TY - GEN
T1 - TERRA-REF data processing infrastructure
AU - Burnette, Maxwell
AU - Rohde, Gareth S.
AU - Fahlgren, Noah
AU - Sagan, Vasit
AU - Sidike, Paheding
AU - Kooper, Rob
AU - Terstriep, Jeffrey A.
AU - Mockler, Todd
AU - Andrade-Sanchez, Pedro
AU - Ward, Rick
AU - Maloney, J. D.
AU - Willis, Craig
AU - Newcomb, Maria
AU - Shakoor, Nadia
AU - LeBauer, David Shaner
N1 - Funding Information:
The work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000598.
Publisher Copyright:
© 2018 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
PY - 2018/7/22
Y1 - 2018/7/22
N2 - The Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) provides a data and computation pipeline responsible for collecting, transferring, processing and distributing large volumes of crop sensing and genomic data from genetically informative germplasm sets. The primary source of these data is a field scanner system built over an experimental field at the University of Arizona Maricopa Agricultural Center. The scanner uses several different sensors to observe the field at a dense collection frequency with high resolution. These sensors include RGB stereo, thermal, pulse-amplitude modulated chlorophyll fluorescence, imaging spectrometer cameras, a 3D laser scanner, and environmental monitors. In addition, data from sensors mounted on tractors, UAVs, an indoor controlled-environment facility, and manually collected measurements are integrated into the pipeline. Up to two TB of data per day are collected and transferred to the National Center for Supercomputing Applications at the University of Illinois (NCSA) where they are processed.
AB - The Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA-REF) provides a data and computation pipeline responsible for collecting, transferring, processing and distributing large volumes of crop sensing and genomic data from genetically informative germplasm sets. The primary source of these data is a field scanner system built over an experimental field at the University of Arizona Maricopa Agricultural Center. The scanner uses several different sensors to observe the field at a dense collection frequency with high resolution. These sensors include RGB stereo, thermal, pulse-amplitude modulated chlorophyll fluorescence, imaging spectrometer cameras, a 3D laser scanner, and environmental monitors. In addition, data from sensors mounted on tractors, UAVs, an indoor controlled-environment facility, and manually collected measurements are integrated into the pipeline. Up to two TB of data per day are collected and transferred to the National Center for Supercomputing Applications at the University of Illinois (NCSA) where they are processed.
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U2 - 10.1145/3219104.3219152
DO - 10.1145/3219104.3219152
M3 - Conference contribution
AN - SCOPUS:85051414571
SN - 9781450364461
T3 - ACM International Conference Proceeding Series
BT - Practice and Experience in Advanced Research Computing 2018
PB - Association for Computing Machinery
T2 - 2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018
Y2 - 22 July 2017 through 26 July 2017
ER -