High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates

Abstract

Abstract Unoccupied aerial systems (UAS, unoccupied aerial vehicle, and drone) are high‐throughput phenotyping tools that can provide transformational insights into biological and agricultural research, but practical and scientific questions remain. The utility of dense versus sparse temporal collections (e.g., daily, weekly, and monthly flights) has important implications for experimental design, resource allocation, and the scope of scientific questions investigated through UAS. UAS‐derived image data were collected on over 1500 maize hybrid yield trial plots with a temporal (longitudinal, 4D) sampling density of 2.8 days on average between 43 flights throughout the growing season. Correlations of vegetation index (VI) phenomic features between flight dates were generally high between flights separated by only 1 or 2 days but dropped when 3, 4, or more days separated the flights. These varied depending on specific dates and the VI used. Correlations between flights were lower around flowering time than during other parts of the season indicating the phenotypic uniqueness of this developmental period. The cross‐validation accuracy of end of season yields prediction models on untested genotypes from the UAS data (0.59 and 0.62) far exceeded genomic prediction accuracy (0.24) for the same test set hybrids regardless of whether all flight dates were used for prediction or only dates before flowering. Phenomic prediction accuracy marginally increased as additional flight dates were added throughout the season.

Plant phenotyping relevance

UAS画像を用いた高頻度植物フェノタイピングの時間分解能と予測性能を評価しており、取得・解析方法の技術的検証が中心です。

Code and data availability

The paper's data availability statement explicitly provides the authors' analysis/figure-generation scripts on GitHub and both the scripts and phenotypic tabular data on Zenodo, directly enabling reproduction of this paper's UAS phenomic prediction analyses.