Full TGIF Record # 325045
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Web URL(s):https://scisoc.confex.com/scisoc/2022am/meetingapp.cgi/Paper/143302
    Last checked: 02/02/2023
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Publication Type:
Content Type:Abstract or Summary only
Author(s):Earp, Ryan; Moss, Justin Quetone; Anderson, Michael; Wu, Yanqi; Zhang, Jing
Author Affiliation:Earp: Presenting Author and Oklahoma State University; Moss, Anderson, and Wu: Oklahoma State University; Zhang: University of Georgia-Tifton
Title:Phone-app and drone-based imagery as new methods in evaluating turf coverage in advanced turf phenotyping
Section:Turfgrass Physiology and Abiotic Stress Oral (includes student competition)
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C05 turfgrass science
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Meeting Info.:Baltimore, Maryland: November 6-9, 2022
Source:ASA, CSSA, SSSA International Annual Meeting. 2022, p. 143302.
# of Pages:1
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
Abstract/Contents:"Evaluating the green coverage in turf bermudagrasses is critical in understanding how complete the canopy is and can be used in evaluating spring and fall color, turf health, and turf establishment. The traditional methods for determining turf coverage are often time and labor demanding especially as the size of the trial area increases. However, as remote sensing technologies advance and become more accessible, they will likely displace traditional method in large scale turfgrass trials for measuring turf coverage. In this trial, 85 plots of hybrid bermudagrass (Cynodon dactylon x C. transvaalensis) cultivars were evaluated weekly from planting to complete turf coverage. The percent green cover (PCG) was measured by taking images using a lightbox and calculating the number of green pixels using TurfAnalyzer. Four alternative methods to determine green coverage were also tested by using the Canopeo application, and drone imagery measuring the normalized difference vegetation index (NDVI), normalized difference red edge index, and the green leaf index. These alternative methods were used to fit a regression to the PCG for each plot using a predictive analysis. A generalized linear model using a logit link function was fitted to a random sample of the TurfAnalyzer data set for each collection method to determine its predictive ability. The results produced by the four alternative methods were significantly associated to the PCG with the R2 ranging from 0.79 to 0.91, and root mean square error (RMSE) ranged from 2.1 to 3.2. The Canopeo app resulted in the lowest RMSE, and the NDVI imaging resulted in the highest R2. This study shows there is potential opportunities in using these new methods to determine green coverage of turfgrass."
Note:This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Earp, R., J. Q. Moss, M. Anderson, Y. Wu, and J. Zhang. 2022. Phone-app and drone-based imagery as new methods in evaluating turf coverage in advanced turf phenotyping. Agron. Abr. p. 143302.
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