Characterization of grain yield in spring wheat using agronomic, image-derived phenomics, and genomic approaches

Abstract

Future yield gains in wheat (Triticum aestivum L.) will increasingly rely on multi-omic approaches that combine phenomic and genomic data. In this study, we used canopy imaging to examine plant growth over time to assess plant growth dynamics and determine their relationship with grain yield and yield components. The doubled haploid spring wheat population (AAC Brandon/Pasteur) was phenotyped from 2021 to 2023 at Ottawa, Canada. High-throughput image data were collected using the PlotCam, alongside manual measurements of yield components. These data were used to develop yield prediction models and quantitative trait locus (QTL) analysis using single nucleotide polymorphism-based genotyping. Significant correlations were found between image-derived traits, particularly estimated biomass (EBM), and both terminal dry biomass and grain yield. QTLs were associated with yield components, phenomic traits, and grain yield. Notably, a major grain yield QTL on chromosome 6D was linked to EBM from tillering through dough stages. A seed weight QTL on chromosome 2B was associated with senescence rate, while another on 6B co-located with EBM at heading, flowering, and dough stages. Additionally, a QTL for spike and seed number on 5B was linked to EBM at emergence, heading, and dough stages. These results highlight the value of integrating image-derived phenomics and genomic analysis to better understand the physiological basis of grain yield. This approach provides a deeper understanding of source–sink dynamics and offers a framework for selecting high-performing genotypes across environments.