Genomic characterization of plant height and lodging in spring oat ( Avena sativa L.) using time‐series aerial sensor data

Abstract Plant height is one of the key economic traits in spring oat ( Avena sativa L.) and a major determinant of lodging, and consequently, yield potential. Understanding the genetic basis of the variation in plant height can significantly benefit plant improvement efforts. Although plant height is straightforward to measure, it can be intense when conducted across numerous plots and environments. A rapid and cost‐effective estimation of plant height from aerial images obtained from sensors mounted onto uncrewed aerial systems (UAS) can be an alternative approach to accelerate the goals of breeding programs. To achieve this, a global panel of oat accessions was evaluated for automatic plant height estimation at the plot‐level using digital surface models. Vegetation indices were calculated from the UAS RGB (red, green, blue) color and multispectral imagery collected from four sites at different intervals. Vegetation indices were modeled with conventional measurements to assess accuracy. Genome‐wide association mapping was performed using UAS and ground truth data to gain a comprehensive understanding of the genetic architecture of plant height. Finally, we discuss genomic prediction for both UAS and ground truth data to contextualize the use of UAS in practical plant breeding.