Abstract
The aim of this study is to develop and predict models of tractive efficiency using the artificial neural network and stepwise approach. The tractive efficiency of tractor (CASE JX75T) was measured experimentally. Experiments were conducted in the site of Basrah University. Which had silty clay soil texture. The field conditions included effect of two level of cone index (550 and 980 kPa), two level of moisture content (8 and 21%), three forward speeds (0.54, 0.83 and 1.53 m/s) and four level of tillage depths (10, 15, 20 and 25 cm). The results illustrated that both developed models (stepwise approach and ANN technique) had acceptable performance for predicting tractive efficiency of tractor under various field conditions. However, ANN model outperformed stepwise model, where 4-7-1 topology showed the best power for predicting tractive efficiency with R-squared of 0.97 and MSE of 0.0074 with Levenberg-Marquardt training algorithm. The analysis of variance demonstrated that the studied parameters had single significant effect on tractive efficiency. The most parameter influential on tractive efficiency was tillage depth followed forward speed, cone index and moisture content.
Highlights
Tractive efficiency is one of the most important criteria to evaluate tractor performance during field operations
Artificial neural network (ANN) approach has illustrated to be essential as an exciting alternative way concerning the intricate system
artificial neural network (ANN) is one of the intelligent computational methods, which aims to offer a mapping between the input space and the desirable space by perception the essential relationships between the data using learning approach and the processors called the neurons
Summary
Tractive efficiency is one of the most important criteria to evaluate tractor performance during field operations. The results from a research work conducted by Aday et al (1) shows that the maximum traction efficiency of 2WD and 4WD tractors were 0.72 and 0.79 which occurred at traction wheels slip of 12% and 7%, respectively. Peca et al (19) studied the effects of engine speed and forward speed on the tractive efficiency in tractor operations. Many researchers have shown the capacity of ANN versus regression approach such as research done by Rahimi and Abbaspour (20) They utilized ANN and stepwise multiple range regression methods for the forecast of tractor fuel consumption and their results explained that ANN provided better prediction compared to stepwise regression. The aim of this study is to develop models for predicting the tractive efficiency of the tractor (CASE JX75T) utilizing ANN and Design Expert software (stepwise approach). Moisture content and bulk density were calculated from equations 1and 2 respectively
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