B Khoshnevisan - Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
Sh. Rafiee - Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
J. Iqbald - cDepartment of Software Engineering, Faculty of Computer Science and Information Technology, University of Malaya, ۵۰۶۰۳ Kuala Lumpur, Malaysia
Sh. Shamshirbande - Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, ۵۰۶۰۳ Kuala Lumpur, Malaysia.
M. Omid - Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Islamic Republic of Iran.
N. B. Anuarf - Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, ۵۰۶۰۳ Kuala Lumpur, Malaysia
A. W. Abdul Wahabg - Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, ۵۰۶۰۳ Kuala Lumpur, Malaysia

In this study greenhouse tomato production was investigated from energy consumption and greenhouse gas (GHG) emission point of views. Moreover, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) were employed to model energy consumption for greenhouse tomato production. Total energy input and output were calculated as ۱۳۱۶.۱۴ and ۲۸۱.۱ GJ/ha. Among the all energy inputs natural gas and electricity had the most significant contribution to the total energy input. Evaluations of GHG emission illustrated that the total GHG emission was estimated at ۳۴۷۵۸.۱۱ kg CO۲eq./ha and among all inputs, electricity played the most important role, followed by natural gas. Drawing a comparison between ANN and ANFIS models demonstrated that the ANFIS-based models due to employing fuzzy rules can model output energy more accurate than ANN models. Accordingly, Correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) for the best ANFIS architecture were calculated as ۰.۹۸۳, ۰.۰۲۵ and ۰.۱۴۹, respectively while these performance parameters for the best ANN model was computed as ۰.۹۳۳, ۰.۰۵۴۱۴ and ۰.۲۷۹, respectively.

energy consumption, ANFIS, ANN, GHG emission, Tomato production