Greenhouse environmental control systems using sensor networks are becoming more widespread and sophisticated. To match the produce of expert farmers, these systems collect data about cultivation environment and growth situation, and aim to control the environment for cultivating high quality crops. However, with no agriculture experience, it is difficult for system users to set control parameters of several devices properly. In order to reproduce prediction control performed by expert farmers’ cultivation without human intervention, the authors propose a smart greenhouse environmental control system based on sliding window-based support vector regression (SW-SVR). The proposed system performs prediction control based on accurate predictions in real time. SW-SVR is a new machine learning algorithm for time series data prediction. The prediction model automatically adjusts to the current environment periodically, predicts time series data with high accuracy and low computational complexity. The proposed system using SW-SVR enables system users to optimize controls for crops. Meanwhile, since plant growth is related to the photosynthesis and transpiration of leaves, the authors developed wireless scattered light sensors which measure leaf area size indirectly so as to estimate plant growth. Our experimental results, using data of scattered light sensors on-site, outside weather data, and forecast data as independent variables of SW-SVR for hydroponic culture of tomatoes, show the proposed system reduced prediction error of nitrogen absorption amount by 59.44% as Mean Absolute Error (MAE) and 52.89% as Root Mean Squared Error (RMSE) compared with SVR, and reduced training data by 43.07% on average. Furthermore, the sugar content of tomatoes cultivated by the prototype system increased 1.54 times compared with usual tomatoes.
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