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Sci."},{"key":"10.1016\/j.ecoinf.2025.103495_bb0395","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1676\/14-138.1","article-title":"Flight feather molt in yellow-headed blackbirds (Xanthocephalus xanthocephalus) in North Dakota","volume":"127","author":"Twedt","year":"2015","journal-title":"Wilson J. Ornithol."},{"issue":"6407","key":"10.1016\/j.ecoinf.2025.103495_bb0400","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1126\/science.aat7526","article-title":"A continental system for forecasting bird migration","volume":"361","author":"Van Doren","year":"2018","journal-title":"Science"},{"key":"10.1016\/j.ecoinf.2025.103495_bb0405","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.2193\/0091-7648(2005)33[1282:EOAAFD]2.0.CO;2","article-title":"Efficacy of an animal-activated frightening device on urban elk and mule deer","volume":"33","author":"VerCauteren","year":"2005","journal-title":"Wildl. Soc. Bull."},{"key":"10.1016\/j.ecoinf.2025.103495_bb0410","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.cropro.2018.11.008","article-title":"Evaluation of two unmanned aircraft systems as tools for protecting crops from blackbird damage","volume":"117","author":"Wandrie","year":"2019","journal-title":"Crop Prot."},{"key":"10.1016\/j.ecoinf.2025.103495_bb0415","series-title":"Proceedings of the 12th Asian Control Conference (ASCC)","first-page":"108","article-title":"Autonomous pest bird deterring for agricultural crops using teams of unmanned aerial vehicles","author":"Wang","year":"2019"},{"key":"10.1016\/j.ecoinf.2025.103495_bb0420","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.cropro.2020.105260","article-title":"Bird damage management in vineyards: comparing efficacy of a bird psychology-incorporated unmanned aerial vehicle system with netting and visual scaring","volume":"137","author":"Wang","year":"2020","journal-title":"Crop Prot."},{"key":"10.1016\/j.ecoinf.2025.103495_bb0425","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1111\/jav.00619","article-title":"Low-budget ready-to-fly unmanned aerial vehicles: An effective tool for evaluating the nesting status of canopy-breeding bird species","volume":"46","author":"Weissensteiner","year":"2015","journal-title":"J. Avian Biol."},{"key":"10.1016\/j.ecoinf.2025.103495_bb0430","series-title":"An Evaluation of the Social Perceptions and Biological Efficacy of Unmanned Aircraft Systems for Avian Agriculture Conflict, Environmental and Conservation Sciences (Biological Sciences)","first-page":"83","author":"White","year":"2021"},{"key":"10.1016\/j.ecoinf.2025.103495_bb0435","doi-asserted-by":"crossref","first-page":"WR24066","DOI":"10.1071\/WR24066","article-title":"Establishing protocols to apply repellents while hazing crop pests: importance of habitat, flock size, and time on blackbird (Icteridae) responses to a drone capable of spraying","volume":"52","author":"White","year":"2025","journal-title":"Wildl. Res."},{"key":"10.1016\/j.ecoinf.2025.103495_bb0440","first-page":"3","article-title":"Ecology, bioenergetics, and agricultural impacts of a winter-roosting population of blackbirds and starlings","volume":"93","author":"White","year":"1985","journal-title":"Wildl. Monogr."},{"key":"10.1016\/j.ecoinf.2025.103495_bb0445","doi-asserted-by":"crossref","first-page":"5504","DOI":"10.1080\/01431161.2017.1390621","article-title":"Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests \u2013 preliminary results","volume":"39","author":"Witczuk","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.ecoinf.2025.103495_bb0450","doi-asserted-by":"crossref","first-page":"3072","DOI":"10.1038\/s41467-023-38901-y","article-title":"Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape","volume":"14","author":"Wu","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2025.103495_bb0455","doi-asserted-by":"crossref","first-page":"4858","DOI":"10.3390\/s24154858","article-title":"Hp-YOLOv8: high-precision small object detection algorithm for remote sensing images","volume":"24","author":"Yao","year":"2024","journal-title":"Sensors"},{"issue":"3","key":"10.1016\/j.ecoinf.2025.103495_bb0460","first-page":"7","article-title":"Diagnostic checking in regression relationships","volume":"2","author":"Zeileis","year":"2002","journal-title":"R News"}],"container-title":["Ecological Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1574954125005047?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1574954125005047?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T10:35:53Z","timestamp":1773311753000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1574954125005047"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":92,"alternative-id":["S1574954125005047"],"URL":"https:\/\/doi.org\/10.1016\/j.ecoinf.2025.103495","relation":{},"ISSN":["1574-9541"],"issn-type":[{"value":"1574-9541","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning to detect, classify, and count blackbirds damaging agriculture using drone-based imagery: Supporting AI-driven automation for deployment of damage management tools","name":"articletitle","label":"Article Title"},{"value":"Ecological Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ecoinf.2025.103495","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103495"}}