Full TGIF Record # 324954
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Web URL(s):https://scisoc.confex.com/scisoc/2022am/meetingapp.cgi/Paper/143862
    Last checked: 01/26/2023
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Publication Type:
Content Type:Abstract or Summary only
Author(s):Zhang, Jing; Maleski, Jerome; Schwartz, Brian M.; Raymer, Paul; Kenworthy, Kevin E.; Milla-Lewis, Susana R.; Chandra, Ambika; Wu, Yanqi
Author Affiliation:Zhang: Presenting Author and University of Georgia-Tifton; Maleski and Schwartz: University of Georgia-Tifton; Raymer: University of Georgia-Griffin; Kenworthy: University of Florida; Milla-Lewis: North Carolina State University; Chandra: Texas A&M AgriLife Research-Dallas; Wu: Oklahoma State University
Title:Assessing turfgrass for drought resistance using unmanned aerial system based remote sensing
Section:Turfgrass Physiology, Molecular Biology, Microbiome, and Genetics Poster (includes student competition)
Other records with the "Turfgrass Physiology, Molecular Biology, Microbiome, and Genetics Poster (includes student competition)" Section

C05 turfgrass science
Other records with the "C05 turfgrass science" Section
Meeting Info.:Baltimore, Maryland: November 6-9, 2022
Source:ASA, CSSA, SSSA International Annual Meeting. 2022, p. 143862.
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
# of Pages:1
Abstract/Contents:"Small Unmanned Aircraft Systems (UAS) has been proven to be a reliable platform for field high-throughput phenotyping in turfgrass breeding. Different sensors including RGB, multispectral, thermal, and hyperspectral cameras provide opportunities for assessing turf performance under biotic and abiotic stresses. In order to evaluate drought resistance in replicated field trials of four warm-season turfgrass species, UAS-based high-resolution imagery from different data sources were used, integrating spatial analysis. The objectives were 1) to assess and address the spatial effect of a particular field with varying elevation; 2) to conduct genotypic comparison of turf performance under drought; 3) to set up hyperspectral imaging system and collect preliminary data. Morans I test was conducted in order to determine if there is a correlation between neighboring spatial units, including characteristics relevant to plant responses and field variation. Spatial trends were modeled using linear model with block effect, row-col, exponential, gaussian, and matern structure. Spatial effect was found significant in the field and the model suitable for genotypic comparison was row column modeled as random effect. Four turf species were evaluated for their performance during drought periods in 2022 and some of the top performers were identified. Hyperspectral imaging system was set up, tested, and initial data was obtained. Pipeline for data cube curation and data mining is needed and the future work will exploit the high-resolution time series data for a set of breeding interests."
Note:This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Zhang, J., J. Maleski, B. M. Schwartz, P. Raymer, K. E. Kenworthy, S. R. Milla-Lewis, et al. 2022. Assessing turfgrass for drought resistance using unmanned aerial system based remote sensing. Agron. Abr. p. 143862.
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