PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORK MODELLING TO A PLOUGHING UNIT IN VARIOUS SOIL CONDITIONS

The specific objective of this study is to find a suitable artificial neural network model for estimating the operation indicators (disturbed soil volume, effective field capacity, draft force, and energy requirement) of ploughing units (tractor disc) in various soil conditions. The experiment involved two different factors, i.e., (Iota) soil texture index and (Iota Iota) field work index, and included soil moisture content, tractor engine power, soil bulk density, tillage speed, tillage depth, and tillage width, which were linked to one dimensionless index. We assessed the effectiveness of artificial neural network and multiple linear regression models between the values predicted and the actual values using the mean absolute error criterion to test data points. When the artificial neural network model was applied, the mean absolute error values for disturbed soil volume, effective field capacity, draft force, and energy requirement were 69.41 m(3)<middle dot>hr(-1), 0.04 ha<middle dot>hr(-1), 1.24 kN, and 1.95 kw<middle dot>hr<middle dot>ha(-1), respectively. In order to evaluate the behaviour of new models, the coefficient R-2 was used as a criterion, where R-2 values in artificial neural network were 0.9872, 0.9553, 0.9948, and 0.9718, respectively, for the aforementioned testing dataset. Simultaneously, R-2 values in multiple linear regression were 0.7623, 0.696, 0.492, and 0.5572, respectively, for the same testing dataset. Based on these comparisons, it was clear that predictions using the artificial neural network models proposed are very satisfactory.