Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials.

ABSTRACT:

Key message

Optimized phenomic selection in durum wheat uses near-infrared spectra, feature engineering and parameter tuning. Our study reports improvements in predictive ability and emphasizes customized preprocessing for different traits and models. The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genoty

SUBMITTER: Meyenberg C 

PROVIDER: S-EPMC11263437 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature