جستجوی مقالات مرتبط با کلیدواژه « greenhouse temperature » در نشریات گروه « مکانیزاسیون کشاورزی »

Internal temperatures of greenhouse and its control is one of the important parameters in greenhouses and plays a key role in the economics of production. Although the greenhouse is a closed environment, it is not completely isolated from the outside. Therefore, the conditions inside the greenhouse are constantly changing under the influence of outside climate change. The purpose of this study was to estimate the internal air temperature of polyethylene greenhouse with respect to the external parameters of the greenhouse including air temperature (Tout), air relative humidity (Hout), solar radiation (S) and wind speed (V). For this purpose, different method of artificial neural networks including Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Adaptive Nero Fuzzy Inference System (ANFIS) were used. Comparison between different neural network models showed that RBF method had better prediction performance than MLP and ANFIS with higher coefficient of determination (R2=0.93) and lower error (RMSE=2.25). The results of the RBF model estimation for the prediction future temperature indicated an acceptable error in the prediction by the model for the next two hours and thus, the farmers had enough time to provide the necessary measures to prevent the greenhouse temperature rise in the future and save in energy consumption.

Today's agriculture, greenhouse cultivation plays a key role in increasing the quantity and quality of products. Indoor conditions of the greenhouse depend on some external factors, which are usually not easily predictable. The purpose of this study was to estimate the air temperature inside the greenhouse in two modes of ventilation (non-ventilated conditions and evaporative cooling system) using artificial neural network and regression models. Some factors such as solar irradiance, ambient temperature, northern wall temperature and flow rate and temperature of the cooling air were employed as the inputs of the models. Verification of the models was conducted using statistical criteria of mean square error, correlation coefficient and mean absolute percentage error. In order to train the neural network from multilayer perceptron with the algorithm of post-error learning and using the Levenberg-marquart training algorithms, the Bayesian regression and the gradient conjugate scalar, and in the regression model of the progressive and forward method for determining regression equations were used. Comparison of the statistical criteria indicated that the artificial neural network method predicted the greenhouse temperature with a higher accuracy than the regression model.