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dc.contributor.authorJung, Dae-Hyun-
dc.contributor.authorKim, Hyoung Seok-
dc.contributor.authorJhin, Changho-
dc.contributor.authorKim, Hak-Jin-
dc.contributor.authorPark, Soo Hyun-
dc.date.accessioned2024-01-19T17:31:49Z-
dc.date.available2024-01-19T17:31:49Z-
dc.date.created2021-09-05-
dc.date.issued2020-06-
dc.identifier.issn0168-1699-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/118578-
dc.description.abstractGreenhouses provide controlled environmental conditions for crop cultivation but require careful management to ensure ideal growing conditions. In this study, we tested three deep-learning-based neural network models (Artificial neural network, ANN; Nonlinear autoregressive exogenous model, NARX; and Recurrent neural networks - Long short-term memory, RNN-LSTM) to determine the best approach to predicting environmental changes in temperature, humidity, and CO2 within a greenhouse to improve management strategies. This study determined the prediction performance for time steps from 5 to 30 min and showed that the accuracy of the time-based algorithm gradually decreased as prediction time increased. The best model for all datasets was RNN-LSTM, even after 30 min, with an R-2 of 0.96 for temperature, 0.80 for humidity, and 0.81 for CO2 concentration. The results of this study show that it is possible to apply deep-learning-based prediction models for more precisely managing greenhouse.-
dc.languageEnglish-
dc.publisherELSEVIER SCI LTD-
dc.subjectMICROCLIMATE-
dc.subjectPARAMETERS-
dc.subjectDESIGN-
dc.subjectCFD-
dc.titleTime-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse-
dc.typeArticle-
dc.identifier.doi10.1016/j.compag.2020.105402-
dc.description.journalClass1-
dc.identifier.bibliographicCitationCOMPUTERS AND ELECTRONICS IN AGRICULTURE, v.173-
dc.citation.titleCOMPUTERS AND ELECTRONICS IN AGRICULTURE-
dc.citation.volume173-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid000531080400028-
dc.identifier.scopusid2-s2.0-85082820559-
dc.relation.journalWebOfScienceCategoryAgriculture, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalResearchAreaAgriculture-
dc.relation.journalResearchAreaComputer Science-
dc.type.docTypeArticle-
dc.subject.keywordPlusMICROCLIMATE-
dc.subject.keywordPlusPARAMETERS-
dc.subject.keywordPlusDESIGN-
dc.subject.keywordPlusCFD-
dc.subject.keywordAuthorClimate modeling-
dc.subject.keywordAuthorDeep Neural Network-
dc.subject.keywordAuthorForecasting model-
dc.subject.keywordAuthorGreenhouse control-
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