天津农业科学2023，Vol.29Issue(11)：84-90,7.DOI:10.3969/j.issn.1006-6500.2023.11.014
基于LSTM神经网络的温室气候环境因子预测
Prediction of Greenhouse Climate Environmental Factors Based on LSTM Neural Network
摘要
Abstract
The current greenhouse climate environment,environmental monitoring data can only reflect the current environmental con-ditions and cannot predict the trend of greenhouse environmental changes.To solve the problem of poor greenhouse climate environ-ment control,a method for predicting greenhouse climate environment factors based on the LSTM neural network was adopted.The humidity,temperature and carbon dioxide concentration collected in the greenhouse were standardized as historical data,and 90%of the data was used as the training set and 10%was used as the test set.The LSTM prediction model was established by setting initial parameters,and the training accuracy of the model was adjusted constantly by finding different model parameters.Finally,the LSTM prediction model was tested and validated by the test set.Both a BP neural network model and a GRU prediction model were estab-lished in order to better illustrate the superiority of the LSTM prediction model.The results showed that the LSTM prediction model could effectively predict the trend of changes in humidity,temperature,and carbon dioxide concentration in greenhouse and had an average improvement of 5.80%and 3.81%in the prediction accuracy compared to the BP neural network model and GRU prediction model.The LSTM prediction model established in the paper can achieve accurate prediction of greenhouse climate environmental fac-tors and provide certain decision-making support for greenhouse environmental regulation.关键词
LSTM神经网络/温室气候环境因子/环境调控Key words
LSTM neural network/greenhouse climate environment factors/greenhouse environmental分类
计算机与自动化引用本文复制引用
梁志超,宋华鲁,樊阳阳,齐康康,徐浩,王帅..基于LSTM神经网络的温室气候环境因子预测[J].天津农业科学,2023,29(11):84-90,7.基金项目
山东省农业科学院农业科技创新工程(CXGC2023A34、CXGC2023F07、CXGC2021A22) （CXGC2023A34、CXGC2023F07、CXGC2021A22）
