Research on Greenhouse Environment Prediction Based on GCAKF-CNN-LSTM

Abstract

Highlights A GCAKF-CNN-LSTM model is proposed for greenhouse temperature and humidity forecasting. The grey correlation analysis is used to select the most relevant variables. Kalman filter is applied for denoising to improve the data quality. The proposed model achieves higher forecasting accuracy with the lowest forecasting errors. Abstract. Accurate prediction of temperature and humidity in the greenhouse environment is helpful to regulate the environment and promote crop growth. Aiming at the characteristics of nonlinear and strong coupling in the greenhouse environment, this article proposes a hybrid greenhouse temperature and humidity prediction model based on preprocessing algorithms, Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Firstly, grey correlation analysis (GCA) is used to screen the data features and analyze the factors affecting the temperature and humidity in the greenhouse. Secondly, data is denoised by the Kalman filter (KF) to reduce the noise interference. Thirdly, the local connection and weight sharing features of the CNN are applied to obtain effective features from the series, and the long- and short-term dependence relationships of the data are learned by using the LSTM networks. Finally, the proposed model is validated on the greenhouse data. Experimental results demonstrated that, compared with Back Propagation(BP), Gated Recurrent Units (GRU), and LSTM, the RMSE of temperature prediction results was reduced by 31.5%, 21.6%, 14.4%, and the MAE reduced by 48.5%, 41.0%, and 32.3%, respectively. The RMSE of humidity prediction results decreased by 28.3%, 2.73%, and 0.63%, and the MAE decreased by 69.4%, 54.5%, and 10.8%, respectively. The proposed model can improve prediction accuracy and provide a decision basis for improving the timeliness of the greenhouse environmental control system. Keywords: Convolutional neural network, Greenhouse environment prediction, Kalman filter, Long short-term memory network.