:v:2:y:2025:i:1:p:178-185
    as

    Download full text from publisher

    File URL: https://soapubs.com/index.php/AIDT/article/view/1333/1216
    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:axf:aidtaa:v:2:y:2025:i:1:p:178-185. 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: Yuchi Liu (email available below). General contact details of provider: https://soapubs.com/index.php/ICSS .

    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.