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

Abstract

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.

Plant phenotyping relevance

UVハイパースペクトル画像を用いて植物病害ストレスおよび抵抗性反応を非侵襲的に特徴づけ、深層学習分類と波長重要度解析も検証しており、表現型取得・解析法が中心である。

Code and data availability

The paper's hyperspectral UV imaging data, metabolite measurements, and deep learning analysis are not publicly deposited; the data availability statement says they are available only from the corresponding author upon reasonable request. No public code, model, or dataset URL is provided.

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