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

• Published in: Plant phenome journal 2024-12, Vol.7 (1)
• Main Authors: Washburn, Jacob D., Adak, Alper, DeSalvio, Aaron J., Arik, Mustafa A., Murray, Seth C.
• Format: Article
• Language: English
• ISSN: 2578-2703; 2578-2703
• DOI: 10.1002/ppj2.20113

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. Temporally dense unoccupied aerial systems (UAS) data contain unique information. Phenomic prediction outperformed genomic prediction. Temporal UAS data improved prediction accuracy.