Multi-omics prediction for yellow rust in bread and durum wheat through conventional and Ai-based frameworks
4 affiliations
- 1International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz
- 2Department of Statistics, Post-Graduate College (COLPOS) Montecillos
- 3Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara
- 4Facultad de Telemática, Universidad de Colima
Abstract
Yellow rust (YR) is a major threat to both bread and durum wheat production, often causing substantial yield losses. Conventional visual scoring of YR severity, while widely adopted, is labor-intensive, time-consuming, and prone to human error. In this study, we evaluated the predictability (PA), defined as the correlation between predicted and observed values, using genomic and phenomic data for YR severity under multiple prediction scenarios in two biparental wheat populations (bread and durum). YR scoring was conducted on two dates, with YR severity visually assessed while unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) data were collected using a multispectral camera. HTP data were processed to extract spectral wavelengths and vegetation indices (VIs), and all lines were also genotyped using SNP arrays. We tested a diverse set of models, including parametric, machine learning, and deep learning approaches. PA increased markedly when HTP-derived data were used compared with genomic markers alone. For example, support vector regression (SVR) improved from 0.35 (markers only) to 0.87 (wavelengths only). However, integrating genomic and phenomic data did not yield further improvements, as models often plateaued when using HTP-derived features alone. Cross-crop prediction demonstrated promising generalization across bread and durum wheat, achieving PA values up to 0.83. For this last task, best linear unbiased prediction (BLUP) and multilayer perception (MLP) consistently provided robust performance across scenarios. These findings highlight the strong potential of UAV-based HTP for rapid, scalable, and accurate prediction of YR severity in wheat. While genomics retains broad utility for breeding, the practical integration of phenomics and AI-driven prediction pipelines will ultimately depend on breeding program strategies, resources, and objectives.
Related
- EnsPlex: Integrative Protein Complex Prediction through Multi-source Complementary Structural Sampling and Topology-Aware Candidate Selection
- Integrating multi-environment and path coefficient analysis for the identification of yellow rust-resistant bread wheat (Triticum aestivum L.) genotypes
- Computational prediction of a multi-epitope Human Metapneumovirus vaccine candidate through integrated reverse vaccinology and pan-genomic approaches
- An extensible deep learning framework for multi-type chromatin interaction prediction through hierarchical adaptation strategies
- Construction of an infectious clone of tomato leaf curl New Delhi virus and development of a duplex assay through recombinase polymerase amplification-lateral flow dipstick to detect the virus and tomato yellow leaf curl virus simultaneously
BibTeX
@article{vitale2026multiomics,
title = {{Multi-omics prediction for yellow rust in bread and durum wheat through conventional and Ai-based frameworks}},
author = {Paolo Vitale and Jose Crossa and Abelardo Montesinos-López and Karim Ammar and Susanne Dreisigacker and Velu Govindan and Osval Montesinos-López and Gilberto Thompson and Keith Gardner and Matthew Reynolds and Guillermo Gerard},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-59013-9},
url = {https://doi.org/10.1038/s41598-026-59013-9},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-omics prediction for yellow rust in bread and durum wheat through conventional and Ai-based frameworks
AU - Paolo Vitale
AU - Jose Crossa
AU - Abelardo Montesinos-López
AU - Karim Ammar
AU - Susanne Dreisigacker
AU - Velu Govindan
AU - Osval Montesinos-López
AU - Gilberto Thompson
AU - Keith Gardner
AU - Matthew Reynolds
AU - Guillermo Gerard
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-59013-9
UR - https://doi.org/10.1038/s41598-026-59013-9
AB - Yellow rust (YR) is a major threat to both bread and durum wheat production, often causing substantial yield losses. Conventional visual scoring of YR severity, while widely adopted, is labor-intensive, time-consuming, and prone to human error. In this study, we evaluated the predictability (PA), defined as the correlation between predicted and observed values, using genomic and phenomic data for YR severity under multiple prediction scenarios in two biparental wheat populations (bread and durum). YR scoring was conducted on two dates, with YR severity visually assessed while unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) data were collected using a multispectral camera. HTP data were processed to extract spectral wavelengths and vegetation indices (VIs), and all lines were also genotyped using SNP arrays. We tested a diverse set of models, including parametric, machine learning, and deep learning approaches. PA increased markedly when HTP-derived data were used compared with genomic markers alone. For example, support vector regression (SVR) improved from 0.35 (markers only) to 0.87 (wavelengths only). However, integrating genomic and phenomic data did not yield further improvements, as models often plateaued when using HTP-derived features alone. Cross-crop prediction demonstrated promising generalization across bread and durum wheat, achieving PA values up to 0.83. For this last task, best linear unbiased prediction (BLUP) and multilayer perception (MLP) consistently provided robust performance across scenarios. These findings highlight the strong potential of UAV-based HTP for rapid, scalable, and accurate prediction of YR severity in wheat. While genomics retains broad utility for breeding, the practical integration of phenomics and AI-driven prediction pipelines will ultimately depend on breeding program strategies, resources, and objectives.
SN - 2045-2322
ER -