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Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments

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dc.contributor.authorAlhnaity, Bashar
dc.contributor.authorPearson, Simon
dc.contributor.authorLeontidis, Georgios
dc.contributor.authorKollias, Stefanos
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.date.accessioned2021-04-01T20:31:01Z
dc.date.available2021-04-01T20:31:01Z
dc.date.issued2020-11-23
dc.descriptionFunding Information: This work is part of EU Interreg SMARTGREEN project (2017-2021). We would like to thank all the growers (UK & EU), for providing the data. Their valuable feedback, suggestions and comments are highly appreciated to increase the overall quality of this work.en
dc.format.extent7
dc.format.extent508485
dc.identifier163638322
dc.identifier1a2ec492-d434-4684-94c0-c165ee7c5e25
dc.identifier85097321834
dc.identifier.citationAlhnaity, B, Pearson, S, Leontidis, G & Kollias, S 2020, Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments. in Acta Horticulturae : Greensys 2019 - International Symposium on Advanced Technologies and Management for Innovative Greenhouses. vol. 1296, Acta Horticulturae, International Society for Horticultural Science, pp. 425-431. https://doi.org/10.17660/ActaHortic.2020.1296.55en
dc.identifier.doi10.17660/ActaHortic.2020.1296.55
dc.identifier.isbn0567-7572
dc.identifier.isbn2406-6168
dc.identifier.issn0567-7572
dc.identifier.otherORCID: /0000-0001-6671-5568/work/91891226
dc.identifier.urihttps://hdl.handle.net/2164/16174
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85097321834&partnerID=8YFLogxKen
dc.identifier.urlhttps://arxiv.org/abs/1907.00624en
dc.language.isoeng
dc.publisherInternational Society for Horticultural Science
dc.relation.ispartofActa Horticulturaeen
dc.relation.ispartofseriesActa Horticulturaeen
dc.subjectDeep learningen
dc.subjectFicusen
dc.subjectGrowthen
dc.subjectPredictionen
dc.subjectRecurrent LSTM neural networksen
dc.subjectStem diameteren
dc.subjectTomatoen
dc.subjectYield rateen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectHorticultureen
dc.subject.lccQA75en
dc.titleUsing Deep Learning to Predict Plant Growth and Yield in Greenhouse Environmentsen
dc.typeConference itemen

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