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This work is done in Ahvaz Township in Iran to estimate the amount of energy consumption for wheat production with artificial neural network. Required information is obtained randomly by completing questionnaires and face to face interviews with 90 farmers. Results show that the fertilizer, seed, and herbicide were the major energy consumers, and minor energy consumers were transportation. Energy productivity, net energy gain, and energy ratio were respectively 0.052 kg/Mj, 63.2 GJ and 1.51. Total amount of Carbon dioxide (CO2) emission in wheat production was calculated as 0.931 tonha-1. Diesel fuel had the highest share (0.470 tonha-1) followed by chemical fertilizer machinery (0.231 tonha-1) and machinery (0.230 tonha-1). To estimate output energy Multilayer Perceptron (MLP), Radial Basis Function Network (RBF) and Self-Organizing Map (SOM) networks by changing in the number of hidden layers, training algorithm and number of neurons were used. Results showed that, MLP network have the maximum­ determination coefficient of 97% and the minimum MSE of 0.004 with topology of 6-7-7-1 and LM training. The sensitivity analysis of input parameters on output showed that total seed, fertilizer and chemical poisons had the highest and machinery had the lowest sensitivity on output energy with 52 and 5%, respectively.       Key words: Artificial neural network, energy efficiency, wheat.

Application of artificial neural networks ANNs to predict energy output for wheat production in Iran