Prediction of Greenhouse Climate Environmental Factors Based on LSTM Neural Network
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.
国家科技期刊平台
NSTL
万方数据
维普
