Abstract:
The goal of this study was to predict the moisture
content of paddy using machine
vision and artificial neural networks (ANNs). The g
rains 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 v
elocity
on the
L*a*b*
values were significant (P< 0.01) and not signific
ant (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 fu
nction in hidden layer and Tansig in
output layer. Mean square error, coefficient of det
ermination, and mean absolute error of
the optimized ANN were 0.001, 0.9630, and 0.031, re
spectively.
Keywords:
Back propagation neural network, Color features, Im
age processing, Rice.
