Tsai, Yi-ZhihYi-ZhihTsaiHsu, Kan-ShengKan-ShengHsuWu, Hung-YuHung-YuWuLin, Shu-I.Shu-I.LinHWA-LUNG YUKUO-TSANG HUANGMING-CHE HUSHAO-YIU HSU2021-02-042021-02-042020https://www.scopus.com/inward/record.url?eid=2-s2.0-85085133917&partnerID=40&md5=bfec2aee949b8ed35427977e830e55cahttps://scholars.lib.ntu.edu.tw/handle/123456789/548300Climate change might potentially cause extreme weather events to become more frequent and intense. It could also enhance water scarcity and reduce food security. More efficient water management techniques are thus required to ensure a stable food supply and quality. Maintaining proper soil water content and soil temperature is necessary for efficient water management in agricultural practices. The usage of water and fertilizers can be significantly improved with a precise water content prediction tool. In this study, we proposed a new framework that combines weather forecast data, numerical models, and machine learning methods to simulate and predict the soil temperature and volumetric water content in a greenhouse. To test the framework, we performed greenhouse experiments with cherry tomatoes. The numerical models and machine learning methods we selected were Newton's law of cooling, HYDRUS-1D, the random forest model, and the ICON (inferring connections of networks) model. The measured air temperature, soil temperature, and volumetric water content during the cultivation period were used for model calibration and validation. We compared the performances of the models for soil temperature and volumetric water content predictions. The results showed that the random forest model performed a more accurate prediction than other methods under the limited information provided from greenhouse experiments. This approach provides a framework that can potentially learn best water management practices from experienced farmers and provide intelligent information for smart greenhouse management. © 2020 by the authors.Cherry tomato; Hydrus-1d; Inferring connections of networks (icon); Machine learning; Time-series[SDGs]SDG2[SDGs]SDG6[SDGs]SDG13[SDGs]SDG15Agricultural robots; Climate change; Cultivation; Decision trees; Food supply; Greenhouses; Machine learning; Numerical methods; Numerical models; Random forests; Soil moisture; Temperature; Water conservation; Water management; Agricultural practices; Greenhouse experiments; Intelligent information; Machine learning methods; Model calibration and validation; Newton's law of cooling; Random forest modeling; Volumetric water content; Weather forecasting; agricultural practice; climate change; extreme event; fertilizer application; food security; freshwater sediment; machine learning; prediction; soil temperature; soil water; water content; water storage; water use; weather forecasting; Lycopersicon esculentum var. cerasiformejournal article10.3390/W120411762-s2.0-85085133917WOS:000539527500253