IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i5p1004-d1138106.html
   My bibliography  Save this article

Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing

Author

Listed:
  • Jiangtao Ji

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
    Longmen Laboratory, Luoyang 471000, China)

  • Nana Li

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Hongwei Cui

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Yuchao Li

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Xinbo Zhao

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Haolei Zhang

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Hao Ma

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, China
    Longmen Laboratory, Luoyang 471000, China)

Abstract

Rapid acquisition of chlorophyll content in maize leaves is of great significance for timely monitoring of maize plant health and guiding field management. In order to accurately detect the relative chlorophyll content of summer maize and study the responsiveness of vegetation indices to SPAD (soil and plant analyzer development) values of summer maize at different spatial vertical scales, this paper established a prediction model for SPAD values of summer maize leaves at different spatial scales based on UAV multispectral images. The experiment collected multispectral image data from summer maize at the jointing stage and selected eight vegetation indices. By using the sparrow search optimized kernel limit learning machine (SSA-KELM), the prediction models for canopy leaf (CL) SPAD CL and ear leaf (EL) SPAD EL were established, and a linear fitting analysis was conducted combining the measured SPAD CL values and SPAD EL values on the ground. The results showed that for SPAD CL , the R 2 of the linear fitting between the predicted values and measured values was 0.899, and the RMSE was 1.068. For SPAD EL , the R 2 of linear fitting between the predicted values and the measured values was 0.837, and the RMSE was 0.89. Compared with the model established by the partial least squares method (PLSR), it is found that the sparrow search optimized kernel limit learning machine (SSA-KELM) has more precise prediction results with better stability and adaptability for small sample prediction. The research results can provide technical support for remote sensing monitoring of the chlorophyll content of summer maize at different spatial scales.

Suggested Citation

  • Jiangtao Ji & Nana Li & Hongwei Cui & Yuchao Li & Xinbo Zhao & Haolei Zhang & Hao Ma, 2023. "Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing," Agriculture, MDPI, vol. 13(5), pages 1-15, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:1004-:d:1138106
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/5/1004/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/5/1004/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brewer, K. & Clulow, A. & Sibanda, M. & Gokool, S. & Naiken, V. & Mabhaudhi, Tafadzwanashe, 2022. "Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems," Papers published in Journals (Open Access), International Water Management Institute, pages 1-14(3):518.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Zongru Liu & Jiyu Li, 2023. "Application of Unmanned Aerial Vehicles in Precision Agriculture," Agriculture, MDPI, vol. 13(7), pages 1-4, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Colette de Villiers & Cilence Munghemezulu & Zinhle Mashaba-Munghemezulu & George J. Chirima & Solomon G. Tesfamichael, 2023. "Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    2. Shaeden Gokool & Maqsooda Mahomed & Richard Kunz & Alistair Clulow & Mbulisi Sibanda & Vivek Naiken & Kershani Chetty & Tafadzwanashe Mabhaudhi, 2023. "Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A Scoping Review and Bibliometric Analysis," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    3. Pradosh Kumar Parida & Eagan Somasundaram & Ramanujam Krishnan & Sengodan Radhamani & Uthandi Sivakumar & Ettiyagounder Parameswari & Rajagounder Raja & Silambiah Ramasamy Shri Rangasami & Sundapalaya, 2024. "Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize," Agriculture, MDPI, vol. 14(7), pages 1-20, 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:13:y:2023:i:5:p:1004-:d:1138106. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.