TY - JOUR
T1 - UAS-based plant phenotyping for research and breeding applications
AU - Guo, Wei
AU - Carroll, Matthew E.
AU - Singh, Arti
AU - Swetnam, Tyson L.
AU - Merchant, Nirav
AU - Sarkar, Soumik
AU - Singh, Asheesh K.
AU - Ganapathysubramanian, Baskar
N1 - Funding Information:
We thank all members of the ISU’s Soynomics team for their feedback on this work. We also thank all technical specialists of the Institute for Sustainable Agro-ecosystem Services, University of Tokyo. This work was partially supported by the Iowa Soybean Association (AS and AKS); the Plant Sciences Institute (BG, AKS, and SS); the Bayer Chair in Soybean Breeding (AKS); the R.F. Baker Center for Plant Breeding (AKS); the USDA National Institute of Food and Agriculture (NIFA) Food and Agriculture Cyberinformatics Tools (FACT) (award 2019-67021-29938) (AS, BG, SS, AKS, and NM); the NSF (S&CC-1952045) (AKS and SS); the USDA-CRIS (IOW04714) project (AKS and AS); the NSF (DBI-1265383) and (DBI-1743442) CyVerse (TS, NM); and the USDA NIFA (awards 2020-67021-31528 and 2020-68013-30934) (BG). This work was also supported by the CREST Program (JPMJCR1512) and the SICORP Program (JPMJSC16H2) (WG) of the Japan Science and Technology Agency, Japan.
Funding Information:
The landscape of service providers that offer turnkey solutions is evolving rapidly (Table 4); at the same time, academic groups are producing ready-to-use open-source anal- ysis workflows powered by deep learning methods [117]. Having a responsive cyberinfrastructure that can effectively leverage both commercial and academic offerings, while scaling (up and down) as the needs of the project evolve is paramount. Supported research cyberinfrastructures (in the US), like NSF CyVerse [118], XSEDE [119], and OpenScienceGrid [120], support the processing and hosting of nationally funded US-based research. Commercial cloud-based turnkey solutions for UAS data management, analysis, and team-based collaboration provide easy-to-use integrated viewers, applications, and app stores (Table 4). Many of these offerings have limits on allowable storage per tier and may not be ideal for a large long-term archival storage. Commercial cloud providers (for example, AWS, Google, and Azure) provide services for managing data through tiered storage and lifecycle management (highly redundant to slower long-term archival). This allows data to migrate from various tiers in an automated and cost-effective manner, and these capabilities can complement local IT resources, when feasible [121–123]. However, institutions may have restrictions on the use of some services and platforms, and this needs to be determined at the planning stage of experiments.
Publisher Copyright:
Copyright © 2021 Wei Guo et al.
PY - 2021/6/10
Y1 - 2021/6/10
N2 - Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.
AB - Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.
UR - http://www.scopus.com/inward/record.url?scp=85110017244&partnerID=8YFLogxK
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U2 - 10.34133/2021/9840192
DO - 10.34133/2021/9840192
M3 - Article
AN - SCOPUS:85110017244
SN - 2643-6515
VL - 2021
JO - Plant Phenomics
JF - Plant Phenomics
M1 - 9840192
ER -