Plant Hyperspectral Imaging


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 computedā€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.

Key Concepts

  • Hyperspectral imaging is a powerful tool for analysing plants.
  • Both spatial and spectral information are acquired via hyperspectral imaging, generating chemically rich information with spatial distribution.
  • Most hyperspectral imaging techniques are nondestructive in nature and require minimal sample preparation.
  • Data are processed by computationalā€based methods, in particular, by using multivariate image analysis (MIA) techniques for feature extraction and classification.
  • Hyperspectral imaging is a versatile analytical technique with many applications in plant studies, such as textural analysis, chemical and biochemical analysis and disease identification.

Keywords: plant analysis; hyperspectral imaging; multispectral imaging; multivariate image analysis (MIA); feature extraction; classification; spectroscopy; spatial chemical information

Figure 1. Illustration of hyperspectral image.
Figure 2. (a) Illustration of a hyperspectral imaging system; (b) imaging acquisition unit.
Figure 3. Illustration of processing steps for analysing a hyperspectral image.
Figure 4. (a) Image of a kiwi fruit acquired via hyperspectral NIR analysis (Zhu et al., ); (b) surface deformation for a round‐shaped object affecting the light scattering (Pu et al., ).
Figure 5. Commelina communis (C. communis) cells exposed to 0, 100 and 200 ppm of calcium, accompanied by hyperspectral pseudo‐colour images acquired in different wavenumber ranges of the fingerprint region (900–1800 cm−1).


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Further Reading

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Su DW (2010) Hyperspectral Imaging for Food Quality Analysis and Control. Cambridge, UK: Academic Press.

Wang L and Zhao C (2016) Hyperspectral Image Processing. New York: Springer‐Verlag Berlin Heidelberg.

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Morais, Camilo LM, Butler, Holly J, McAinsh, Martin R, and Martin, Francis L(Mar 2019) Plant Hyperspectral Imaging. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0028367]