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Spectral analysis and mapping of blackgrass weed by leveraging machine learning and UAV multispectral imagery

dc.contributor.authorSu, Jinya
dc.contributor.authorYi, Dewei
dc.contributor.authorCoombes, Matthew
dc.contributor.authorLiu, Cunjia
dc.contributor.authorZhai, Xiaojun
dc.contributor.authorMcDonald-Maier, Klaus
dc.contributor.authorChen, Wen Hua
dc.date.accessioned2023-07-25T23:07:13Z
dc.date.available2023-07-25T23:07:13Z
dc.date.issued2022-01-01
dc.identifier209761351
dc.identifier652b40e8-a3a5-462d-a5db-8944ed8bb8d5
dc.identifier85121118573
dc.identifier85121118573
dc.identifier.citationSu , J , Yi , D , Coombes , M , Liu , C , Zhai , X , McDonald-Maier , K & Chen , W H 2022 , ' Spectral analysis and mapping of blackgrass weed by leveraging machine learning and UAV multispectral imagery ' , Computers and Electronics in Agriculture , vol. 192 , 106621 . https://doi.org/10.1016/j.compag.2021.106621en
dc.identifier.issn0168-1699
dc.identifier.otherORCID: /0000-0003-1702-9136/work/106597852
dc.identifier.otherORCID: /0000-0002-3121-7208/work/112496143
dc.identifier.urihttp://aura-test.abdn.ac.uk/handle/2164/19678
dc.descriptionAcknowledgements This work was supported by Science and Technology Facilities Council (STFC) with grant numbers ST/N006852/1 and ST/V00137X/1.en
dc.format.extent11
dc.format.extent3471628
dc.language.isoeng
dc.relation.ispartofComputers and Electronics in Agricultureen
dc.subjectBlackgrass weeden
dc.subjectGuided filteren
dc.subjectRandom foresten
dc.subjectSpectral Index (SI)en
dc.subjectunmanned aerial vehicle (UAV)en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectForestryen
dc.subjectAgronomy and Crop Scienceen
dc.subjectComputer Science Applicationsen
dc.subjectHorticultureen
dc.subjectSTFC - Science and Technology Facilities Councilen
dc.subjectST/N006852/1en
dc.subjectST/V00137X/1en
dc.subject.lccQA75en
dc.titleSpectral analysis and mapping of blackgrass weed by leveraging machine learning and UAV multispectral imageryen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.description.statusPeer revieweden
dc.identifier.doihttps://doi.org/10.1016/j.compag.2021.106621
dc.date.embargoedUntil2022-12-05
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85121118573&partnerID=8YFLogxKen
dc.identifier.vol192en


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