Prediction of Soil Temperature and Inside air Humidity in a Semi-Solar Greenhouse Equipped with Cement North Wall by Artificial Neural Network; Case study: Tabriz city

The ecological domain includes some aspects of agriculture that has not a good development until now. Commercial greenhouse is one of the most effective cultivation methods need to more attentions. Researches are on intelligent greenhouses with some important aspects such as receiving the maximum solar radiation and having the minimum heat loss. In recent study, the application of Artificial Neural Network models to predict inside air humidity and soil temperature beside on some inside and outside parameters were investigated. For this purpose, data was recorded from a semi-solar greenhouse located at agricultural faculty of Tabriz University using several temperature and humidity sensors, solar meter and hot wire. This greenhouse has the best structure between all typical kinds of greenhouse and furthermore has the best situation about heat lost and gain the most solar radiation between all types of greenhouses in this region. Single layer multilayer perceptron (MLP) models with LM learning rule and different neurons in hidden layer were used. The results showed that 4-9-1 and 5-6-1 topology with R2 =0.9945 and 0.9971have the highest precision to predict the soil temperature and inside air humidity in semi-solar greenhouse. For theses topologies, MAPE and RMSE were 1.1060, 1.1956% and 1.0353, 0.2502°C, respectively. Comparison between the results of ANN and multiple linear regression (MLR) models showed that ANN is more powerful than mathematical models in this subject. Also results showed that Artificial Neural Network can be used to control automatically the greenhouse environmental parameters with minimum cost in the future.