U.S. Department of EnergyOffice of Scientific and Technical Information
Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery
Abstract
Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this project, we used UAV-based multispectral imagery collected from MicaSense RedEdge-M on a DJI Matrice 600 Pro for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. The raw images were processed with Pix4D Mapper to create the reflectance data, and vegetation indices were calculated from a UAV-based multispectral camera. Statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potential for multiple traits associated with sustainable production of switchgrass. One statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait.
- Authors:
-
- University of Tennessee
- Publication Date:
- DOE Contract Number:
- AC05-00OR22725
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Subject:
- CBI
- OSTI Identifier:
- 1862866
- DOI:
- https://doi.org/10.25983/1862866
Citation Formats
Yaping, Xu, Yaping, Xu, Shrestha, Vivek, Piasecki, Cristiano, Wolfe, Benjamin, Hamilton, Lance, Millwood, Reginald J, Mazaraei, Mitra, and Stewart, Charles Neal. Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery. United States: N. p., 2022.
Web. doi:10.25983/1862866.
Yaping, Xu, Yaping, Xu, Shrestha, Vivek, Piasecki, Cristiano, Wolfe, Benjamin, Hamilton, Lance, Millwood, Reginald J, Mazaraei, Mitra, & Stewart, Charles Neal. Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery. United States. doi:https://doi.org/10.25983/1862866
Yaping, Xu, Yaping, Xu, Shrestha, Vivek, Piasecki, Cristiano, Wolfe, Benjamin, Hamilton, Lance, Millwood, Reginald J, Mazaraei, Mitra, and Stewart, Charles Neal. 2022.
"Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery". United States. doi:https://doi.org/10.25983/1862866. https://www.osti.gov/servlets/purl/1862866. Pub date:Tue Apr 12 20:00:00 EDT 2022
@article{osti_1862866,
title = {Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery},
author = {Yaping, Xu and Yaping, Xu and Shrestha, Vivek and Piasecki, Cristiano and Wolfe, Benjamin and Hamilton, Lance and Millwood, Reginald J and Mazaraei, Mitra and Stewart, Charles Neal},
abstractNote = {Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this project, we used UAV-based multispectral imagery collected from MicaSense RedEdge-M on a DJI Matrice 600 Pro for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. The raw images were processed with Pix4D Mapper to create the reflectance data, and vegetation indices were calculated from a UAV-based multispectral camera. Statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potential for multiple traits associated with sustainable production of switchgrass. One statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait.},
doi = {10.25983/1862866},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Apr 12 20:00:00 EDT 2022},
month = {Tue Apr 12 20:00:00 EDT 2022}
}