Full TGIF Record # 333562
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Web URL(s):https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/150713
    Last checked: 12/08/2023
Publication Type:
Content Type:Abstract or Summary only
Author(s):Torres, Ubaldo; Martin, Daniel; Gurjar, Bholuram; Sapkota, Bishwa; Osburn, Andrew; Straw, Chase M.; Bagavathiannan, Muthukumar V.
Author Affiliation:Torres: Presenting Author and Texas A&M University, College Station, TX; Martin: ARS, United States Department of Agriculture, College Station, TX; Gurjar: Texas A&M University, College Station, TX; Sapkota: College Station, TX; Osburn: Department of Soil and Crop Sciences, College Station, TX; Straw: Horticultural Science, Texas A&M University, College Station, TX; Bagavathiannan: Soil and Crop Sciences, Texas A&M University, College Station, TX
Title:UAS-based detection and site-specific treatment of green kyllinga in turf
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C05 turfgrass science
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Meeting Info.:St. Louis, Missouri: October 29-November 1, 2023
Source:ASA, CSSA, SSSA International Annual Meeting. 2023, p. 150713.
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
# of Pages:1
Abstract/Contents:"Green kyllinga (Kyllinga brevifolia) is a common and problematic weed to control in managed turfgrass systems. The growth pattern and morphology of this species create inconsistent appearance and rough surface characteristics in golf courses. Once established, green kyllinga may spread rapidly, making it difficult to control. Site-specific treatment using a unmanned aerial systems (UAS) -based approach may offer a solution for controlling green kylllinga. Aerial images can be employed for weed detection and subsequent treatment using the drone sprayer. An experiment was conducted in 2021 at Briarcrest Golf Course in Bryan, TX to investigate two objectives: 1) detect green kyllinga from UAS-based aerial RGB imagery, and 2) evaluate the effectiveness of using a drone sprayer for site-specific herbicide treatment of green kyllinga. Treatments included 1) a drone-based herbicide application with weeds located using RTK-GPS, 2) a drone-based herbicide application with weeds located based on image analysis, 3) herbicide application using a backpack sprayer, and 4) an untreated check. Sulfosulfuron (Certainty®) herbicide was used in the experiment for the aerial and ground applications. First, the accuracy of the drone spray targeting green kyllinga patches based on coordinates obtained through manual recording was evaluated. Additionally, visible weed injury was recorded at 4 weeks after treatments, and efficacy between the drone spray treatments and conventional backpack application were compared. The overall accuracy of green kyllinga detection using image analysis was 78%. Compared to backpack applications, the efficacy of aerial applications based on weeds detected using image analysis was 12% lower. No differences were observed between the backpack and the drone-based application with weeds located manually, suggesting that further improvements are necessary with the weed detection model. This experiment provided necessary directions for improving detection accuracy of green kyllinga, and demonstrates the promise of using drone sprayers for site-specific weed management in turfgrass systems."
Note:This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Torres, U., D. Martin, B. Gurjar, B. Sapkota, A. Osburn, C. M. Straw, et al. 2023. UAS-based detection and site-specific treatment of green kyllinga in turf. Agron. Abr. p. 150713.
Fastlink to access this record outside TGIF: https://tic.msu.edu/tgif/flink?recno=333562
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    Last checked: 12/08/2023
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