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| Web URL(s): | https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/152319
Last checked: 12/04/2023 |
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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;
Maleski, Jerome;
Khanal, Sameer;
Webb, Amanda;
Schwartz, Brian M.;
Milla-Lewis, Susana R.;
Patton, Aaron J.;
Huang, Bingru;
Jespersen, David |
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| Author Affiliation: | Zhang: Presenting Author and Georgia, University of Georgia-Tifton, Tifton, GA; Maleski: University of Georgia-Tifton, Tifton, GA; Khanal: Crop and Soil Science, University of Georgia, Athens, GA; Webb: University of Georgia-Tifton, Tifton, GA; Schwartz: Department of Crop and Soil Sciences, University of Georgia, Tifton, GA; Milla-Lewis: Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC; Patton: Purdue University, West Lafayette, IN; Huang: Rutgers University, New Brunswick, NJ; Jespersen: Crop and Soil Sciences, University of Georgia-Griffin, Griffin, GA |
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| Title: | Assessing drought responses of a zoysiagrass mapping population using UAS-based RGB and hyperspectral imaging |
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| Section: | Turfgrass breeding, genetic, molecular biology, microbiome oral II
Other records with the "Turfgrass breeding, genetic, molecular biology, microbiome oral II" Section
C05 turfgrass science
Other records with the "C05 turfgrass science" Section
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| Meeting Info.: | St. Louis, Missouri: October 29-November 1, 2023 |
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| Source: | ASA, CSSA, SSSA International Annual Meeting. 2023, p. 152319. |
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| Publishing Information: | [Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America] |
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| # of Pages: | 1 |
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| Abstract/Contents: | "Unmanned Aerial Systems (UAS) based high-throughput phenotyping in turfgrass has greatly enhanced the capability of turfgrass breeders to evaluate large number of plant materials in field trials. Standard workflows including RGB and multispectral images were developed. Normalized vegetation indices such as NDVI and NDRE are widely used for selection. However, they are not sensitive to moderate drought stresses. Hyperspectral images have potential to offer insight related to physiological responses. The objectives of the study were to 1) study the change of spectral reflectance during soil drying in a zoysiagrass mapping population using hyperspectral images; 2) evaluate the use of several vegetation indices in the field trials including the two previously reported to be better correlated with relative water content; 3) compare the selections made based on RGB and hyperspectral images. Both UAS-based RGB and hyperspectral images were collected in 2023 and images were processed using previously developed workflows. Color index related traits were extracted from RGB images and the data from non-drought and drought dates were subjected to K-Means clustering to select the superior lines. Spectral reflectance (400-1000 nm) at the plot level was extracted from the mapping population. As drought stress progresses, reflectance on the red region increased and the reflectance on the near infrared region decreased in zoysiagrass. Normalized ratio between green and red had greater change in a selected drought resistant genotype under well-watered and drought conditions. Photochemical reflectance index and plant senescence reflectance index seemed to be more sensitive to drought stress by having a larger CV (18%-36%) than other indices (1%-23%) and visual turfgrass quality (18%-19%) in the mapping population. Selections made based on RGB and hyperspectral images align with breeders selection. UAS-based hyperspectral images would be great use in not only turfgrass breeding but also genomics studies." |
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| Language: | English |
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| References: | 0 |
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| Note: | "302-1"
This item is an abstract only! |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Zhang, J., J. Maleski, S. Khanal, A. Webb, B. M. Schwartz, S. R. Milla-Lewis, et al. 2023. Assessing drought responses of a zoysiagrass mapping population using UAS-based RGB and hyperspectral imaging. Agron. Abr. p. 152319. |
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| Web URL(s):
https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/152319
Last checked: 12/04/2023 |
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