Fastlink to record 317127
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| Full TGIF Record # 317127 Item 1 of 1 |
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| Web URL(s): | https://scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/137885 Last checked: 03/31/2022 Requires: JavaScript |
| Publication Type: | Report |
| Content Type: | Abstract or Summary only |
| Author(s): | Zhang, Jing; Austin, Robert; Wang, Tianyi; Maleski, Jerome; Milla-Lewis, Susana R.; Ambika, Chandra; Moss, Justin Quetone; Wu, Yanqi; Kenworthy, Kevin E.; Raymer, Paul; Schwartz, Brian M. |
| Author Affiliation: | Zhang: University of Georgia-Tifton, Tifton, GA; Austin and Milla-Lewis: Crop and Soil Sciences, North Carolina State University, Raleigh, NC; Wang: Texas A&M AgriLife Research, Dallas, TX; Maleski: University of Georgia-Tifton, Tifton, GA; Chandra: Texas A&M AgriLife Research-Dallas, Dallas, TX; Moss: Dept. of Hortilculture and Landscape Architecture, Oklahoma State University, Stillwater, OK; Wu: Department of Plant and Soil and Sciences, Oklahoma State University, Stillwater, OK; Kenworthy: Agronomy, University of Florida, Gainesville, FL; Raymer: University of Georgia-Griffin, Griffin, GA; Schwartz: Department of Crop and Soil Sciences, University of Georgia-Tifton, Tifton, GA |
| Title: | Initiating UAS-based high-throughput phenotyping in turfgrass multi-location variety trials across southeastern U.S. |
| Section: | Molecular techniques, genetics, and turfgrass breeding oral (includes student competition) Other records with the "Molecular techniques, genetics, and turfgrass breeding oral (includes student competition)" Section C05 turfgrass science Other records with the "C05 turfgrass science" Section |
| Meeting Info.: | Salt Lake City, Utah: November 7-10, 2021 |
| Source: | ASA, CSSA and SSSA International Annual Meetings. 2021, p. 137885. |
| Publishing Information: | [Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America] |
| # of Pages: | 1 |
| Abstract/Contents: | "Turfgrass breeders are often limited in their field phenotyping capabilities due to intensive labor demands. Recent advances in Unmanned Aerial Systems (UASs) and sensor technology offer cost-effective solutions for fast, precise, and nondestructive high-throughput phenotyping. The collaborative project was initiated in 2019 to equip turfgrass breeders in the southeastern U.S. with the tools and resources to incorporate UAS-based phenotyping in their breeding programs. The objectives were to 1) establish consistent and standardized data collection workflow using UAS in collaborative turfgrass breeding programs; 2) to collect a comprehensive dataset over the multi-location variety trials. Initial setup of UAS and relevant trainings were carried out in each location with the multi-location variety trials planted in 2020. Flight protocol and process workflow were developed and image data collection started in 2020. Indices such as green leaf index (GLI), normalized difference vegetation index (NDVI), and normalized red edge index (NDRE) were used to indicate turfgrass establishment, spring green-up, and health throughout growing season in 2021. UAS-based images will continue to be collected on the multi-location variety trials regularly with the focus on turfgrass performance under drought. This dataset will provide valuable information for the turfgrass breeders to help them make decisions on advancing lines." |
| Language: | English |
| References: | 0 |
| Note: | This item is an abstract only! "281-6" Includes video, "Initiating UAS-based high-throughput phenotyping in turfgrass multi-location variety trials across southeastern U.S.", 13:43 |
| ASA/CSSA/SSSA Citation (Crop Science-like – may be incomplete): | Zhang, J., R. Austin, T. Wang, J. Maleski, S. R. Milla-Lewis, A. Chandra, et al. 2021. Initiating UAS-based high-throughput phenotyping in turfgrass multi-location variety trials across southeastern U.S.. Agron. Abr. p. 137885. |
| Fastlink to access this record outside TGIF: | http://tic.msu.edu/tgif/flink/RECNO/317127 |
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| Web URL(s) : | https://scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/137885 Last checked: 03/31/2022 Requires: JavaScript |
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