Determining and Modeling of Static Friction Coefficient of Some Agricultural Seeds

An automatic control machine was constructed to measure static friction coefficient (SFC). The machine was assembled from the following main parts: movable structural surface, direct current motor (DCM), infrared wave receiver sensor, infrared wave transmitter sensor, control box, liquid-crystal display (LCD), and protractor unit. The SFC of barley, wheat, chickpea, safflower, rye, soybean, and sunflower seeds were measured on different structural surfaces made of wood, galvanized steel, rubber, glass, and aluminum plate using the machine. To reduce error, trails were carried out in five replications. The measured coefficients were modeled using intelligent models by taking in account the seed and surface type. Results of comparison the SFC values indicated that the surface and seed type had significant effects on the SFC (P 0.01). The maximum and minimum value of the SFC were obtained in case of rye seed with wood surface (μs=0.762) and barley seed with galvanized steel (μs=0.318), respectively (P 0.01). According to the coefficient of determination (R^2) and the root mean square error (RMSE) values of modeling, prediction of the SFC of experimental seeds based on the adaptive nero-fuzzy inference system (ANFIS) model was more satisfaction rather than the artificial neural network (ANN) model.