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dc.contributor.authorGong, Liyun
dc.contributor.authorYu, Miao
dc.contributor.authorJiang, Shouyong
dc.contributor.authorCutsuridis, Vassilis
dc.contributor.authorPearson, Simon
dc.date.accessioned2021-11-18T21:51:00Z
dc.date.available2021-11-18T21:51:00Z
dc.date.issued2021-07-01
dc.identifier.citationGong , L , Yu , M , Jiang , S , Cutsuridis , V & Pearson , S 2021 , ' Deep learning based prediction on greenhouse crop yield combined TCN and RNN ' , Sensors , vol. 21 , no. 13 , 4537 . https://doi.org/10.3390/s21134537en
dc.identifier.issn1424-8220
dc.identifier.otherPURE: 208737979
dc.identifier.otherPURE UUID: 1c13ec97-8ab8-4f60-ae94-322647698638
dc.identifier.otherScopus: 85108866960
dc.identifier.otherPubMed: 34283083
dc.identifier.urihttps://hdl.handle.net/2164/17518
dc.descriptionFunding: This research was supported as part of SMARTGREEN, an Interreg project supported by the North Sea Programme of the European Regional Development Fund of the European Union.en
dc.format.extent16
dc.language.isoeng
dc.relation.ispartofSensorsen
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en
dc.subjectCrop yield predictionen
dc.subjectDeep learningen
dc.subjectGreenhouseen
dc.subjectRecurrent neural network (RNN)en
dc.subjectTemporal convolutional network (TCN)en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectAnalytical Chemistryen
dc.subjectInformation Systemsen
dc.subjectAtomic and Molecular Physics, and Opticsen
dc.subjectBiochemistryen
dc.subjectInstrumentationen
dc.subjectElectrical and Electronic Engineeringen
dc.subjectEuropean Commissionen
dc.subject.lccQA75en
dc.titleDeep learning based prediction on greenhouse crop yield combined TCN and RNNen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.3390/s21134537
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85108866960&partnerID=8YFLogxKen
dc.identifier.vol21en
dc.identifier.iss13en


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