Identifying Resistance in Strawberry Through Disease Screening of Multiple Populations and Image Based Phenotyping.

is a highly detrimental pathogen of soil cultivated strawberry (). Breeding of Verticillium wilt resistance into commercially viable strawberry cultivars can help mitigate the impact of the disease. In this study we describe novel sources of resistance identified in multiple strawberry populations, creating a wealth of data for breeders to exploit. Pathogen-informed experiments have allowed the differentiation of subclade-specific resistance responses, through studying subclade II-1 specific resistance in the cultivar "Redgauntlet" and subclade II-2 specific resistance in "Fenella" and "Chandler." A large-scale low-cost phenotyping platform was developed utilizing automated unmanned vehicles and near infrared imaging cameras to assess field-based disease trials. The images were used to calculate disease susceptibility for infected plants through the normalized difference vegetation index score. The automated disease scores showed a strong correlation with the manual scores. A co-dominant resistant QTL; , present in both "Redgauntlet" and "Hapil" cultivars exhibited a major effect of 18.3% when the two resistance alleles were combined. Another allele, , identified in the "Emily" cultivar was associated with an increase in Verticillium wilt susceptibility of 17.2%, though whether this allele truly represents a susceptibility factor requires further research, due to the nature of the F1 mapping population. Markers identified in populations were validated across a set of 92 accessions to determine whether they remained closely linked to resistance genes in the wider germplasm. The resistant markers from "Redgauntlet" and from "Chandler" were associated with resistance across the wider germplasm. Furthermore, comparison of imaging versus manual phenotyping revealed the automated platform could identify three out of four disease resistance markers. As such, this automated wilt disease phenotyping platform is considered to be a good, time saving, substitute for manual assessment.