Agronomy (Jan 2023)

Estimating Chlorophyll Fluorescence Parameters of Rice (<i>Oryza sativa</i> L.) Based on Spectrum Transformation and a Joint Feature Extraction Algorithm

  • Shuangya Wen,
  • Nan Shi,
  • Junwei Lu,
  • Qianwen Gao,
  • Huibing Yang,
  • Zhiqiang Gao

DOI
https://doi.org/10.3390/agronomy13020337
Journal volume & issue
Vol. 13, no. 2
p. 337

Abstract

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The chlorophyll fluorescence parameter Fv/Fm plays a significant role in indicating the photosynthetic function of plants. The existing technical methods used to measure Fv/Fm are often inefficient and cumbersome. To realize fast and non-destructive monitoring of Fv/Fm, this study took rice under different fertilizer treatments and measured the hyperspectral reflectance information and Fv/Fm data of rice leaves during the whole growth period. Five spectral transformation methods were used to pre-process the spectral data. Then, spectral characteristic wavelengths were extracted by the correlation coefficient method (CC) combined with the competitive adaptative reweighted sampling (CARS) algorithm. Finally, based on the combination of characteristic wavelengths extracted from different spectral transformations, back propagation neural network (BPNN) models were constructed and evaluated. The results showed that: (1) first derivative transform (FD), multiplicative scatter correction (MSC) and standardized normal variation (SNV) methods could effectively highlight the correlation between spectral data and Fv/Fm. The most sensitive bands with high correlation coefficients were concentrated in the range of 650–850 nm, and the absolute values of the highest correlation coefficients were 0.84, 0.73, and 0.72, respectively. (2) The CC-CARS algorithm could effectively screen the characteristic wavelengths sensitive to Fv/Fm. The number of sensitive bands extracted by FD, MSC, and SNV pre-treatment methods were 14, 13, and 16 which only accounted for 2.33%, 2.16%, and 2.66% of the total spectral wavelength (the number of full spectral bands is 601), respectively. (3) The BPNN models were established based on the above sensitive wavelengths, and it was found that MSC-CC-CARS-BPNN had the highest prediction accuracy, and its testing set R2, RMSE and RPD were 0.74, 1.88% and 2.46, respectively. The results can provide technical references for hyperspectral data pre-processing and rapid and non-destructive monitoring of chlorophyll fluorescence parameters.

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