Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions

Abstract

BackgroundThe detection and characterization of resistance reactions of crop plants against fungal pathogens are essential to select resistant genotypes. In breeding practice phenotyping of plant genotypes is realized by time consuming and expensive visual rating. In this context hyperspectral imaging (HSI) is a promising non-invasive sensor technique in order to accelerate and to automate classical phenotyping methods.A hyperspectral microscope was established to determine spectral changes on the leaf and cellular level of barley (Hordeum vulgare) during resistance reactions against powdery mildew (Blumeria graminis f.sp. hordei, isolate K1). Experiments were conducted with near isogenic barley lines of cv. Ingrid, including the susceptible wild type (WT), mildew locus a 12 (Mla12 based resistance) and the resistant mildew locus o 3 (mlo3 based resistance), respectively. The reflection of inoculated and non-inoculated leaves was recorded daily with a hyperspectral linescanner in the visual (400 – 700 nm) and near infrared (700 – 1000 nm) range 3 to 14 days after inoculation.ResultsData analysis showed no significant differences in spectral signatures between non-inoculated genotypes. Barley leaves of the near-isogenic genotypes, inoculated with B. graminis f.sp. hordei differed in the spectral reflectance over time, respectively. The susceptible genotypes (WT, Mla12) showed an increase in reflectance in the visible range according to symptom development. However, the spectral signature of the resistant mlo-genotype did not show significant changes over the experimental period. In addition, a recent data driven approach for automated discovery of disease specific signatures, which is based on a new representation of the data using Simplex Volume Maximization (SiVM) was applied. The automated approach - evaluated in only a fraction of time revealed results similar to the time and labor intensive manually assessed hyperspectral signatures. The new representation determined by SiVM was also used to generate intuitive and easy to interpretable summaries, e.g. fingerprints or traces of hyperspectral dynamics of the different genotypes.ConclusionWith this HSI based and data driven phenotyping approach an evaluation of host-pathogen interactions over time and a discrimination of barley genotypes differing in susceptibility to powdery mildew is possible.

Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance

“…An important step towards the more complex phenotyping has been done by involvement hyperspectral imaging (HSI) and subsequent analyses (Kuska et al, 2015). This technique has been successfully used in remote sensing applications to estimate the level of salinity in soils, using numerous indices to assess the concentration of salt according to different wavelengths of reflectance (Poss et al, 2006;Hamzeh et al, 2013).…”

Combining Digital Imaging and Genome Wide Association Mapping to Dissect Uncharacterized Traits in Plant/Pathogen Interactions

“…This suggests that the lesion size measurements are not providing the 70 full picture of the resistance response (Rowe and Kliebenstein, 2008;Bock et al, 2010;Li et al, 71 2015;Corwin et al, 2016b;Schwanck and Del Ponte, 2016;Barbedo, 2017;Matsunaga et al, 72 2017). There has been recent interest in extending our understanding of plant-pathogen 73 resistance by conducting more extensive phenotyping of disease symptoms, including 74 hyperspectral imaging of lesions that records spectra from the visible into the infrared range 75 (Montes et al, 2007;Kuska et al, 2015;Mutka and Bart, 2015;Leucker et al, 2016). This 76 analysis has differentiated between diseases on the same plant through biochemical responses 77 using light spectra from 400nm to 1000nm (Mahlein et al, 2012).…”

Identification of spectral disease signatures and resistant QTL for charcoal rot infection in soybean

“…However, developing resistant cultivars in breeding programs is time consuming and labor intensive. While recent molecular innovations have accelerated genotyping, breeding programs are now bottlenecked by the visual phenotyping necessary to evaluate a large number of genotypes for performance in response to disease infection (Kuska et al 2015). In regards to breeding for disease resistance, phenotyping is the assessment of resistance based on disease progression or severity scores, and is the driving force behind plant selections and improvement in breeding programs connecting genotypic information and performance in the environment (Fiorani and Schurr 2013).…”

“…In plant phenotyping, changes in the spectral reflectance curve can be associated with different processes in the plant leaf or tissue measured (Mahlein 2016;Mirwaes et al 2016). Differences in these reflectance patterns can be used to identify changes in the imaging subject and develop spectral fingerprints (Kuska et al 2015). In recent plant pathology and phenotyping studies, hyperspectral data has been used to identify differences in the reflectance patterns of resistant and susceptible genotypes inoculated with powdery mildew in barley seedling leaves, the content of M. phaseolina microsclerotia in ground soybean root and stem tissue as a method for severity rating, and has distinguished between the symptoms of three different diseases, Cercospora leaf spot, powdery mildew, and leaf rust at different developmental stages in sugar beet (Fletcher et al 2014;Kuska et al 2015;Mahlein et al 2012b).…”

“…These differing patters of spectral reflectance distinguish diseased tissue from healthy tissue and also identify individual characteristic symptoms of charcoal rot infection in soybean stems. Deviations from the characteristic healthy vegetative spectra have been shown to detect plant diseases in multiple studies(Bauriegel et al 2011;Kuska et al 2015;Leucker et al 2016;Mirwaes et al 2016;Wahabzada et al 2015) Spectral reflectance of selected regions of interest.…”