ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
This article is part of the Research TopicAdvancements in Spectral Imaging Technologies for Breeding Resilient CropsView all articles
Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches
Provisionally accepted
Sudip Kunwar1
MD ALI BABAR1*
Diego Jarquin1
Yiannis Ampatzidis1
Naeem Khan1
Janam Prabhat Acharya1
Jordan McBreen1Samuel Adewale1
Gina Lynn Brown-Guedira2- 1University of Florida, Gainesville, United States
- 2USDA-ARS, Raleigh, United States
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Optimizing biomass partitioning is essential for achieving sustainable yield improvement in wheat, particularly under increasing environmental stress. Traits such as spike partitioning index (SPI), harvest index (HI), and fruiting efficiency (FE) are central to understanding how assimilates are allocated between vegetative and reproductive organs. However, their complex physiology and the difficulty of manual phenotyping have limited their routine use in breeding programs. This study assessed the potential of unmanned aerial vehicle (UAV)-based hyperspectral reflectance data to predict biomass partitioning traits and related yield components in wheat. Three trials of facultative soft wheat lines (2022–2024) and an independent validation set of advanced breeding lines were used to develop genomic prediction (GP), phenomic prediction (PP), and integrated multi-omic models combining genomic, phenomic, and environmental covariates (ECs). Kernel-based best linear unbiased prediction (BLUP), and machine-learning based, random forest regression and partial least squares regression were implemented to estimate predictive ability (PA). Phenomics-driven models markedly outperformed GP across most traits, achieving PA up to 0.61 for SPI, 0.56 for FE, 0.71 for grains/m2 (GN), and 0.66 for grain yield (GY). Hyperspectral data provided higher accuracy than vegetation indices, and multi-omic integration slightly improved prediction (PA up to 0.73 for GN). These results demonstrate that UAV-based hyperspectral phenotyping can effectively capture canopy-level physiological signals associated with biomass partitioning, offering a scalable and data-driven approach for in-season selections. This can help wheat breeding programs to optimize biomass partitioning in modern wheat cultivars for long-term yield resilience and genetic gain.
Keywords: Biomass partitioning optimization, Data-driven breeding, Environmental covariates, Prediction enhancement, remote sensing, vegetation indices
Received: 12 Nov 2025; Accepted: 20 Jan 2026.
Copyright: © 2026 Kunwar, BABAR, Jarquin, Ampatzidis, Khan, Acharya, McBreen, Adewale and Brown-Guedira. 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: MD ALI BABAR
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