Fuzzy knowledge-based model for prediction of soil loosening and draft efficiency in tillage

A knowledge-based system for assessing soil loosening and draft efficiency in tillage is presented. The knowledge-based system was built through expert opinion elicitation and available scientific data using fuzzy logic. It is expected that such a non-linear relationship includes some uncertainties. A fuzzy inference system employing fuzzy If–Then rules has an ability to deal with ill-defined and uncertain systems. Compared with traditional approaches, fuzzy logic is more efficient in linking the multiple inputs to a single output in a non-linear domain. The main purpose of this study is to investigate the relationship between cultivator shares working parameters to soil loosening and draft efficiency, and to illustrate how fuzzy expert system might play an important role in prediction of these. Experimental values were taken in soil bin. The trials were conducted in different working depths and forward velocities of cultivator shares. In this paper, a sophisticated intelligent model, based on Mamdani approach fuzzy modeling principles, was developed to predict the changes in soil loosening and draft efficiency of tool. The fuzzy model consists of 25 rules. In this research, a Mamdani max–min inference for inference mechanism and the center of gravity (Centroid) defuzzifier formula method for defuzzification were used as these operators assure a linear interpolation of the output between the rules. The verification of the proposed model is achieved via various numerical error criterias. For all parameters, the relative error of predicted values was found to be less than the acceptable limits (10%).