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| Web URL(s): | https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/150331
Last checked: 11/30/2023 |
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| Publication Type:
| Report |
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| Content Type: | Abstract or Summary only |
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| Author(s): | Bagavathiannan, Muthukumar V. |
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| Author Affiliation: | Texas A&M University, College Station, TX |
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| Title: | Weed recognition and site-specific management in turf using digital technologies |
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| Section: | Symposium--Leveraging new technologies to improve turfgrass research
Other records with the "Symposium--Leveraging new technologies to improve turfgrass research" 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. 150331. |
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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: | "Remotely piloted aerial application systems (RPAASs) for herbicide applications are becoming more popular in the United States. These can be coupled with sensor-based recognition and mapping of weed patches for site-specific treatment. While the adoption of such digital technologies for weed control in turf systems is still in its infancy, they offer several advantages. These benefits include reduced environmental footprint from limiting herbicide use through site-specific treatments, potential for reducing the impact on non-target organisms, flexibility in operation timing, and decreased foot and vehicle traffic compared to traditional spray applications. The use of computer vision techniques and deep learning has emerged as a promising tool for weed recognition in turf, enabling targeted and efficient weed management. Combining effective weed recognition with RPAAS and/or unmanned or manned ground vehicles shows great potential for improving the current capabilities in turf weed management. However, many challenges still exist in adopting these technologies and obtaining high efficacies. Research in these topics is ongoing to provide the necessary directions for improving the application of digital technologies for weed management in turf." |
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| Language: | English |
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| References: | 0 |
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| Note: | "323-2"
This item is an abstract only! |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Bagavathiannan, M. V. 2023. Weed recognition and site-specific management in turf using digital technologies. Agron. Abr. p. 150331. |
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| Web URL(s):
https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/150331
Last checked: 11/30/2023 |
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