Prediction of Paddy Moisture Content during Thin Layer Drying Using Machine Vision and Artificial Neural Networks

The goal of this study was to predict the moisture content of paddy using machine vision and artificial neural networks (anns). the grains were dried as thin layer with air temperatures of 30, 40, 50, 60, 70, and 80°c and air velocities of 0.54, 1.18, 1.56, 2.48 and 3.27 ms-1. kinetics of l*a*b* were measured. the air temperature, air velocity, and l*a*b* values were used as ann inputs. the results showed that with increase in drying time, l* decreased, but a* and b* increased. the effect of air temperature and air velocity on the l*a*b* values were significant (p< 0.01) and not significant (p> 0.05), respectively. changing of color values at 80°c was more than other temperatures. the optimized ann topology was found as 5-7-1 with logsig transfer function in hidden layer and tansig in output layer. mean square error, coefficient of determination, and mean absolute error of the optimized ann were 0.001, 0.9630, and 0.031, respectively.