UAV-based multispectral time-series imagery of biomass sorghum - 2019
Citation:
Varela Quintela, Sebastian; Leakey, Andrew (2021): UAV-based multispectral time-series imagery of biomass sorghum - 2019. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0353090_V1
Persistent link for this dataset:
Related Article Varela, S., Pederson, T., Bernacchi, C.J., Leakey, A.D.B. "Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning." Remote Sensing. 2021, 13, 1763. https://doi.org/10.3390/rs13091763
Dataset Description

UAV-based high-resolution multispectral time-series orthophotos utilized to understand the relation between growth dynamics, imagery temporal resolution, and end-of-season biomass productivity of biomass sorghum as bioenergy crop. Sensor utilized is a RedEdge Micasense flown at 40 meters above ground level at the Energy Farm- UIUC in 2019.
Subject Life Sciences
Keywords Unmanned aerial vehicles; High throughput phenotyping; Machine learning; Bioenergy crops
License CC BY
Funder U.S. Department of Energy (DOE) - Grant: DE-SC0018420
Corresponding Creator Sebastian Varela Quintela
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Version DOI Comment Publication Date
1 10.13012/B2IDB-0353090_V1 2021-05-10
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update: {"subject"=>["", "Life Sciences"]} 2021-05-11T00:51:59Z