Machine learning-based classification of roses using 18 SNP markers for optimized genebank management

Our results demonstrate that machine learning applied to SNP marker data can robustly resolve genetic relationships among rose cultivars and provide novel insights into the alignment of horticultural classifications with genomic structure. The high predictive accuracies obtained suggest that marker-based classification can serve as a reliable complementary tool for genebank management, cultivar identification, and reassessment of traditional rose categories.