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http://dx.doi.org/10.9728/dcs.2018.19.4.749

Development of Prediction Model for Greenhouse Control based on Machine Learning  

Kim, Sang Yeob (Department of Clean Fuel & Power Generation, Korea Institute of Machinery & Materials)
Park, Kyoung Sub (Protected Horticulture Research Institute, National Institute of Horticultural and Herbal Science)
Lee, Sang Min (Department of Clean Fuel & Power Generation, Korea Institute of Machinery & Materials)
Heo, Byeong Mun (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University)
Ryu, Keun Ho (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University)
Publication Information
Journal of Digital Contents Society / v.19, no.4, 2018 , pp. 749-756 More about this Journal
Abstract
In this study, we developed a prediction model for greenhouse control using machine learning technique. The prediction model was developed using measured data (2016) on greenhouse in the Protected Horticulture Research Institute. In order to improve the predictive performance of model and to ensure the reliability of data, the dimension of the data was reduced by correlation analysis. The dataset were divided into spring, summer, autumn, and winter considering the seasonal characteristics. An artificial neural network, recurrent neural network, and multiple regression model were constructed as a machine leaning based prediction model and evaluated by comparative analysis with real dataset. As a result, ANN showed good performance in selected dataset, while MRM showed good performance in full dataset.
Keywords
Greenhouse control; Prediction model; Machine learning; Artificial neural network; Recurrent neural network;
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Times Cited By KSCI : 1  (Citation Analysis)
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