Towards Streamlining the Choice of Crossing Combinations in Plant Breeding by Integrating Model-Based Recommendations and Plant Breeder’s Preferences

Selecting crossing combinations crucial for successfully developing new improved crop varieties and genomic data from DNA markers have become invaluable for guiding plant breeders in evaluating and choosing promising crosses between potential parents. However, navigating the vast array of thousands of possible parental combinations, even with extensive genomic information, can be challenging, even for experienced breeders with deep knowledge of their crop’s gene pool. This case study aimed to evaluate the effectiveness of a recommender system to support plant breeders in this complex decision-making process. It took a retrospective approach, analyzing selection decisions made by an experienced breeder across several thousand potential crossing combinations over six years. The results indicated that a recommender system could significantly reduce the time and effort needed to identify promising crosses aligned with the breeder’s preferences. However, active feedback from the breeder to the recommender system appeared to be essential for achieving a satisfactory prediction. Integrating model-based recommendations and plant breeder’s preferences in a recommender system featuring such a reciprocal fine-tuning scheme, where the breeder actively provides feedback to the machine in the style of hybrid human–artificial intelligence, represents one step towards streamlining the choice of crossing combinations in plant breeding programs.