Greenhouse Environmental Control System Based on SW-SVR
Greenhouse Environmental Control System Based on SW-SVR
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DOI：
10.1016/j.procs.2015.08.249
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Y. Kaneda;Hirofumi Ibayashi;N. Oishi;H. Mineno
中科院分区：
文献类型：
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作者：
Y. Kaneda;Hirofumi Ibayashi;N. Oishi;H. Mineno
利用传感器网络的温室环境控制系统正变得越来越广泛和复杂。为了匹配专业农民的产品，这些系统收集有关种植环境和生长情况的数据，旨在控制种植优质作物的环境。然而，由于没有农业经验，系统用户很难正确设置多个设备的控制参数。为了再现专家农户在无人为干预的情况下进行的预测控制，作者提出了一种基于滑动窗的支持向量回归（SW-SVR）的智能温室环境控制系统。该系统在实时准确预测的基础上进行预测控制。SW-SVR是一种新的用于时间序列数据预测的机器学习算法。预测模型周期性自动适应当前环境，预测时间序列数据精度高，计算复杂度低。使用SW-SVR的拟议系统使系统用户能够优化作物控制。同时，由于植物的生长与叶片的光合作用和蒸腾作用有关，作者开发了无线散射光传感器，间接测量叶片面积大小，从而估计植物的生长情况。以现场散射光传感器数据、室外天气数据和预报数据为自变量，对番茄水培的SW-SVR进行了实验研究。实验结果表明，与SVR相比，该系统对氮吸收量的平均绝对误差（MAE）和均方根误差（RMSE）的预测误差分别降低了59.44%和52.89%，训练数据平均减少了43.07%。此外，该系统栽培的番茄含糖量比普通番茄提高了1.54倍。
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.