This study leverages advanced genomics and machine learning to refine the understanding of key fruit quality traits in peaches. Using whole-genome resequencing data from an F1 progeny of two distant peach cultivars, the researchers constructed an ultra-high-density genetic map, identifying key quantitative trait loci (QTLs) for traits such as fruit shape, color, and maturity. Notably, the study introduces machine learning models for more accurate phenotyping of fruit color, revealing two previously undetectable QTLs for peach flesh color variation. These innovations provide a new framework for precision breeding, enhancing peach quality and other complex traits through improved mapping and phenotyping strategies.

Researchers have developed a novel computational imaging system that integrates optical compression and deep learning to achieve high-speed video capture using only 5% of the data required by conventional cameras. The system successfully visualized Leidenfrost droplet dynamics at 1,200 frames per second, delivering performance comparable to commercial high-speed cameras while significantly reducing cost and storage requirements.