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Spectral signatures in the UV range can be combined with secondary plant metabolites by deep learning to characterize barley–powdery mildew interaction

Brugger, Anna ; Schramowski, Patrick ; Paulus, Stefan ; Steiner, Ulrike ; Kersting, Kristian ; Mahlein, Anne‐Katrin (2021)
Spectral signatures in the UV range can be combined with secondary plant metabolites by deep learning to characterize barley–powdery mildew interaction.
In: Plant Pathology, 70 (7)
doi: 10.1111/ppa.13411
Article, Bibliographie

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Abstract

In recent studies, the potential of hyperspectral sensors for the analysis of plant–pathogen interactions was expanded to the ultraviolet range (UV; 200–380 nm) to monitor stress processes in plants. A hyperspectral imaging set‐up was established to highlight the influence of early plant–pathogen interactions on secondary plant metabolites. In this study, the plant–pathogen interactions of three different barley lines inoculated with Blumeria graminis f. sp. hordei (Bgh, powdery mildew) were investigated. One susceptible genotype (cv. Ingrid, wild type) and two resistant genotypes (Pallas 01, Mla1‐ and Mla12‐based resistance and Pallas 22, mlo5‐based resistance) were used. During the first 5 days after inoculation (dai) the plant reflectance patterns were recorded and plant metabolites relevant in host–pathogen interactions were studied in parallel. Hyperspectral measurements in the UV range revealed that a differentiation between barley genotypes inoculated with Bgh is possible, and distinct reflectance patterns were recorded for each genotype. The extracted and analysed pigments and flavonoids correlated with the spectral data recorded. A classification of noninoculated and inoculated samples with deep learning revealed that a high performance can be achieved with self‐attention networks. The subsequent feature importance identified wavelengths as the most important for the classification, and these were linked to pigments and flavonoids. Hyperspectral imaging in the UV range allows the characterization of different resistance reactions, can be linked to changes in secondary plant metabolites, and has the advantage of being a non‐invasive method. It therefore enables a greater understanding of plant reactions to biotic stress, as well as resistance reactions.

Item Type: Article
Erschienen: 2021
Creators: Brugger, Anna ; Schramowski, Patrick ; Paulus, Stefan ; Steiner, Ulrike ; Kersting, Kristian ; Mahlein, Anne‐Katrin
Type of entry: Bibliographie
Title: Spectral signatures in the UV range can be combined with secondary plant metabolites by deep learning to characterize barley–powdery mildew interaction
Language: English
Date: 2021
Place of Publication: Oxford
Publisher: John Wiley & Sons
Journal or Publication Title: Plant Pathology
Volume of the journal: 70
Issue Number: 7
DOI: 10.1111/ppa.13411
Corresponding Links:
Abstract:

In recent studies, the potential of hyperspectral sensors for the analysis of plant–pathogen interactions was expanded to the ultraviolet range (UV; 200–380 nm) to monitor stress processes in plants. A hyperspectral imaging set‐up was established to highlight the influence of early plant–pathogen interactions on secondary plant metabolites. In this study, the plant–pathogen interactions of three different barley lines inoculated with Blumeria graminis f. sp. hordei (Bgh, powdery mildew) were investigated. One susceptible genotype (cv. Ingrid, wild type) and two resistant genotypes (Pallas 01, Mla1‐ and Mla12‐based resistance and Pallas 22, mlo5‐based resistance) were used. During the first 5 days after inoculation (dai) the plant reflectance patterns were recorded and plant metabolites relevant in host–pathogen interactions were studied in parallel. Hyperspectral measurements in the UV range revealed that a differentiation between barley genotypes inoculated with Bgh is possible, and distinct reflectance patterns were recorded for each genotype. The extracted and analysed pigments and flavonoids correlated with the spectral data recorded. A classification of noninoculated and inoculated samples with deep learning revealed that a high performance can be achieved with self‐attention networks. The subsequent feature importance identified wavelengths as the most important for the classification, and these were linked to pigments and flavonoids. Hyperspectral imaging in the UV range allows the characterization of different resistance reactions, can be linked to changes in secondary plant metabolites, and has the advantage of being a non‐invasive method. It therefore enables a greater understanding of plant reactions to biotic stress, as well as resistance reactions.

Uncontrolled Keywords: Blumeria graminis f. sp. hordei, deep learning, Hordeum vulgare, hyperspectral imaging, secondary plant metabolites, UV range
Classification DDC: 000 Generalities, computers, information > 004 Computer science
500 Science and mathematics > 580 Plants (botany)
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: 15 Feb 2024 14:04
Last Modified: 15 Feb 2024 14:04
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