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Deep learning based prediction on greenhouse crop yield combined TCN and RNN

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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.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.date.accessioned2021-11-18T21:51:00Z
dc.date.available2021-11-18T21:51:00Z
dc.date.issued2021-07-01
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.description.statusPeer revieweden
dc.format.extent16
dc.format.extent1184644
dc.identifier208737979
dc.identifier1c13ec97-8ab8-4f60-ae94-322647698638
dc.identifier85108866960
dc.identifier34283083
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.doi10.3390/s21134537
dc.identifier.iss13en
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/2164/17518
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85108866960&partnerID=8YFLogxKen
dc.identifier.vol21en
dc.language.isoeng
dc.relation.ispartofSensorsen
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

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