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dc.contributor.authorOnoufriou, George
dc.contributor.authorHanheide, Marc
dc.contributor.authorLeontidis, Georgios
dc.date.accessioned2021-04-01T12:41:01Z
dc.date.available2021-04-01T12:41:01Z
dc.date.issued2020-04-17
dc.identifier.citationOnoufriou , G , Hanheide , M & Leontidis , G 2020 , ' The Augmented Agronomist Pipeline and Time Series Forecasting ' , 3rd UK Robotics & Autonomous Systems Conference (UK-RAS) , 17/04/20 - 17/04/20 . https://doi.org/10.31256/Qm1Fu7Len
dc.identifier.citationconferenceen
dc.identifier.otherPURE: 161109993
dc.identifier.otherPURE UUID: a62d862b-e98a-4db1-8c53-c61383735d8d
dc.identifier.otherORCID: /0000-0001-6671-5568/work/91891222
dc.identifier.urihttps://hdl.handle.net/2164/16168
dc.format.extent3
dc.language.isoeng
dc.subjectMachine Learningen
dc.subjectRoboticsen
dc.subjectAGRICULTUREen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccQA75en
dc.titleThe Augmented Agronomist Pipeline and Time Series Forecastingen
dc.typeConference posteren
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Computer Science and Informaticsen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.description.statusPeer revieweden
dc.description.versionPostprinten
dc.identifier.doihttps://doi.org/10.31256/Qm1Fu7L
dc.identifier.urlhttps://www.ukras.org/publications/ras-proceedings/UKRAS20/en


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