Enhancing prediction accuracy of foliar essential oil content, growth, and stem quality in <i>Eucalyptus globulus</i> using multi-trait deep learning models.

ABSTRACT: Eucalyptus globulus Labill., is a recognized multipurpose tree, which stands out not only for the valuable qualities of its wood but also for the medicinal applications of the essential oil extracted from its leaves. In this study, we implemented an integrated strategy comprising genomic and phenomic approaches to predict foliar essential oil content, stem quality, and growth-related traits within a 9-year-old breeding population of E. globulus. The strategy involved evaluating Uni/Multi-trait deep learning (DL) models by incorporating genomic data related to single nucleotide polymorphisms (SNPs) and haplotypes, as well as the phenomic data from leaf near-infrared (NIR) spectroscopy. Our results showed that essential oil content (oil yield) ranged from 0.01 to 1.69% v/fw and

SUBMITTER: Mieres-Castro D 

PROVIDER: S-EPMC11499176 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature