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dc.contributor.authorTakaya, Kosukeen
dc.contributor.authorSasaki, Yuen
dc.contributor.authorIse, Takeshien
dc.contributor.alternative高屋, 浩介ja
dc.contributor.alternative佐々木, 優ja
dc.contributor.alternative伊勢, 武史ja
dc.date.accessioned2022-08-09T00:03:44Z-
dc.date.available2022-08-09T00:03:44Z-
dc.date.issued2022-
dc.identifier.urihttp://hdl.handle.net/2433/275806-
dc.description.abstractMonitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost.en
dc.language.isoeng-
dc.publisherJapanese Society of Breedingen
dc.publisher.alternative日本育種学会ja
dc.rights© 2022 by JAPANESE SOCIETY OF BREEDINGen
dc.subjectdeep learningen
dc.subjectchopped picture methoden
dc.subjectalien planten
dc.subjectSolidago altissimaen
dc.subjectaction cameraen
dc.subjectcitizen scienceen
dc.subjectcomputer visionen
dc.titleAutomatic detection of alien plant species in action camera images using the chopped picture method and the potential of citizen scienceen
dc.typejournal article-
dc.type.niitypeJournal Article-
dc.identifier.jtitleBreeding Scienceen
dc.identifier.volume72-
dc.identifier.issue1-
dc.identifier.spage96-
dc.identifier.epage106-
dc.relation.doi10.1270/jsbbs.21062-
dc.textversionpublisher-
dc.identifier.pmid36045894-
dcterms.accessRightsopen access-
dc.identifier.pissn1344-7610-
dc.identifier.eissn1347-3735-
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