Image-based Phenotyping and Disease Screening of Multiple Populations for resistance to Verticillium dahliae in cultivated strawberry Fragaria x ananassa

1.0Verticillium dahliae is a highly detrimental pathogen of soil cultivated strawberry (Fragaria x ananassa). 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 biparental 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 V. dahliae subclade II-1 specific resistance in the cultivar Redgauntlet and subclade II-2 specific resistance in Fenella and Chandler.\n\nA large-scale low-cost phenotyping platform was developed utilising 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.\n\nA co-dominant resistant QTL; FaRVd3D, present in both Redgauntlet and Hapil cultivars exhibited a major effect of 18.3 % when the two resistance alleles were combined. Another allele, FaRVd5D, 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 bi-parental cross.\n\nMarkers identified in bi-parental 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 FaRVd2B from Redgauntlet and FaRVd6D 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.