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Plant Hyperspectral Imaging

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Plant Hyperspectral Imaging. / Morais, Camilo; Butler, Holly; McAinsh, Martin Robert et al.
eLS. Wiley, 2019. p. 1-12.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Entry for encyclopedia/dictionary

Harvard

Morais, C, Butler, H, McAinsh, MR & Martin, F 2019, Plant Hyperspectral Imaging. in eLS. Wiley, pp. 1-12. https://doi.org/10.1002/9780470015902.a0028367

APA

Morais, C., Butler, H., McAinsh, M. R., & Martin, F. (2019). Plant Hyperspectral Imaging. In eLS (pp. 1-12). Wiley. https://doi.org/10.1002/9780470015902.a0028367

Vancouver

Morais C, Butler H, McAinsh MR, Martin F. Plant Hyperspectral Imaging. In eLS. Wiley. 2019. p. 1-12 doi: 10.1002/9780470015902.a0028367

Author

Morais, Camilo ; Butler, Holly ; McAinsh, Martin Robert et al. / Plant Hyperspectral Imaging. eLS. Wiley, 2019. pp. 1-12

Bibtex

@inbook{44121bb8a7524375b7e38ed106aac314,
title = "Plant Hyperspectral Imaging",
abstract = "Hyperspectral imaging can generate spatial chemical information in plants. The imaging acquisition system is basically composed of a radiation source, sample stage, objective lens, spectrograph, CCD camera and a computer to store and process derived data. Most hyperspectral imaging acquisition approaches are nondestructive in nature and require minimum sample preparation, thus producing chemically rich information without modifying a sample{\textquoteright}s features. Data processing is mainly performed via multivariate image analysis (MIA), where computed-based methods are employed for preprocessing, feature extraction and multivariate analysis towards classification. Applications vary according to the desired information of interest, but they mainly include textural analysis, chemical and biochemical analysis and plant disease identification. Successful studies in these areas reinforce the sensitivity and versatility of hyperspectral imaging in plants.",
keywords = "Plant analysis, hyperspectral imaging, multispectral imaging, multivariate image analysis, feature extraction, classification, spectroscopy, spatial chemical information",
author = "Camilo Morais and Holly Butler and McAinsh, {Martin Robert} and Frank Martin",
year = "2019",
month = mar,
day = "20",
doi = "10.1002/9780470015902.a0028367",
language = "English",
pages = "1--12",
booktitle = "eLS",
publisher = "Wiley",

}

RIS

TY - CHAP

T1 - Plant Hyperspectral Imaging

AU - Morais, Camilo

AU - Butler, Holly

AU - McAinsh, Martin Robert

AU - Martin, Frank

PY - 2019/3/20

Y1 - 2019/3/20

N2 - Hyperspectral imaging can generate spatial chemical information in plants. The imaging acquisition system is basically composed of a radiation source, sample stage, objective lens, spectrograph, CCD camera and a computer to store and process derived data. Most hyperspectral imaging acquisition approaches are nondestructive in nature and require minimum sample preparation, thus producing chemically rich information without modifying a sample’s features. Data processing is mainly performed via multivariate image analysis (MIA), where computed-based methods are employed for preprocessing, feature extraction and multivariate analysis towards classification. Applications vary according to the desired information of interest, but they mainly include textural analysis, chemical and biochemical analysis and plant disease identification. Successful studies in these areas reinforce the sensitivity and versatility of hyperspectral imaging in plants.

AB - Hyperspectral imaging can generate spatial chemical information in plants. The imaging acquisition system is basically composed of a radiation source, sample stage, objective lens, spectrograph, CCD camera and a computer to store and process derived data. Most hyperspectral imaging acquisition approaches are nondestructive in nature and require minimum sample preparation, thus producing chemically rich information without modifying a sample’s features. Data processing is mainly performed via multivariate image analysis (MIA), where computed-based methods are employed for preprocessing, feature extraction and multivariate analysis towards classification. Applications vary according to the desired information of interest, but they mainly include textural analysis, chemical and biochemical analysis and plant disease identification. Successful studies in these areas reinforce the sensitivity and versatility of hyperspectral imaging in plants.

KW - Plant analysis

KW - hyperspectral imaging

KW - multispectral imaging

KW - multivariate image analysis

KW - feature extraction

KW - classification

KW - spectroscopy

KW - spatial chemical information

U2 - 10.1002/9780470015902.a0028367

DO - 10.1002/9780470015902.a0028367

M3 - Entry for encyclopedia/dictionary

SP - 1

EP - 12

BT - eLS

PB - Wiley

ER -