Estimating Equivalent Water Thickness of Tobacco Leaves Based on Water Hyperspectral Indices

doi:10.11924/j.issn.1000-6850.casb2023-0031

Abstract

Abstract:

In order to timely and accurately monitor the tobacco leaf moisture status, this study conducted water stress experiments on different flue-cured tobacco varieties for over two consecutive years (2020 and 2021). The spectral reflectance and leaf equivalent water thickness (EWT) of tobacco leaves were measured, and the water spectral indices based on EWT were screened out and used to construct a prediction model. The results showed that: (1) the leaf equivalent water thickness of different tobacco genotypes decreased with the reduction of irrigation amount. (2) The spectral reflectance of tobacco leaves under different moisture treatments varied regularly in the visible and near-infrared wavelength ranges. (3) The spectral sensitive regions of EWT were mainly concentrated in the visible region of 500-600 nm, the near-infrared region of 700-900 nm and 1000-1250 nm, and the short-wave infrared region of 1900-2000 nm. The optimal water spectral indices were NDWI (R₁₉₂₀, R₁₉₃₀) and SRWI (R₁₉₃₀, R₁₉₂₀), and the core bands for EWT were 1920 nm and 1930 nm. (4) The accuracy and stability result of ELM was the best in the different linear and nonlinear prediction models of EWT prediction models, with the model decision coefficient of 0.853, the validation decision coefficient of 0.855, and the RMSE of 0.004. This indicates that water spectral indices combined with nonlinear models can be utilized to predict the moisture content in tobacco leaves.

Key words: tobacco leaf, equivalent water thickness, water hyperspectral characteristic parameters, prediction model, extreme learning machine

JIA Fangfang, TENG Shihua, HE Lin, FU Anqi, CHEN Shuping, ZHAO Zhongyuan. Estimating Equivalent Water Thickness of Tobacco Leaves Based on Water Hyperspectral Indices[J]. Chinese Agricultural Science Bulletin, 2024, 40(1): 151-156.

Figures/Tables 4

References 25

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模型	建模P-R²	验证
模型	建模P-R²	T-R²	RMSE
SMLR	0.339^**	0.089	0.034
SVM	0.663^**	0.877^**	0.004
ELM	0.853^**	0.855^**	0.004
BP神经网络	0.786^**	0.805^**	0.005

模型	建模P-R²	验证
模型	建模P-R²	T-R²	RMSE
SMLR	0.339^**	0.089	0.034
SVM	0.663^**	0.877^**	0.004
ELM	0.853^**	0.855^**	0.004
BP神经网络	0.786^**	0.805^**	0.005