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| Web URL(s): | https://scisoc.confex.com/scisoc/2018am/meetingapp.cgi/Paper/113668
Last checked: 11/14/2018
Requires: JavaScript |
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| Publication Type:
| Report |
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| Content Type: | Abstract or Summary only |
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| Author(s): | Hong, Mu;
Bremer, Dale J.;
van der Merwe, Deon |
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| Author Affiliation: | Hong: Kansas State University, Manhattan, KS; Bremer: Horticulture and Natural Resources, Kansas State University, Manhattan, KS; van der Merwe: Farm Animal Health, University of Utrecht, Utrecht, Netherlands |
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| Title: | Evaluating small unmanned aerial systems for detecting drought stress on turfgrass |
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| Section: | C05 turfgrass science
Other records with the "C05 turfgrass science" Section
Turf ecology and management I: Physiology, irrigation, and abiotic stress oral (includes student competition)
Other records with the "Turf ecology and management I: Physiology, irrigation, and abiotic stress oral (includes student competition)" 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. 113668. |
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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: Drought resistance; Equipment evaluation; Problem diagnosis; Unmanned aerial vehicles
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| Abstract/Contents: | "Recent advances in small unmanned aerial systems (sUAS) may provide rapid and accurate methods for turf research and management. The study was to evaluate early drought detection ability of ultra-high resolution remote sensing with sUAS technology, and compare it with traditional techniques on fairway-height 'Declaration' creeping bentgrass (Agrostis stolonifera L.) treated from severe deficit to well-watered irrigation (15, 30, 50, 65, 80, and 100% evapotranspiration replacement). Airborne measurements with a modified digital camera mounted on a hexacopter included reflectance from broad bands (near infrared [NIR, 680- 780 nm], and green and blue bands [400-580 nm]), from which eight vegetation indices (VIs) were derived for evaluation. Canopy temperature was measured only for the final year with a thermal infrared camera mounted on drone. Traditional measurements were volumetric water content (VWC), visual quality (VQ), percentage green cover (PGC), and VIs from handheld devices. Declines in VWC in irrigation-deficit treatments were consistently detected by the NIR band and six VIs from sUAS, and NDVI, NIR and red band from a handheld device, before drought stress was evident in VQ and PGC. These bands and indices predicted drought stress at least one week before symptoms appeared in VQ and PGC. Canopy temperature predicted drought stress as early as VIs, demonstrating its ability to predict drought stress. For sUAS, only the NIR and GreenBlue VI [(green-blue)/(green+blue)] consistently predicted drought stress throughout three years. For the handheld device, NDVI and red band predicted drought events earlier than NIR. Results indicate using ultra-high resolution remote sensing with sUAS can detect drought stress as well as handheld device before it is visible to the human eye and may prove viable for irrigation management on turfgrass." |
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| Language: | English |
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| References: | 0 |
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| See Also: | See also related article, "Thermal imaging detects early drought stress in turfgrass utilizing small unmanned aircraft systems" in Agrosystems, Geosciences & Environment, 2(1) 2019, p. 190028 [1-9], R=326451. R=326451
See also related article, "Thermal imaging detects early drought stress in turfgrass utilizing small unmanned aircraft systems" in 2017 Turfgrass Research: Research Reports [Kansas State University], 5(5) 2019, p. [1-6], R=311517. R=311517
See also related article, "Evaluating small unmanned aerial systems for detecting drought stress on turfgrass" in Turfgrass and Environmental Research Program: 2018 Research Summaries, p. 206-212, R=304998. R=304998 |
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| Note: | This item is an abstract only!
"142-6" |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Hong, M., D. J. Bremer, and D. van der Merwe. 2018. Evaluating small unmanned aerial systems for detecting drought stress on turfgrass. Agron. Abr. p. 113668. |
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Last checked: 11/14/2018
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