A research team has shown that combining genomic information with low-cost drone-derived phenomic data can improve the prediction of key yield traits in potato breeding. The study addresses a long-standing challenge: breeders must identify superior clones from large populations while working with a crop whose complex tetraploid genetics and clonal propagation can slow genetic gain. By bringing together DNA-marker information and repeated field images, the researchers captured both inherited potential and environmentally influenced plant performance. The integrated approach was especially effective for total tuber yield and larger tubers, suggesting that accessible image-based phenotyping can add practical value to genomic selection and help breeders make earlier, more efficient decisions.
Potato (Solanum tuberosum) breeding remains unusually time- and resource-intensive. New cultivars can take 10 to 15 years to develop, and selection must account for complex polyploid inheritance, low propagation rates, and strong environmental effects on yield. Genomic selection can accelerate breeding by predicting performance from DNA markers, while high-throughput phenotyping can rapidly measure crop responses in the field. Yet genomic prediction for highly quantitative potato traits often reaches only modest accuracy, and aboveground images do not always translate reliably into belowground tuber performance. Previous work has also relied heavily on multispectral imaging. Given these challenges, a deeper investigation is needed into whether inexpensive, repeated field imaging can complement genomic information for more accurate potato selection.
Published (DOI: 10.1093/hr/uhag175) online on April 30, 2026, in Horticulture Research, the study was conducted by researchers from the Department of Plant Breeding at the Swedish University of Agricultural Sciences (SLU) in Alnarp, Sweden. The team evaluated whether genomic single nucleotide polymorphism (SNP) data and phenomic traits derived from unmanned aerial vehicle (UAV) red–green–blue (RGB) imagery could be combined to predict agronomic performance. Their analysis focused on tuber yield by size class and starch content, while also testing whether longitudinal image information could capture environmental variation that genomic data alone may miss.
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