Prognostication of fuel consumption for Massey Ferguson tractor (MF 285) by artificial neural network based modeling approach.

Due to the ascending significance of energy in the world, prognostication and optimization of Fuel Consumption (FC) in agricultural works is merit to consideration. Therefore, performance model for evolving parameters of tractors and implements are essential for farm machinery, operators and manufacturers alike. A conventional tillage system which included a moldboard plow with three furrows was used for collecting data from MF285 Massey Ferguson tractor. Field experiments were carried out in the experimental farm of Agricultural Engineering Department of Tehran University, Karaj province, Iran, which had loamy soil texture. The objective of this study was to assess the predictive capability of several configurations of ANNs for performance evaluating of tractor in parameter of fuel consumption. To predict performance parameters, ANN models with back-propagation algorithm were developed using a MATLAB software with different topologies and training algorithms. The ANN model with 6-7-1 structure and Levenberg-Marquardt training algorithm had the best performance with R2 of 0.969 and MSE of 0.13427 for TFC prediction. The 6-8-1 topology shows the best power for prediction of AFC with R2 and MSE of 0.885 and 0.01348, respectively. Also, the 6-10-1 structure yielded the best performance for prediction of SFC with R2 of 0.935 and MSE of 0.012756. The obtained results promoted that the neural network can be able to learn the relationships between the input variables and fuel consumption of tractor, reliable.