[Submitted on 13 Oct 2026]
Title:Agronomic Characterization and Time-Series UAV Phenotyping to Support Field Pea Breeding in Iowa
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Abstract:Field pea has gained interest in the Midwest U.S. and has the potential to benefit Iowa cropping systems. This study evaluated 17 accessions and 7 Canadian varieties for agronomic and seed yield performance. This study found that PI accession trials exhibit genetic diversity in phenology and canopy traits, whereas the Canadian elite varieties demonstrated great yield potential under Iowa conditions. The accession genotypes matured between 75 DAP (late June) and 95 DAP (late July) across six site-years. The top-yielding Canadian varieties averaged 1961 kg/ha across two site-years. To comprehensively assess field pea performance, this study extracted time-series Unmanned Aerial Vehicle(UAV)-derived traits, assessed UAV-derived traits, and evaluated their correlations with multiple agronomic traits and yield. The results found that UAV-derived time-series canopy area was the most yield-relevant trait (r = 0.63) among all UAV-derived canopy features, vegetation indices, and manually collected measurements. Compared with yield, UAV-derived canopy area showed higher selection reliability (H2 = 0.87, CV = 13.46%), making it suitable for phenotypic selection and accelerating Midwest field pea breeding programs. This study identifies useful UAV-derived features for pea breeding and demonstrates the value of integrating diverse germplasm, elite commercial varieties, and time-series UAV phenotyping. Combining genetic variation from the accession trial with the yield potential of high-performing commercial varieties provides valuable breeding materials and phenotyping strategies for developing locally adapted, high-yielding field pea varieties for future production in the Midwest.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.19154 [cs.CV]
(or arXiv:2610.19154v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.19154
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yanben Shen [view email] [v1] Tue, 13 Oct 2026 20:21:52 UTC (14,397 KB)
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