Machine learning to detect, classify, and count blackbird…
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Show/Hide Bibliographic Information Title Statement: Machine learning to detect, classify, and count blackbirds damaging agriculture using drone-based imagery: Supporting AI-driven automation for deployment of damage management tools
transparent WS Author: Klug, Page E.
transparent Other Authors: Duttenhefner, Jessica L.
transparent ElSaid, AbdeElRahman A.
transparent Year: 2025
transparent Citation/References: Indexed by: Duttenhefner, J.L., A. A. ElSaid, and P.E. Klug. 2025. Machine learning to detect, classify, and count blackbirds damaging agriculture using drone-based imagery: Supporting AI-driven automation for deployment of damage management tools. Ecological Informatics 92:e:103495. doi: https://doi.org​/10.1016/j.ecoi​nf.2025.103495
transparent Rights: The NWRC Library has made digital copies of these published papers available on this website as part of its effort to disseminate research findings. Copies of the papers are to be used for noncommercial, personal, and educational purposes only.
transparent Affiliated Project: NWRC Blackbirds & Starlings
transparent Language: eng
transparent Type: text
transparent Description: pdf
transparent Format-Extent: 12 pages
transparent File ID No.: REP 2025-097

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Copy Call Number Location Item ID Status
1. Collapse for less details 1   Digital Collections 00030088 Online or Non-Circulating
1 Vertical Data
Collection Type: Wildlife Services Publications
Media: Text
 
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