Amr Abd-Elrahman

High-throughput phenotyping is an emerging approach in plant science, but thus far only a few applications have been made in horticultural crop breeding. Remote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection. In the present study, we evaluated the integration of spectral analysis of canopy reflectance and genomic information for the prediction of strawberry (Fragaria × ananassa) powdery mildew disease. Two multi-parental breeding populations of strawberry comprising a total of 340 and 464 pedigree-connected seedlings were evaluated in two separate seasons. A single-trait Bayesian prediction method using 1001 spectral wavebands in the ultraviolet–visible–near infrared region (350–1350 nm wavelength) combined with 8552 single nucleotide polymorphism markers showed up to 2-fold increase in predictive ability over models using markers alone. The integration of high-throughput phenotyping was further validated independently across years/trials with improved response to selection of up to 90%. We also conducted Bayesian multi-trait analysis using the estimated vegetative indices as secondary traits. Three vegetative indices (Datt3, REP_Li, and Vogelmann2) had high genetic correlations (rA) with powdery mildew visual ratings with average rA values of 0.76, 0.71, and 0.71, respectively. Increasing training population sizes by incorporating individuals with only vegetative index information yielded substantial increases in predictive ability. These results strongly indicate the use of vegetative indices as secondary traits for indirect selection. Overall, combining spectrometry and genome-wide prediction improved selection accuracy and response to selection for powdery mildew resistance, demonstrating the power of an integrated phenomics–genomics approach in strawberry breeding.