A breeder in the sky: scoring flowering with fewer flights
- 1The Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68588, USA;Department of Agronomy and Horticulture, University of Nebraska-
Abstract
Flowering time is a trait of broad interest and importance to both plant breeders and plant biologists. Unlike many other traits, flowering time cannot be scored accurately from a single observation; its measurement requires repeated observations of the same experiments over time. Both classification- and object-detection-based approaches have demonstrated the potential to estimate flowering time from Unmanned aerial vehicle (UAV) imagery rather than by manual observation, but they still require data collection at many time points. Here, we train and deploy a regression-based framework for scoring flowering time. Using a dataset of more than 200,000 UAV images and associated flowering-time records collected across 27 environments, we demonstrate that this approach enables the prediction of flowering time from as few as one observation per field experiment and successfully generalizes to field experiments in environments not represented in the training dataset. These results indicate that regression-based approaches for predicting flowering time from sparse UAV observations have the potential to substantially reduce the data-collection burden in field experiments
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BibTeX
@misc{ji2026breeder,
title = {{A breeder in the sky: scoring flowering with fewer flights}},
author = {Z. Ji and A. J. DeSalvio and A. Adak and M. A. Arik and D. Ertl and S. M. Kaeppler and J. Edwards and S. Flint-Garcia and C. N. Hirsch and A. M. Thompson and J. D. Washburn and N. de Leon and B. Maharjan and D. Santra and P. Izere and B. Zhao and Y. Shi and S. C. Murray and Y. Ge and J. C. Schnable},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.19.752900},
url = {https://www.biorxiv.org/content/10.64898/2026.09.19.752900v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A breeder in the sky: scoring flowering with fewer flights
AU - Z. Ji
AU - A. J. DeSalvio
AU - A. Adak
AU - M. A. Arik
AU - D. Ertl
AU - S. M. Kaeppler
AU - J. Edwards
AU - S. Flint-Garcia
AU - C. N. Hirsch
AU - A. M. Thompson
AU - J. D. Washburn
AU - N. de Leon
AU - B. Maharjan
AU - D. Santra
AU - P. Izere
AU - B. Zhao
AU - Y. Shi
AU - S. C. Murray
AU - Y. Ge
AU - J. C. Schnable
PY - 2026
DA - 2026/09/19
PB - bioRxiv
DO - 10.64898/2026.09.19.752900
UR - https://www.biorxiv.org/content/10.64898/2026.09.19.752900v1
AB - Flowering time is a trait of broad interest and importance to both plant breeders and plant biologists. Unlike many other traits, flowering time cannot be scored accurately from a single observation; its measurement requires repeated observations of the same experiments over time. Both classification- and object-detection-based approaches have demonstrated the potential to estimate flowering time from Unmanned aerial vehicle (UAV) imagery rather than by manual observation, but they still require data collection at many time points. Here, we train and deploy a regression-based framework for scoring flowering time. Using a dataset of more than 200,000 UAV images and associated flowering-time records collected across 27 environments, we demonstrate that this approach enables the prediction of flowering time from as few as one observation per field experiment and successfully generalizes to field experiments in environments not represented in the training dataset. These results indicate that regression-based approaches for predicting flowering time from sparse UAV observations have the potential to substantially reduce the data-collection burden in field experiments
ER -