Full TGIF Record # 310343
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Web URL(s):https://scisoc.confex.com/scisoc/2019am/meetingapp.cgi/Paper/120429
    Last checked: 02/05/2020
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Publication Type:
Content Type:Abstract or Summary only
Author(s):Zhang, Jing; Schwartz, Brian M.; Kenworthy, Kevin E.; Wu, Yanqi; Chandra, Ambika; Milla-Lewis, Susana R.; Raymer, Paul L.
Author Affiliation:Zhang: Deparment of Agronomy, University of West Florida Research & Education Center, Tifton, GA; Schwartz: Department of Crop and Soil Sciences, University of Georgia-Tifton, Tifton, GA; Kenworthy: Agronomy, University of Floria, Gainesville, FL; Wu: Oklahoma State University, Stillwater, OK; Chandra: Soil and Crop Sciences, Texas A&M University, Dallas, TX; Milla-Lewis: Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC; Raymer: University of Georgia-Griffin, Griffin, GA
Title:Application of unmanned aerial systems based imagery and data analytics in turfgrass field trials
Section:C05 turfgrass science
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Turfgrass science poster
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Meeting Info.:San Antonio, Texas: November 10-13, 2019
Source:ASA, CSSA and SSSA International Annual Meetings. 2019, p. 120429.
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
# of Pages:1
Keywords:TIC Keywords: Aerial photography; Comparisons; Cynodon; Data processing; Equipment evaluation; Genotyping; Normalized Difference Vegetation Index; Percent living ground cover; Stenotaphrum secundatum; Unmanned aerial vehicles; Zoysia
Abstract/Contents:"Unmanned Aerial Systems (UAS) equipped with both visual and multispectral sensors have improved the efficiency of data collection in turfgrass variety trials. Previous study introduced workflow and models to predict ground-level ratings using UAS-based measurements, but whether or not the models are sensor dependent is unknown. The objectives of this research were 1) to develop a general workflow and analytical tools for integrating UAS into the evaluation of turfgrass variety trials; and 2) to compare color and vegetation indexes obtained from two UAS platforms with different cameras and sensors. Flights were conducted monthly from September 2018 to September 2019 on bermudagrass (Cynodon spp.), zoysiagrass (Zoysia spp.), and St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] variety trials. The general workflow included UAS flight (planning and conducting flights), image processing (generating orthomosaics), data acquisition (zonal statistics), data analysis (analysis of variance, multiple comparison, and genotypic ranking), and data visualization. Genotypic rankings based on turfgrass performance index, which sums up the number of times a genotype enters the top statistical group, were generated for each species. These analyses allowed the identification of treatments with high variability across replications, making it possible for the researcher to further investigate problematic plots. Genotypic rankings for each species during different time periods such as winter dormancy, spring green up, and drought were generated. In summary, regression models using two different UAS platforms to predict ground measurements such as percent green cover and NDVI were camera and sensor dependent."
Note:This item is an abstract only!
"Poster #1608"
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Zhang, J., B. M. Schwartz, K. E. Kenworthy, Y. Wu, A. Chandra, S. R. Milla-Lewis, et al. 2019. Application of unmanned aerial systems based imagery and data analytics in turfgrass field trials. Agron. Abr. p. 120429.
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