基于CNN-GRU的菇房多点温湿度预测方法研究

Multi-point Prediction of Temperature and Humidity of Mushroom Based on CNN-GRU

有效获取温室出菇房的温湿度空间分布对于优化食用菌环境胁迫、病害预警、出菇房预调控至关重要,但传统的单点预测不能很好地满足菇房整体环境性能评估的需求。针对出菇房内温湿度时序性、非线性、空间分布差异性的特点,提出一种基于卷积神经网络(CNN)与门控循环单元神经网络(GRU)相结合的菇房多点温湿度预测方法。将温室室外历史气象数据、温室室内历史小气候环境数据、多点环境分布特征、通风信息和加湿信息多特征数据按照时间序列构造二维矩阵作为输入,采用CNN挖掘数据中蕴含的有效信息,提取反映温室环境数据相互联系的高维特征,将提取的特征向量构造为时间序列输入GRU网络进行多点温湿度预测。将该预测方法应用于北京市农林科学院的日光温室出菇房内多点温湿度预测,实验结果表明,该预测方法对于出菇房内各点温度RMSE平均值为0.211℃,MAE平均值为0.140℃,误差控制在±0.5℃范围内的平均比例为97.57%;对于出菇房内各点相对湿度RMSE平均值为2.731%,MAE平均值为1.713%,误差控制在±5%范围内的平均比例为92.62%;相比传统的BP神经网络、长短期记忆神经网络(LSTM)和门控循环单元神经网络(GRU),该预测方法具有更高的预测精度。

It was vitally important to effectively obtain the spatial distribution of temperature and humidity of the greenhouse mushroom house in advance for optimizing environmental stress of edible fungi,early warning of disease and pre-regulation of the mushroom house.The traditional single-point prediction could not well meet the demand of evaluation of overall environmental performance for the mushroom house.According to the characteristics of time series,non-linear and different spatial distribution of temperature and humidity in mushroom house,a multi-point prediction method of temperature and humidity for the mushroom house based on convolutional neural network(CNN)and gated recurrent unit neural network(GRU)was proposed,which took the historical outdoor meteorological data of the greenhouse,the indoor microclimate environmental data,environmental distribution characteristics,the ventilation information and the humidification information as input by constructing a two-dimensional matrix according to the time series.Firstly,CNN was used to mine the effective information contained in the data to extract the high-dimensional features reflecting the interrelation of greenhouse environmental data,and then the extracted feature vectors were constructed as time series and input to the GRU network for multi-point prediction of temperature and humidity.The prediction model proposed was applied to the multi-point prediction of temperature and humidity in the mushroom house of a solar greenhouse in Beijing Academy of Agricultural and Forestry Sciences,and the experimental results showed that the averaged RMSE and MAE were 0.211℃and 0.140℃,respectively,for the temperature prediction at each point in the mushroom house,and the average proportion of error control within±0.5℃was 97.57%.For the humidity prediction at each point in the mushroom house,the averaged RMSE and MAE were 2.731%and 1.713%,respectively,and the average proportion of error control within±5%was 92.62%.Comparing with traditional BP neural network,long short-term memory neural network(LSTM),and gated recurrent unit neural network(GRU),the prediction model proposed had higher prediction accuracy.