基于多模态数据驱动的黄瓜温室湿度预测方法

Research on humidity prediction method of cucumber greenhouse based on multi-mode data driving

温室湿度精准预测对病害防治策略制定、水肥自动灌溉等具有重要意义。本文研究了一种基于多模态数据驱动的预测方法。为解耦温室环境控制中环境变量复杂关系,提高模型预测效率,利用LASSO回归从多温室环境参数中筛选得到温室空气湿度变化强关联环境因子,结合CNN提取图像空间特征的优势,基于GAF理论将温室时间序列分别转化为GASF与GADF二维图像,进一步增强有效信息,抑制环境噪声,通过引入低复杂度的双卷积层充分提取图像潜在特征,识别湿度变化趋势,对不同湿度变化趋势的时间序列逐一构建Bayesian_LSTM预测模型,增加平稳输入提高预测精度。针对黄瓜温室,将室内温度、湿度、光照强度历史时间序列转化为二维图像作为输入,分析验证了模型的预测性能。试验数据显示当时间滑动窗口大小为15,选用GADF转化图像,Bayesian_LSTM隐藏节点数为100时,平均绝对误差、平均绝对百分比误差、均方根误差分别达到2.58%、4.56%、4.80%,为模型性能最优。对比RNN、GRU、Bi-GRU、1D-CNN共4种主流预测模型,试验结果均表现出良好的预测性能。

The accurate prediction of greenhouse humidity is of great significance to the formulation of disease control strategies and automatic irrigation of water and fertilizer.In this paper,aprediction method based on multi-modal data driven for full or Chinese names is studied.In order to decouple the complex relationship of environmental variables in greenhouse environmental control and improve the prediction efficiency of the model,this paper uses LASSO regression to screen the strongly related environmental factors of greenhouse air humidity changes from multiple greenhouse environmental parameters.Combining the advantages of CNN in extracting image spatial characteristics,based on GAF theory,the greenhouse time series are converted into two dimensional images of Gram angle summation field and Gram angle difference field,further enhancing effective information and suppressing environmental noise,The low complexity double convolution layer is introduced to fully extract the potential features of the image,identify the humidity change trend,and construct for the time series of different humidity change trends one by one Bayesian_LSTM prediction model,increase smooth input to improve prediction accuracy.In this paper,the historical time series of indoor temperature,humidity and light intensity are converted into two-dimensional images as input for cucumber greenhouse,and the prediction performance of the model is analyzed and verified.The experimental data shows that when the time sliding window size is 15,Gram angular difference field,Bayesian_When the number of LSTM hidden nodes is 100,the average absolute error,average absolute percentage error,and root mean square error reach 2.58%,4.56%,and 4.80%respectively,which is the best performance of the model.Compared with four mainstream prediction models,RNN,GRU,Bi-GRU and 1D-CNN,the test results show good prediction performance.