Drones Cut Costs for Breeding Hardier Potatoes, Study Shows

Abstract Solanum tuberosum L. (potato) is the world's most important vegetable crop, and developing improved cultivars is paramount for global food security. The efficacy of the genomic prediction models that accelerate breeding and genome‐wide association studies (GWAS) depends on large, high‐quality phenotypic datasets, which are often associated with inhibitory large costs for manual phenotyping. Recent advances in drone‐based phenotyping offer an affordable, high‐throughput phenotyping approach. This study evaluated the reliability of novel drone‐based phenotyping for GWAS in tetraploid potato. Image‐derived data on plant emergence and late blight resistance were collected using a drone‐mounted red‐green‐blue camera across separate field trials over two to three growing seasons. The dataset comprised 6262 phenotypic observations of emergence and 6972 observations of late blight resistance. A total of 2576 clones in an emergence panel and 1520 clones in a late blight resistance panel were genotyped for 8731 markers using a custom single nucleotide polymorphism array. GWAS yielded five significant quantitative trait loci (QTLs) for late blight resistance—three coinciding with known Rpi ‐gene clusters and StCDF1 —and four significant minor‐effect QTLs underpinning emergence. Rediscovery of known late blight loci supports the reliability of the approach. Flanking regions of the emergence QTLs include candidate genes encoding proteins involved in plant growth and development, stress response, compound trafficking, and nutrient assimilation. However, 58%–76% of genes in the flanking regions lack functional annotation, underscoring the need for transcriptomic studies of early plant development. We demonstrate that integrating remote phenotyping and GWAS can yield robust results in tetraploid potatoes, overcoming the past limitation of phenotyping costs.