Spectral correction and dimensionality reduction of hyperspectral images for plant water stress assessment

Lin, Jian Wen and Mohd Shahrimie Mohd Asaari, . and Dhondt, Stijn (2023) Spectral correction and dimensionality reduction of hyperspectral images for plant water stress assessment. Pertanika Journal of Science & Technology (Malaysia), 31 (4). 1827 -1845. ISSN 2231-8526

Hyperspectral Imaging (HSI) is one of the emerging techniques used in plant phenotyping as it carries abundant information and is non-invasive to plants. However, factors like illumination effect and high-dimensional spectral features need to be solved to attain higher accuracy of plant trait analysis. This research explored and analysed spectral normalisation and dimensionality reduction methods. The focus of this paper is twofold; the first objective was to explore the Standard Normal Variate (SNV), Least Absolute Deviations (L1) and Least Squares (L2) normalisation for spectral correction. The second objective was to explore the feasibility of Principal Component Analysis (PCA) and Analysis of Variance Fisher’s Test (ANOVA F-test) for spectral dimensionality reduction in spectral discriminative modelling. The analysis techniques were validated with HSI data of maise plants for early detection of water deficit stress response. Results showed that SNV performed the best among the three normalisation methods. Besides, ANOVA F-test outperformed PCA for the band selection method as it improved the trait assessment on the water deficit response of maise plants.

Full text available from: Official URL: http://www.pertanika.upm.edu.my/resources/files/Pe...

Abstract

Hyperspectral Imaging (HSI) is one of the emerging techniques used in plant phenotyping as it carries abundant information and is non-invasive to plants. However, factors like illumination effect and high-dimensional spectral features need to be solved to attain higher accuracy of plant trait analysis. This research explored and analysed spectral normalisation and dimensionality reduction methods. The focus of this paper is twofold; the first objective was to explore the Standard Normal Variate (SNV), Least Absolute Deviations (L1) and Least Squares (L2) normalisation for spectral correction. The second objective was to explore the feasibility of Principal Component Analysis (PCA) and Analysis of Variance Fisher’s Test (ANOVA F-test) for spectral dimensionality reduction in spectral discriminative modelling. The analysis techniques were validated with HSI data of maise plants for early detection of water deficit stress response. Results showed that SNV performed the best among the three normalisation methods. Besides, ANOVA F-test outperformed PCA for the band selection method as it improved the trait assessment on the water deficit response of maise plants.

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Item Type:	Article
AGROVOC Term:	phenotyping
AGROVOC Term:	tissue analysis
AGROVOC Term:	principal component analysis
AGROVOC Term:	image analysis
AGROVOC Term:	spectral analysis
AGROVOC Term:	research data
Geographical Term:	Malaysia
Uncontrolled Keywords:	Hyperspectral Imaging (HSI),
Depositing User:	Ms. Azariah Hashim
Date Deposited:	15 Jan 2025 03:15
Last Modified:	27 Jan 2025 02:45
URI:	http://webagris.upm.edu.my/id/eprint/1966

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