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.

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.

Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new nondestructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola, which causes Cercospora leaf spot disease (CLS), and Uromyces betae, which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Nondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV range. The study demonstrates that HSI in the UV range is a promising, nondestructive tool to investigate the influence of plant diseases on plant physiology and biochemistry.