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dc.contributor.authorOnoufriou, George
dc.contributor.authorHanheide, Marc
dc.contributor.authorLeontidis, Georgios
dc.date.accessioned2022-11-24T20:49:02Z
dc.date.available2022-11-24T20:49:02Z
dc.date.issued2022-11-15
dc.identifier.citationOnoufriou , G , Hanheide , M & Leontidis , G 2022 ' Premonition Net, A Multi-Timeline Transformer Network Architecture Towards Strawberry Tabletop Yield Forecasting ' ArXiv . https://doi.org/10.48550/arXiv.2211.08177en
dc.identifier.otherPURE: 221941903
dc.identifier.otherPURE UUID: 670a4e44-7565-4ce3-8af2-ff792fa6d33f
dc.identifier.otherORCID: /0000-0001-6671-5568/work/123848134
dc.identifier.urihttps://hdl.handle.net/2164/19603
dc.descriptionFunding statement: This research was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) studentship [grant numbers 2155898, BB/S507453/1]. The authors would also like to thank the Lincoln Centre for Autonomous Systems (L-CAS) for their help and support with the autonomous robotic Thorvald systems, and the availability of tools and resources such as the Riseholme strawberry tabletop itseof.en
dc.format.extent9
dc.language.isoeng
dc.publisherArXiv
dc.rightshttps://creativecommons.org/licenses/by-sa/4.0/en
dc.subjectSDG 9 - Industry, Innovation, and Infrastructureen
dc.subjectSDG 2 - Zero Hungeren
dc.subject2040 Data and Artificial Intelligenceen
dc.subjectDeep Learningen
dc.subjectStrawberry Yield Forecastingen
dc.subjectTime Seriesen
dc.subjectTransformersen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectBiotechnology and Biological Sciences Research Council (BBSRC)en
dc.subject2155898en
dc.subjectBB/S507453/1en
dc.subject.lccQA75en
dc.titlePremonition Net, A Multi-Timeline Transformer Network Architecture Towards Strawberry Tabletop Yield Forecastingen
dc.typeWorking or discussion paperen
dc.contributor.institutionUniversity of Aberdeen.Vice Principalsen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.description.versionPreprinten
dc.identifier.doihttps://doi.org/10.48550/arXiv.2211.08177


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