Full TGIF Record # 333509
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Web URL(s):https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/150464
    Last checked: 12/06/2023
Publication Type:
Content Type:Abstract or Summary only
Author(s):Huang, Bingru; Zhang, Jing
Author Affiliation:Huang: Presenting Author and Rutgers University, New Brunswick, NJ; Zhang: Georgia, University of Georgia-Tifton, Tifton, GA
Title:Remote sensing and machine-learning guided high throughput selection and water conservation programs in turfgrass
Section:Symposium--advances in plant phenotyping and its applications
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C05 turfgrass science
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Meeting Info.:St. Louis, Missouri: October 29-November 1, 2023
Source:ASA, CSSA, SSSA International Annual Meeting. 2023, p. 150464.
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
# of Pages:1
Abstract/Contents:"Water availability for irrigation becomes increasingly limited in turfgrass management. Developing water-saving programs is critically important for the turfgrass industry. Water conservation can be achieved by selecting drought-tolerant turfgrass species/cultivars through high throughput phenotyping and precision irrigation management programs, which can be facilitated by using remote sensing and machine-learning technologies that can accurately monitor and evaluate real-time plant physiological conditions, plant phenotypic traits, soils, and environments. The decision support systems developed from remote sensing and high-throughput phenotyping data will be able to rapid selection of drought-tolerant or water-use efficient turfgrass cultivars. This presentation will discuss current research using remote sensing and machine-learning technologies that address the question of how to quickly select and efficiently breed drought-tolerant, water-saving turfgrasses and how to develop efficient water-saving irrigation programs."
This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Huang, B., and J. Zhang. 2023. Remote sensing and machine-learning guided high throughput selection and water conservation programs in turfgrass. Agron. Abr. p. 150464.
Fastlink to access this record outside TGIF: https://tic.msu.edu/tgif/flink?recno=333509
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    Last checked: 12/06/2023
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