An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth

Alhnaity, BasharKollias, StefanosLeontidis, GeorgiosJiang, ShouyongSchamp, BertPearson, Simon2022-02-012022-02-012021-06-01Alhnaity, B, Kollias, S, Leontidis, G, Jiang, S, Schamp, B & Pearson, S 2021, 'An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth', Information Sciences, vol. 560, INS16166, pp. 35-50. https://doi.org/10.1016/j.ins.2021.01.0370020-0255ORCID: /0000-0001-6671-5568/work/91891228https://hdl.handle.net/2164/18007Acknowledgements 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. We would like to thank all growers (UK and EU), for providing us with the presented data sets. We also wish to thank the reviewers of the paper. Their valuable feedback, suggestions and comments helped us to improve the quality of this work.16922518engMulti-step predictionWavelet analysisDeep neural networksAttention mechanismTime-series analysisLSTMPlant growth predictionQA75 Electronic computers. Computer scienceArtificial IntelligenceComputer Science ApplicationsQA75An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growthJournal article10.1016/j.ins.2021.01.037560