Phenomic‐assisted selection: Assessment of the potential of near‐infrared spectroscopy for blueberry breeding

Abstract The integration of high‐throughput technologies such as near‐infrared spectroscopy (NIRS) for phenomic‐assisted selection in plant breeding has gained relevance in recent years. In blueberry, the use of phenomic selection could enable selection in the early stages, where thousands of seedlings are visually selected, and the use of genomic selection (GS) is cost‐prohibitive. In this study, we compared phenomic and GS in 372 genotypes, which were phenotyped for multiple fruit quality traits across 2 years. Our contribution is fourfold: (i) phenomic and GS methods have comparable predictive performances for multiple traits; (ii) leaves can achieve the highest genetic gains in the long term among NIRS of different biological tissues (leaf and fruit); (iii) BayesB, mixed models, and random forest resulted in the best predictive results across traits for optimizing phenomic prediction; and finally (iv) attention was drawn to the possibility of using phenomic prediction across environments. Altogether, for the first time in the blueberry literature, the utility of NIRS for phenomic‐assisted selection is demonstrated. While the primary focus is on blueberries, this approach can be evaluated in other fruit trees.