Abstract: In order to improve the performance of greenhouse control system and figure out the multi-time scale variable problem between plant growth and environment elements in greenhouses, the ensemble empirical mode decomposition (EEMD) and Elman neural network were used to predict cayenne pepper height in this paper. Taking the cayenne pepper 8819 and its plant height and environment elements (temperature, relative humidity, total inner radiation) as research object, plant height and environment elements were decomposed by the method of EEMD. The cayenne pepper plants sampled were irrigated with the nutrient solution with electricity conductivity of 2.0 and pH value of 6.5 from Shandong Agricultural University. Five intrinsic mode functions (imfs) were obtained by the EEMD, named imf1, imf2, imf3, imf4 and imf5. Oscillations of cayenne pepper plant height and environment elements at different frequency were shown by imf1, imf2, imf3 and imf4, while variation trend of cayenne pepper plant height and environment elements were shown in imf5. All imfs were reconstructed by reconstruction of the EEMD with approximate values of 1, 0 and 0 respectively for correlation coefficient, standard error and mean absolute error to original time series. The imfs obtained by EEMD decomposition were used to build plant height prediction model at different time-scale frequencies based on EEMD method and Elman neural network. All the plants of cayenne pepper 8819 sampled were divided into 2 sets, which were training set and testing set. Average values of sampled plants and environment elements were used in neural network building. In this paper, the environment elements (temperature, relative humidity and inner total radiation) formed the input layer of EEMD-Elman network, and the prediction of cayenne pepper plant height was the output layer of EEMD-Elman network. Double-layer feedback structure method was used in this paper, in which there were 10 nodes in the first layer and 3 nodes in the second layer. The function 'transig' was used as transfer function in the feedback layer, while the function 'purelin' was used in the output layer. Sample data were divided into training set, validation set and testing set in proportion of 0.7:0.15:0.15. Five sub-neural networks were established by imf1, imf2, imf3, imf4 and imf5 of samples' plant height and environment elements. Final predicted value of sampled plant height was reconstructed by EEMD reconstruction, using the results of 5 sub-neural networks. Results of EEMD-Elman prediction model showed the mean absolute error for plant height was 1.69 cm, with the correlation coefficient of 0.996 and the standard error of 1.104, which meant the prediction value was significantly correlated to the real value. Otherwise, 2 different prediction models were built by the method of EEMD-BP (back propagation) neural network and Elman neural network. Results of EEMD-BP prediction model showed the mean absolute error of 5.40 cm for plant height, with the correlation coefficient of 0.812 and the standard error of 7.012, while results of Elman model showed the mean absolute error of 8.87 cm, with the correlation coefficient of 0.908 and the standard error of 5.032. The results of 3 different prediction models were compared. The prediction of EEMD-Elman neural network was the best among the 3 neural networks. The results of the models with EEMD were better than that of the Elman model without EEMD. So, EEMD could decompose time series into different time scales according to its own features without the disturbance of noises and singular wave signals. Details of original time series were demonstrated decently by EEMD. Fluctuation of original time series could be explained better at different time scales by EEMD. Precision of prediction could be improved by Elman neural network with double-layer feedback structure construction. In conclusion, the combination of EEMD and Elman neural network can be used to figure out the issue of the prediction of multi-time scale between plant growth and environment variation in greenhouses, in order to provide effective references for control objectives optimization in greenhouse control system.