Skip to main content
ARS Home » Midwest Area » Columbia, Missouri » Plant Genetics Research » Research » Publications at this Location » Publication #410804
Research Project: Adaptation of Grain Crops to Varying Environments Including Climates, Stressors, and Human Uses

Location: Plant Genetics Research

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

Author
item Washburn, Jacob
item ADAK, ALPER - Texas A&M University
item DESALVIO, AARON - Texas A&M University
item MUSTAFA, ARIK - Texas A&M University
item MURRAY, SETH - Texas A&M University

Submitted to: The Plant Phenome Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/22/2024
Publication Date: 7/29/2024
Citation: Washburn, J.D., Adak, A., Desalvio, A., Mustafa, A., Murray, S.C. 2024. High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates. The Plant Phenome Journal. 7. https://doi.org/10.1002/ppj2.20113.
DOI: https://doi.org/10.1002/ppj2.20113

Interpretive Summary: Unoccupied aerial systems (UAS, UAV, drone) are critical to data collection in both scientific studies and applied agriculture. They enable the inexpensive collection of highly accurate data at multiple times throughout the growing season. Most studies to date have collected UAV data at a limited number of time points (5-10) throughout the season and have found that the data at each time point is valuable and relatively unique. It remains unknown how temporally dense the sampling would need to be before the information in each sample becomes redundant and an efficient use of resources for researchers and farmers. To investigate this question, 43 flights were performed and analyzed in a single season with an average of 2.8 days between flights. The results indicated that even at this high temporally sampling density unique and potentially useful information is still contained between many of the flights. However, for most purposes (for example yield prediction), far fewer flights should typically be sufficient.

Technical 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.