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| Web URL(s): | https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/150464
Last checked: 12/06/2023 |
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| Publication Type:
| Report |
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| Content Type: | Abstract or Summary only |
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| Author(s): | Huang, Bingru;
Zhang, Jing |
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| Author Affiliation: | Huang: Presenting Author and Rutgers University, New Brunswick, NJ; Zhang: Georgia, University of Georgia-Tifton, Tifton, GA |
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| Title: | Remote sensing and machine-learning guided high throughput selection and water conservation programs in turfgrass |
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| Section: | Symposium--advances in plant phenotyping and its applications
Other records with the "Symposium--advances in plant phenotyping and its applications" Section
C05 turfgrass science
Other records with the "C05 turfgrass science" Section
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| Meeting Info.: | St. Louis, Missouri: October 29-November 1, 2023 |
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| Source: | ASA, CSSA, SSSA International Annual Meeting. 2023, p. 150464. |
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| Publishing Information: | [Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America] |
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| # of Pages: | 1 |
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| 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." |
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| Language: | English |
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| References: | 0 |
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| Note: | "385-1"
This item is an abstract only! |
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| 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. |
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| Web URL(s):
https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/150464
Last checked: 12/06/2023 |
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