Literature DB >> 33971438

Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis.

Ruomei Zhao1, Lulu An1, Di Song1, Minzan Li2, Lang Qiao1, Ning Liu3, Hong Sun4.   

Abstract

The tuber development and nutrient transportation of potato crops are closely related to canopy photosynthesis dynamics. Chlorophyll fluorescence parameters of photosystem II, especially the maximum quantum yield of primary photochemistry (Fv/Fm), are intrinsic indicators for plant photosynthesis. Rapid detection of Fv/Fm of leaves by spectroscopy method instead of time-consuming pulse amplitude modulation technique could help to indicate potato development dynamics and guide field management. Accordingly, this study aims to extract fluorescence signals from hyperspectral reflectance to detect Fv/Fm. Hyperspectral imaging system and closed chlorophyll fluorescence imaging system were applied to collect the spectral data and values of Fv/Fm of 176 samples. The spectral data were decomposed by continuous wavelet transform (CWT) to obtain wavelet coefficients (WFs). Three mother wavelet functions including second derivative of Gaussian (gaus2), biorthogonal 3.3 (bior3.3) and reverse biorthogonal 3.3 (rbio3.3) were compared and the bior3.3 showed the best correlation with Fv/Fm. Two variable selection algorithms were used to select sensitive WFs of Fv/Fm including Monte Carlo uninformative variables elimination (MC-UVE) algorithm and random frog (RF) algorithm. Then the partial least squares (PLS) regression was used to establish detection models, which were labeled as bior3.3-MC-UVE-PLS and bior3.3-RF-PLS, respectively. The determination coefficients of prediction set of bior3.3-MC-UVE-PLS and bior3.3-RF-PLS were 0.8071 and 0.8218, respectively, and the root mean square errors of prediction set were 0.0181 and 0.0174, respectively. The bior3.3-RF-PLS had the best detection performance and the corresponding WFs were mainly distributed in the bands affected by fluorescence emission (650-800 nm), chlorophyll absorption and reflection. Overall, this study demonstrated the potential of CWT in fluorescence signals extraction and can serve as a guide in the quick detection of chlorophyll fluorescence parameters.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Chlorophyll fluorescence; Continuous wavelet transform; Hyperspectral imaging; Potato leaf; Random frog algorithm

Year:  2021        PMID: 33971438     DOI: 10.1016/j.saa.2021.119768

Source DB:  PubMed          Journal:  Spectrochim Acta A Mol Biomol Spectrosc        ISSN: 1386-1425            Impact factor:   4.098


  2 in total

1.  Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features.

Authors:  Qiushuang Yao; Ze Zhang; Xin Lv; Xiangyu Chen; Lulu Ma; Cong Sun
Journal:  Front Plant Sci       Date:  2022-07-13       Impact factor: 6.627

2.  Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging.

Authors:  Lukáš Krauz; Petr Páta; Jan Kaiser
Journal:  Sensors (Basel)       Date:  2022-01-13       Impact factor: 3.576

  2 in total

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