Predictive Modeling of Massey Ferguson Tractor Performance Parameters Using Artificial Neural Network Methodology

Predicting tractor performance factors accurately is crucial for enhancing energy efficiency and assisting with the choice of machinery in agricultural operations. Using the Nebraska Tractor Test Laboratory (NTTL) identical data, this study uses artificial neural network (ANN) modeling to forecast important performance metrics of a front wheel assist (FWA) Massey Ferguson tractor. A feed-forward ANN model was developed and validated using reported data from official tractor tests. Performance indicators, such as drawbar pull (kN), drawbar power (kW), hourly fuel consumption rate (kg/h), drawbar specific fuel consumption (kg/kW·h), and drawbar specific volumetric fuel efficiency (kW/kg·h), were utilized as outputs and certain operational factors, the tractor characteristics variables as well as other variables were used as inputs. Statistical measures, including the coefficient of determination and error metrics from training and testing datasets, were used to assess the model’s performance. The results showed that the ANN model produced excellent generalization capabilities and good prediction performance by correctly capturing the nonlinear correlations between inputs and tractor performance indicators. The suggested strategy performed better than traditional regression-based techniques documented in the literature, especially when operation variables and tractor characteristics varied. The results show that combining NTTL data with ANN techniques offers a dependable and affordable method for predicting tractor performance indicators and evaluating energy efficiency. This eliminates the need for extensive experimental procedures and promotes data-driven decision-making in agricultural machinery management.