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

Shuangya Wen, Nan Shi, Junwei Lu (), Qianwen Gao, Wenrui Hu, Zhengdengyuan Cao, Jianxiang Lu, Huibin Yang and Zhiqiang Gao
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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
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

Agriculture, 2022, vol. 12, issue 8, 1-16

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.

Keywords: rice ( Oryza sativa L.); hyperspectral reflectance; vegetation index; chlorophyll fluorescence parameters; continuous wavelet transform; back propagation neural network (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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