Alhnaity, BasharPearson, SimonLeontidis, GeorgiosKollias, Stefanos2021-04-012021-04-012020-11-23Alhnaity, 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.2020.1296.550567-75722406-61680567-7572ORCID: /0000-0001-6671-5568/work/91891226https://hdl.handle.net/2164/16174Funding 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.7508485engDeep learningFicusGrowthPredictionRecurrent LSTM neural networksStem diameterTomatoYield rateQA75 Electronic computers. Computer scienceHorticultureQA75Conference item10.17660/ActaHortic.2020.1296.55http://www.scopus.com/inward/record.url?scp=85097321834&partnerID=8YFLogxKhttps://arxiv.org/abs/1907.00624