Molecular marker analysis allow for a rapid and advanced pre-selection and resistance screenings in plant breeding processes. During the phenotyping process, optical sensors have proved their potential to determine and assess the function of the genotype of the breeding material. Thereby, biomarkers for speciﬁc disease resistance traits provide valuable information for calibrating optical sensor approaches during early plant-pathogen interactions. In this context, the combination of physiological, metabolic phenotyping and phenomic proﬁles could establish efﬁcient identiﬁcation and quantiﬁcation of relevant genotypes within breeding processes. Experiments were
conducted with near-isogenic lines of H. vulgare (susceptible, mildew locus o (mlo) and Mildew locus a (Mla) resistant). Multispectral imaging of barley plants was daily conducted 0–8 days after inoculation (dai) in a high-throughput facility with 10 wavelength bands from 400 to 1,000 nm. In parallel, the temporal dynamics of the activities of invertase isoenzymes, as key sink speciﬁc enzymes that irreversibly cleave the transport
sugar sucrose into the hexose monomers, were proﬁled in a semi high-throughput approach. The activities of cell wall, cytosolic and vacuole invertase revealed speciﬁc dynamics of the activity signatures for susceptible genotypes and genotypes with mlo and Mla based resistances 0–120 hours after inoculation (hai). These patterns could be used to differentiate between interaction types and revealed an early inﬂuence of Blumeria graminis f.sp. hordei (Bgh) conidia on the speciﬁc invertase activity already 0.5 hai. During this early powdery mildew pathogenesis, the reﬂectance intensity increased in the blue bands and at 690 nm. The Mla resistant plants showed an
increased reﬂectance at 680 and 710 nm and a decreased reﬂectance in the near infrared bands from 3 dai. Applying a Support Vector Machine classiﬁcation as a supervised machine learning approach, the pixelwise identiﬁcation and quantiﬁcation of powdery mildew diseased barley tissue and hypersensitive response spots were established. This enables an automatic identiﬁcation of the barley-powdery mildew interaction. The study established a proof-of-concept for plant resistance phenotyping with multispectral imaging in high-throughput. The combination of invertase analysis and multispectral imaging showed to be a complementing validation system. This will provide
a deeper understanding of optical data and its implementation into disease resistance screening.