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

Jiangtao Ji, Nana Li, Hongwei Cui, Yuchao Li, Xinbo Zhao, Haolei Zhang and Hao Ma ()
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Jiangtao Ji: College of Agricultural Equipment Engineering, Henan University of Science and Technology, 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

Agriculture, 2023, vol. 13, issue 5, 1-15

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.

Keywords: multispectral remote sensing; vegetation index; SPAD values; SSA-KELM; PLSR (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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