Full TGIF Record # 310251
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Web URL(s):https://scisoc.confex.com/crops/2019am/meetingapp.cgi/Paper/121846
    Last checked: 01/31/2020
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Publication Type:
Content Type:Abstract or Summary only
Author(s):Baldwin, Christian M.; Koehler, John F.; Yendrick, Craig; Sullivan, Dana; McCall, David S.; Clady, Ross; Burrell, Samantha
Author Affiliation:Baldwin: Scotts Co., Marysville, OH; Koehler, Yendrick, Clady and Burrell: Scotts Miracle-Gro Company, Marysville, OH; Sullivan: TurfScout, LLC., Greensboro, NC; McCall: School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA
Title:Drones and data collection: Generating powerful, data driven decisions in a more efficient and economical manner
Section:C05 turfgrass science
Other records with the "C05 turfgrass science" Section

Turf ecology and management oral III: Culture, stress & rhizosphere ecology
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Meeting Info.:San Antonio, Texas: November 10-13, 2019
Source:ASA, CSSA and SSSA International Annual Meetings. 2019, p. 121846.
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: Fertilization rates; Image analysis; Methodology; Polymer-coated urea; Remote sensing; Spectral analysis; Unmanned aerial vehicle
Abstract/Contents:"Remote sensing methods, such as digital image analysis (DIA), have been universally adopted as a standard method for quantifying turfgrass color. However, advances in drone technology could collect similar data in a more efficient manner. To investigate the accuracy of spectral imaging using drone technology, ground truth data (visual and DIA) was correlated with spectral image analysis data on a polymer-coated urea fertilizer rate titration trial. Polymer coated-urea fertilizer with different release profiles (45-d, 90-d, and 120-d) were applied on 1 May 2018 at rates of 0, 24, 49, 74, and 98 kg/ha on Kentucky bluegrass maintained at a 1.3cm. Ground truth data included DIA and visual color (1-9 scale). Spectral image analysis occurred from images collected following a drone flight. All data was collected on 12 June 2018. When correlating data from the drone image to DIA and visual data, an r2 of 0.64 and 0.82 was noted, respectively. Also, similar means separations were observed for all data collection methods. Based on the data collected for this trial, spectral image analysis data generated using drone technology was able to produce similar data compared to more time consuming ground truth methods. Additional trials currently being evaluated with drone technology include turfgrass breeder screening nurseries, garden flower counts/plant size, and weed identification in turf."
Note:This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Baldwin, C. M., J. F. Koehler, C. Yendrick, D. Sullivan, D. S. McCall, R. Clady, et al. 2019. Drones and data collection: Generating powerful, data driven decisions in a more efficient and economical manner. Agron. Abr. p. 121846.
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    Last checked: 01/31/2020
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