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Cited by 210Remote Sensing in AgricultureSpectroscopy and Chemometric AnalysesSmart Agriculture and AI

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HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging

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Bergsträsser, Sergej, Fanourakis, Dimitrios, Schmittgen, Simone, Cendrero-Mateo, Maria, Jansen, Marcus, Scharr, Hanno, Rascher, Uwe·2015·Plant Methods
DOI 10.1186/s13007-015-0043-0PMC PMC4302522
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Abstract

BACKGROUND: Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device. RESULTS: The working principle of the HyperART system relies on the upward redirection of transmitted and reflected light (range of 400 to 2500 nm) of a plant sample towards two line scanners. By using both the reflectance and transmittance image, an image of leaf absorption can be calculated. The comparison with the dynamically high-resolution ASD FieldSpec data showed good correlation, underlying the accuracy of the HyperART system. Our experiments showed that variation in both leaf chlorophyll content of four different crop species, due to different fertilization regimes during growth, and fungal symptoms on sugar beet leaves could be accurately estimated and monitored. The use of leaf reflectance and transmittance, as well as their sum (by which the non-absorbed radiation is calculated) obtained by the HyperART system gave considerably improved results in classification of Cercospora leaf spot disease and determination of chlorophyll content. CONCLUSIONS: The HyperART system offers the possibility for non-invasive and accurate mapping of leaf transmittance and absorption, significantly expanding the applicability of reflectance, based on mapping spectroscopy, in plant sciences. Therefore, the HyperART system may be readily employed for non-invasive determination of the spatio-temporal dynamics of various plant properties.

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Figure 1 Two prototypes of the HyperART system with an image example. (a, b) Schematics of the hyperspectral absorption reflectance transmittance imaging (HyperART) system, employed for simultaneous recording of both reflectance (ρ) and transmittance (τ). In the first prototype (a) the camera is moving, whereas in the second one (b) the samples are moved. A, scan direction; B, line scanner; C, field of view; D, illumination source; E, light beam; F, reflected light; G, transmitted light; H, black painted metal sheets (to avoid direct illumination of the mirror from the light source and to reduce light scattering in the scanning process); I, mirrors; J, plant, of which leaves are fixed in the clip; K, clip, where leaf sample is placed; L, framework; M, slide bar. (c) Calculation of absorption (α) in the hyperspectral cube (acquired by the HyperART system), based on ρ and τ.
Figure 1 Two prototypes of the HyperART system with an image example. (a, b) Schematics of the hyperspectral absorption reflectance transmittance imaging (HyperART) system, employed for simultaneous recording of both reflectance (ρ) and transmittance (τ). In the first prototype (a) the camera is moving, whereas in the second one (b) the samples are moved. A, scan direction; B, line scanner; C, field of view; D, illumination source; E, light beam; F, reflected light; G, transmitted light; H, black painted metal sheets (to avoid direct illumination of the mirror from the light source and to reduce light scattering in the scanning process); I, mirrors; J, plant, of which leaves are fixed in the clip; K, clip, where leaf sample is placed; L, framework; M, slide bar. (c) Calculation of absorption (α) in the hyperspectral cube (acquired by the HyperART system), based on ρ and τ.CC BY · PubMed Central
Figure 2 Comparison of spectral signatures obtained by FluoWat and HyperART devices. (a) Reflectance (ρ) and transmittance (τ) spectra of sugar beet leaves acquired by using FluoWat or the hyperspectral absorption reflectance transmittance imaging (HyperART) system. Data are expressed as relative values. Dashed areas indicate SEM (n = 5). (b) Quotients of ρ, τ and sum of ρ with τ (i.e., ρ + τ) of spectral data acquired by using FluoWat and the HyperART system.
Figure 2 Comparison of spectral signatures obtained by FluoWat and HyperART devices. (a) Reflectance (ρ) and transmittance (τ) spectra of sugar beet leaves acquired by using FluoWat or the hyperspectral absorption reflectance transmittance imaging (HyperART) system. Data are expressed as relative values. Dashed areas indicate SEM (n = 5). (b) Quotients of ρ, τ and sum of ρ with τ (i.e., ρ + τ) of spectral data acquired by using FluoWat and the HyperART system.CC BY · PubMed Central
Figure 3 Values difference in normalized histograms of Cercospora Leaf Spot Index (CLSI) images. Normalized histograms (estimated probability functions PDF) of the calculated CLSI values based on (a) reflectance (ρ), (b) transmittance (τ) or (c) combination of ρ with τ (i.e., ρ + τ). Spectra were acquired by using the hyperspectral absorption, reflectance, transmittance (HyperART) imaging system. Measurements were conducted on three leaves of an infected sugar beet plant, and on three leaves of another non-infected (control) sugar beet plant. The two leaves of the former plant showed visual symptoms of infection, whereas the third one was symptom-free.
Figure 3 Values difference in normalized histograms of Cercospora Leaf Spot Index (CLSI) images. Normalized histograms (estimated probability functions PDF) of the calculated CLSI values based on (a) reflectance (ρ), (b) transmittance (τ) or (c) combination of ρ with τ (i.e., ρ + τ). Spectra were acquired by using the hyperspectral absorption, reflectance, transmittance (HyperART) imaging system. Measurements were conducted on three leaves of an infected sugar beet plant, and on three leaves of another non-infected (control) sugar beet plant. The two leaves of the former plant showed visual symptoms of infection, whereas the third one was symptom-free.CC BY · PubMed Central
Figure 4 RGB, colour coded Cercospora Leaf Spot Index (CLSI) and total classification error images. Images were calculated from reflectance (ρ), transmittance (τ) and combination of ρ with τ (i.e., ρ + τ) of sugar beet leaves infected by Cercospora beticola. The classification was performed by supervised SVM (support vector machine) classification on hyperspectral images, and unsupervised K-Mean (on CLSI images) using two classes (visible symptoms and plat tissue without visible symptoms). Images were acquired by using the hyperspectral absorption reflectance transmittance imaging (HyperART) system. The rectangular panels in the images show the enlargement of red bordered infected leave surface.
Figure 4 RGB, colour coded Cercospora Leaf Spot Index (CLSI) and total classification error images. Images were calculated from reflectance (ρ), transmittance (τ) and combination of ρ with τ (i.e., ρ + τ) of sugar beet leaves infected by Cercospora beticola. The classification was performed by supervised SVM (support vector machine) classification on hyperspectral images, and unsupervised K-Mean (on CLSI images) using two classes (visible symptoms and plat tissue without visible symptoms). Images were acquired by using the hyperspectral absorption reflectance transmittance imaging (HyperART) system. The rectangular panels in the images show the enlargement of red bordered infected leave surface.CC BY · PubMed Central

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Metadata

Authors
Bergsträsser, S., Fanourakis, D., Schmittgen, S., Cendrero-Mateo, M., Jansen, M., Scharr, H., & Rascher, U.
Affiliations
Forschungszentrum Jülich (DE)
Corresponding author
Bergsträsser, Sergej
ORCID
Venue
Plant Methods
Publisher
Springer Science and Business Media LLC
Published
2015
Volume
11
Issue
1
Pages
1
ISSN
1746-4811 (ISSN-L)
DOI
10.1186/s13007-015-0043-0
PMC
PMC4302522
OpenAlex
W2160038364
Cited by
209
References
57
Citations by year
2026: 7 · 2025: 12 · 2024: 19 · 2023: 16 · 2022: 14 · 2021: 30 · 2020: 38 · 2019: 14 · 2018: 11 · 2017: 22 · 2016: 16 · 2015: 10
FWCI
17.83
Type
journal article
Language
en
Open access
Licence
Locations
  • Plant Methods (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • JuSER (Forschungszentrum Jülich) (submittedVersion, other-oa)
Funders
  • European Commission (284443)
  • Bundesministerium für Bildung und Forschung (315309)
  • European Regional Development Fund
Topics
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
  • Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords
hyperspectral imaging, leaf reflectance, leaf transmittance, leaf absorption, Cercospora beticola, chlorophyll content, plant disease severity, fertilization regimes, line scanner

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