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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Plant Bioinformatics
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1451784
This article is part of the Research Topic Recent Advances in Big Data, Machine, and Deep Learning for Precision Agriculture, Volume II View all 3 articles

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

Provisionally accepted
Daniel Mieres-Castro Daniel Mieres-Castro 1Carlos Maldonado Carlos Maldonado 2Freddy Mora-Poblete Freddy Mora-Poblete 1*
  • 1 Institute of Biological Sciences, University of Talca, Talca, Chile
  • 2 Center for Genomics and Bioinformatics, Faculty of Sciences, Major university, Santiago, Santiago Metropolitan Region (RM), Chile

The final, formatted version of the article will be published soon.

    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 had no significant correlation with any growth-related traits. This suggests that selection solely based on growth-related traits did not influence the essential oil content. Genomic heritability estimates ranged from 0.25 (diameter at breast height (DBH) and oil yield) to 0.71 (DBH and stem straightness (ST)), while pedigree-based heritability exhibited a broader range, from 0.05 to 0.88. Notably, oil yield was found to be moderate to highly heritable, with genomic values ranging from 0.25 to 0.60, alongside a pedigree-based estimate of 0.48. The DL prediction models consistently achieved higher prediction accuracy (PA) values with a Multi-trait approach for most traits analyzed, including oil yield (0.699), tree height (0.772), DBH (0.745), slenderness coefficient (0.616), stem volume (0.757), and ST (0.764). The Uni-trait approach achieved superior PA values solely for branching quality (0.861). NIR spectral absorbance was the best omics data for CNN or MLP models with a Multi-trait approach. These results highlight considerable genetic variation within the Eucalyptus progeny trial, particularly regarding oil production. Our results contribute significantly to understanding omics-assisted deep learning models as a breeding strategy to improve growth-related traits and optimize essential oil production in this species.

    Keywords: Eucalyptus essential oil, wood production, deep learning, Genomic prediction, phenomic prediction, Multi-Trait, multi-omic, High-throughput Plant Phenotyping and Genotyping

    Received: 19 Jun 2024; Accepted: 18 Sep 2024.

    Copyright: © 2024 Mieres-Castro, Maldonado and Mora-Poblete. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Freddy Mora-Poblete, Institute of Biological Sciences, University of Talca, Talca, Chile

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.