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Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range

Brugger, Anna ; Behmann, Jan ; Paulus, Stefan ; Luigs, Hans-Georg ; Kuska, Matheus Thomas ; Schramowski, Patrick ; Kersting, Kristian ; Steiner, Ulrike ; Mahlein, Anne-Katrin (2024)
Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range.
In: Remote Sensing, 2019, 11 (12)
doi: 10.26083/tuprints-00016743
Article, Secondary publication, Publisher's Version

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Abstract

Previous plant phenotyping studies have focused on the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm) range. The ultraviolet range (UV, 200–380 nm) has not yet been used in plant phenotyping even though a number of plant molecules like flavones and phenol feature absorption maxima in this range. In this study an imaging UV line scanner in the range of 250–430 nm is introduced to investigate crop plants for plant phenotyping. Observing plants in the UV-range can provide information about important changes of plant substances. To record reliable and reproducible time series results, measurement conditions were defined that exclude phototoxic effects of UV-illumination in the plant tissue. The measurement quality of the UV-camera has been assessed by comparing it to a non-imaging UV-spectrometer by measuring six different plant-based substances. Given the findings of these preliminary studies, an experiment has been defined and performed monitoring the stress response of barley leaves to salt stress. The aim was to visualize the effects of abiotic stress within the UV-range to provide new insights into the stress response of plants. Our study demonstrated the first use of a hyperspectral sensor in the UV-range for stress detection in plant phenotyping.

Item Type: Article
Erschienen: 2024
Creators: Brugger, Anna ; Behmann, Jan ; Paulus, Stefan ; Luigs, Hans-Georg ; Kuska, Matheus Thomas ; Schramowski, Patrick ; Kersting, Kristian ; Steiner, Ulrike ; Mahlein, Anne-Katrin
Type of entry: Secondary publication
Title: Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range
Language: English
Date: 16 January 2024
Place of Publication: Darmstadt
Year of primary publication: 2019
Place of primary publication: Basel
Publisher: MDPI
Journal or Publication Title: Remote Sensing
Volume of the journal: 11
Issue Number: 12
Collation: 11 Seiten
DOI: 10.26083/tuprints-00016743
URL / URN: https://tuprints.ulb.tu-darmstadt.de/16743
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

Previous plant phenotyping studies have focused on the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm) range. The ultraviolet range (UV, 200–380 nm) has not yet been used in plant phenotyping even though a number of plant molecules like flavones and phenol feature absorption maxima in this range. In this study an imaging UV line scanner in the range of 250–430 nm is introduced to investigate crop plants for plant phenotyping. Observing plants in the UV-range can provide information about important changes of plant substances. To record reliable and reproducible time series results, measurement conditions were defined that exclude phototoxic effects of UV-illumination in the plant tissue. The measurement quality of the UV-camera has been assessed by comparing it to a non-imaging UV-spectrometer by measuring six different plant-based substances. Given the findings of these preliminary studies, an experiment has been defined and performed monitoring the stress response of barley leaves to salt stress. The aim was to visualize the effects of abiotic stress within the UV-range to provide new insights into the stress response of plants. Our study demonstrated the first use of a hyperspectral sensor in the UV-range for stress detection in plant phenotyping.

Uncontrolled Keywords: plant substances, hyperspectral imaging, UV-range, abiotic stress
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-167432
Additional Information:

This article belongs to the Special Issue Advanced Imaging for Plant Phenotyping

Classification DDC: 000 Generalities, computers, information > 004 Computer science
600 Technology, medicine, applied sciences > 600 Technology
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Artificial Intelligence and Machine Learning
Zentrale Einrichtungen
Zentrale Einrichtungen > Centre for Cognitive Science (CCS)
Date Deposited: 16 Jan 2024 10:21
Last Modified: 18 Jan 2024 12:43
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