Full TGIF Record # 317076
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Web URL(s):https://scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/134073
    Last checked: 03/29/2022
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Publication Type:
Content Type:Abstract or Summary only
Author(s):Henderson, Caleb A.; Haak, David; Mehl, Hillary L.; Hutchens, Wendell J.; McCall, David S.
Author Affiliation:Henderson, Hutchens, and McCall: School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA; Haak: Plant Pathology, Physiology, and Weed Science, Virginia Tech, Blacksburg, VA; Mehl: School of Plant & Environmental Sciences, Tidewater AREC, Virginia Tech, Suffolk, VA
Title:Rapid isolation of spring dead spot from aerial imagery using feature extraction techniques
Section:Turfgrass pest management oral II: Diseases (includes student competition)
Other records with the "Turfgrass pest management oral II: Diseases (includes student competition)" Section

C05 turfgrass science
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Meeting Info.:Salt Lake City, Utah: November 7-10, 2021
Source:ASA, CSSA and SSSA International Annual Meetings. 2021, p. 134073.
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
# of Pages:1
Abstract/Contents:"Turfgrass managers stand to gain a lot from advancements in precision agriculture and precision turfgrass management. While advanced methods of image analysis do exist and are able to show high accuracy in mapping pest incidence, they require access to advanced computation that is outside of the purview of many managers. Mapping of individual pests by hand is possible as well but these methods are very time consuming. This highlights the opportunity for less advanced, still accurate methods for mapping disease in turfgrass systems. To address this need, we developed a Python script that uses simple algorithms to map spring dead spot incidence in bermudagrass fairways. It does this by first attempting to remove non-turfgrass items within an image and then looking for circular patches within the remaining image. Looking at images collected across four fairways from a golf course in Virginia, we were able to determine that the program can reach accuracies of 97% when compared to hand drawn maps while reducing the treatable coverage of the fairway by over 30%. This was done while running entirely on a laptop in under five minutes for each mosaicked aerial image-set. Our computer automated detection of spring dead spot could allow turfgrass managers to more efficiently use fungicides which may be too cost-prohibitive for traditional broadcast applications."
Note:This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Henderson, C. A., D. Haak, H. L. Mehl, W. J. Hutchens, and D. S. McCall. 2021. Rapid isolation of spring dead spot from aerial imagery using feature extraction techniques. Agron. Abr. p. 134073.
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    Last checked: 03/29/2022
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