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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
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    References listed on IDEAS

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    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.
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    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.

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