A Neural Network Model for Photosynthesis Prediction

A common problem in greenhouse production is the CO2 supply inside of the greenhouse to increase crop yields by stimulating photosynthesis; However, CO2 is one of the most expensive production inputs. Therefore it is necessary to apply CO2 only when it is necessary in order to reduce cost. Consequently a good greenhouse control tool was necessary, so two neural network models were developed, one for CO2 prediction and the other for pho-to synthesis prediction by doing this, we can know the photosynthesis tendency. If this process is increasing CO2 supply continuous, on the contrary CO2 stops. For the CO2 model eight input variables were used and a 1800 data pattern. The ANN was feed with 200 different input data (June 22, 3:20 p.m to 19:55 p.m) and the MSE error between actual and predicted values was 535. The results from the CO2 model was linked with the photosynthesis model. In this last model seven variables were used. Predictions were very good in both cases. The sensitivity analysis performed in CO2 and photosynthesis prediction show that relative humidity is one of the most important variables affecting photosynthesis, after solar radiation, and CO2.