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http://dx.doi.org/10.12791/KSBEC.2022.31.3.152

Development and Verification of Smart Greenhouse Internal Temperature Prediction Model Using Machine Learning Algorithm  

Oh, Kwang Cheol (Agriculture and Life Science Research Institute, Kangwon National University)
Kim, Seok Jun (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University)
Park, Sun Yong (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University)
Lee, Chung Geon (Agriculture and Life Science Research Institute, Kangwon National University)
Cho, La Hoon (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University)
Jeon, Young Kwang (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University)
Kim, Dae Hyun (Department of Biosystems Engineering, Kangwon National University)
Publication Information
Journal of Bio-Environment Control / v.31, no.3, 2022 , pp. 152-162 More about this Journal
Abstract
This study developed simulation model for predicting the greenhouse interior environment using artificial intelligence machine learning techniques. Various methods have been studied to predict the internal environment of the greenhouse system. But the traditional simulation analysis method has a problem of low precision due to extraneous variables. In order to solve this problem, we developed a model for predicting the temperature inside the greenhouse using machine learning. Machine learning models are developed through data collection, characteristic analysis, and learning, and the accuracy of the model varies greatly depending on parameters and learning methods. Therefore, an optimal model derivation method according to data characteristics is required. As a result of the model development, the model accuracy increased as the parameters of the hidden unit increased. Optimal model was derived from the GRU algorithm and hidden unit 6 (r2 = 0.9848 and RMSE = 0.5857℃). Through this study, it was confirmed that it is possible to develop a predictive model for the temperature inside the greenhouse using data outside the greenhouse. In addition, it was confirmed that application and comparative analysis were necessary for various greenhouse data. It is necessary that research for development environmental control system by improving the developed model to the forecasting stage.
Keywords
artificial intelligence; machine learning; simulation model; smart greenhouse; time series data;
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