Temperature and CO2 Prediction to Control Greenhouse Environment

In this study three models are developed, one for prediction of the inside temperature 5 and 10 min ahead of time, other for prediction of CO2 concentration 5 min in advance, and the results of this model are linked with a third model for photosynthesis prediction 5 min ahead. Data were collected from two compartments at the experimental greenhouse (Humboldt University). Artificial Neural Networks (ANN) were used because of their ability to capture the nonlinear relationships governing the greenhouse environment. Matlab's Neural Networks Toolbox was used to train, validate and test the NN models. A data pattern of 7800 and 11 input variables were used for prediction of the inside temperature. A linear regression was performed between actual and predicted values with coefficients of determination (CD) of 0.997 and 0.994 and mean square errors (MSE) of 3.48 and 5.84 for 5 and 10 min temperature predictions. To evaluate the performance of the ANN, a different 200 data set was fed into the NN (October 28, 2 to 6:35 pm), and the predictions were very precise with MSE between actual and predicted values of 0.088 and 0.029. Eight input variables and a 1800 data set were used for predicting CO2 concentration 5 min ahead of time. The CD of the linear regression between actual and predicted values was 0.994. Again the ANN was fed with 200 different input data (22 June, 3:20 to 19:55 pm) and MSE between actual and predicted values was 535. The results from the CO2 model were used as an input in the photosynthesis model. In this last model seven variables were used and the predictions were very precise in both cases for photosynthesis 5 and 10 min ahead. The sensitivity analysis performed shows that relative humidity is one of the most important variables affecting photosynthesis prediction.