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| Web URL(s): | https://scisoc.confex.com/scisoc/2018am/meetingapp.cgi/Paper/112336
Last checked: 11/12/2018 |
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| Publication Type:
| Report |
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| Content Type: | Abstract or Summary only |
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| Author(s): | Zhang, Jing;
Virk, Simerjeet;
Porter, Wesley M.;
Kenworthy, Kevin E.;
Schwartz, Brian M. |
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| Author Affiliation: | Zhang: Department of Agronomy, University of Florida West Florida Research & Education Center, Tifton, GA; Virk: Crop and Soil Sciences, University of Georgia, Tifton, GA; Porter: University of Georgia - Tifton, Tifton, GA; Kenworthy: Agronomy Department, University of Florida, Gainesville, FL; Schwartz: Department of Crop and Soil Sciences, University of Georgia - Tifton, Tifton, GA |
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| Title: | Application of unmanned aerial vehicle based imagery in turfgrass variety trials |
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| Section: | C05 turfgrass science
Other records with the "C05 turfgrass science" Section
Turf environmental science, rhizosphere ecology, and water oral
Other records with the "Turf environmental science, rhizosphere ecology, and water oral" Section
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| Meeting Info.: | Baltimore, Maryland: November 4-7, 2018 |
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| Source: | ASA, CSSA and SSSA International Annual Meetings. 2018, p. 112336. |
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| Publishing Information: | [Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Canadian Society of Agronomy] |
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| # of Pages: | 1 |
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| Keywords: | TIC Keywords: Evaluations; Photography; Unmanned aerial vehicles; Variety trials
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| Abstract/Contents: | "Recent progress in remote sensing technology Unmanned Aerial Vehicle (UAV) systems, provide opportunities for turfgrass breeders to collect more comprehensive data during early stages of selection and in advanced trials. The goal of this study was to assess the use of UAV-based imagery on replicated turfgrass field trials with small plot sizes. Both visual (RGB) images and multispectral images were acquired with a UAV platform on field trials of bermudagrass (Cynodon dactylon L.) and zoysiagrass (Zoysia spp.) with plot sizes of 1.8 by 1.8 m and 0.9 by 0.9 m, respectively. Color indices were calculated from the data extracted from UAV-based RGB images and vegetation indices were calculated from the data extracted from UAV-based multispectral images. Ground truth measurements including visual turfgrass quality (TQ), percent green cover (PGC), and normalized difference vegetation index (NDVI) were taken immediately following the flight. Ground NDVI can be predicted using UAV-based NDVI (R2=0.90, RMSE = 0.03). Both visible atmospherically resistant index (VARI) and NDVI can be used to predict ground PGC (R2= 0.69 - 0.89, RMSE = 6.94 - 8.24%), warranting the use of the more affordable RGB camera in estimating ground PGC. There are 100% and 80% overlaps in identifying the top five bermudagrass entries and the top ten zoysiagrass entries respectively using ground measurements and UAV-based measurements. The results suggest that UAV-based high-resolution imagery is a useful tool for assessing turfgrass performance during variety trials." |
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| Language: | English |
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| References: | 0 |
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| Note: | This item is an abstract only!
"50-2" |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Zhang, J., S. Virk, W. M. Porter, K. E. Kenworthy, and B. M. Schwartz. 2018. Application of unmanned aerial vehicle based imagery in turfgrass variety trials. Agron. Abr. p. 112336. |
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Last checked: 11/12/2018 |
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