IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i8p1197-d885230.html
   My bibliography  Save this article

Continuous Wavelet Transform and Back Propagation Neural Network for Condition Monitoring Chlorophyll Fluorescence Parameters Fv/Fm of Rice Leaves

Author

Listed:
  • Shuangya Wen

    (College of Agronomy, Hunan Agricultural University, Changsha 410128, China)

  • Nan Shi

    (College of Agronomy, Hunan Agricultural University, Changsha 410128, China)

  • Junwei Lu

    (College of Agronomy, Hunan Agricultural University, Changsha 410128, China
    Orient Science & Technology College, Hunan Agricultural University, Changsha 410128, China)

  • Qianwen Gao

    (College of Agronomy, Hunan Agricultural University, Changsha 410128, China)

  • Wenrui Hu

    (College of Agronomy, Hunan Agricultural University, Changsha 410128, China)

  • Zhengdengyuan Cao

    (College of Agronomy, Hunan Agricultural University, Changsha 410128, China)

  • Jianxiang Lu

    (College of Agronomy, Hunan Agricultural University, Changsha 410128, China)

  • Huibin Yang

    (College of Agronomy, Hunan Agricultural University, Changsha 410128, China)

  • Zhiqiang Gao

    (College of Agronomy, Hunan Agricultural University, Changsha 410128, China)

Abstract

The chlorophyll fluorescence parameter Fv/Fm (maximum photosynthetic efficiency of optical system II) is an intrinsic index for exploring plant photosynthesis. Hyperspectral remote sensing technology can be used for rapid nondestructive detection of chlorophyll fluorescence parameters. Existing studies show that there is a good correlation between the vegetation index and Fv/Fm. However, due to the limited hyperspectral information reflected by the vegetation index, the established model often cannot reach the ideal accuracy. Therefore, this study took rice as the research object and explored the internal relationship between chlorophyll fluorescence parameters and spectral reflectance by setting different fertilization treatments. Spectral sensitive information was extracted by vegetation index and continuous wavelet transform (CWT) to explore a more suitable method for Fv/Fm hyperspectral estimation at the rice leaf scale. Then a monitoring model of Fv/Fm in rice leaves was established by the back propagation neural network (BPNN) algorithm. The results showed that: (1) the accuracy of univariate models constructed by Fv/Fm inversion based on 10 commonly used vegetation indices constructed by traditional methods was low; (2) The correlation between leaf hyperspectral reflectance and Fv/Fm could be effectively improved by using CWT, and the accuracy of the univariate model constructed by using the best wavelet coefficients could reach the level of rough evaluation of Fv/Fm; (3) The effect of wavelet transform using different mother wavelet functions as the basis function was different, and bior3.3 function was the best; R 2 , RMSE and RPD of the BPNN model constructed by using the first 10 best wavelet coefficients decomposed by the bior3.3 was 0.823 6, 0.013 2 and 2.304 3. In conclusion, this study proves that CWT can effectively extract sensitive bands of rice leaves for Fv/Fm monitoring, providing a reference for the follow-up rapid and nondestructive monitoring of chlorophyll fluorescence.

Suggested Citation

  • Shuangya Wen & Nan Shi & Junwei Lu & Qianwen Gao & Wenrui Hu & Zhengdengyuan Cao & Jianxiang Lu & Huibin Yang & Zhiqiang Gao, 2022. "Continuous Wavelet Transform and Back Propagation Neural Network for Condition Monitoring Chlorophyll Fluorescence Parameters Fv/Fm of Rice Leaves," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1197-:d:885230
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/8/1197/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/8/1197/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Nita Yuniati & Kusumiyati Kusumiyati & Syariful Mubarok & Bambang Nurhadi, 2023. "Assessment of Biostimulant Derived from Moringa Leaf Extract on Growth, Physiology, Yield, and Quality of Green Chili Pepper," Sustainability, MDPI, vol. 15(9), pages 1-13, April.

    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:12:y:2022:i:8:p:1197-:d:885230. 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.