Abstract
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.
Highlights
In most of developed countries, by calculation, indices of energy efficiency and evaluation of input energy per unit area for various agricultural products, experts try to make energy consumption more efficient (Peiman et al, 2005)
Fuel consumption was among the major energy consumers
Transportation had the minor part of energy consumption
Summary
In most of developed countries, by calculation, indices of energy efficiency and evaluation of input energy per unit area for various agricultural products, experts try to make energy consumption more efficient (Peiman et al, 2005). Determination of energy consumption in every level of production, help us to obtain which level has the minimum input energy (Chaudhary et al, 2006). In addition common methods, some new methods are invented. One of these modern methods is artificial neural network. Networks learn general rules by calculating numerical data or examples; as result of that there are called intelligent systems (Mohammadi et al, 2007). Without considering any initial supposition and previous knowledge of relations among
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