Full TGIF Record # 333539
Item 1 of 1
Web URL(s):https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/152574
    Last checked: 12/07/2023
Publication Type:
i
Report
Content Type:Abstract or Summary only
Author(s):Maleski, Jerome; Zhang, Jing; Vines, Phillip L.; Schwartz, Brian M.
Author Affiliation:Maleski: Presenting Author and University of Georgia-Tifton, Tifton, GA; Zhang: Georgia, University of Georgia-Tifton, Tifton, GA; Vines and Schwartz: Department of Crop and Soil Sciences, University of Georgia, Tifton, GA
Title:Computer vision for turfgrass quality assessment
Section:Turfgrass science poster
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C05 turfgrass science
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520
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Meeting Info.:St. Louis, Missouri: October 29-November 1, 2023
Source:ASA, CSSA, SSSA International Annual Meeting. 2023, p. 152574.
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
# of Pages:1
Abstract/Contents:"Computer vision is used to rate turf quality and is compared to human ratings. This tool can be combined with drone imaging flights for high throughput turf selection and ratings."
Language:English
References:0
Note:This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Maleski, J., J. Zhang, P. L. Vines, and B. M. Schwartz. 2023. Computer vision for turfgrass quality assessment. Agron. Abr. p. 152574.
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https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/152574
    Last checked: 12/07/2023
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