Golpour, I., Amiri Chayjan, R., Amiri Parian, J., Khazaei, J. (2015). Prediction of Paddy Moisture Content during Thin Layer Drying Using Machine Vision and Artificial Neural Networks. , 17(2), 287-298.
I. Golpour; R. Amiri Chayjan; J. Amiri Parian; J. Khazaei. "Prediction of Paddy Moisture Content during Thin Layer Drying Using Machine Vision and Artificial Neural Networks". , 17, 2, 2015, 287-298.
Golpour, I., Amiri Chayjan, R., Amiri Parian, J., Khazaei, J. (2015). 'Prediction of Paddy Moisture Content during Thin Layer Drying Using Machine Vision and Artificial Neural Networks', , 17(2), pp. 287-298.
Golpour, I., Amiri Chayjan, R., Amiri Parian, J., Khazaei, J. Prediction of Paddy Moisture Content during Thin Layer Drying Using Machine Vision and Artificial Neural Networks. , 2015; 17(2): 287-298.

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

Journal of Agricultural Science and Technology
Article 3, Volume 17, Issue 2, 2015, Pages 287-298    PDF (689.38 K)
Authors
I. Golpour1; R. Amiri Chayjan* 1; J. Amiri Parian1; J. Khazaei2
1Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Islamic Republic of Iran.
2Department of Agricultural Technical Engineering, College of Abouraihan, University of Tehran, Islamic Republic of Iran.
Abstract
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.
Keywords
Back propagation neural network; color features; Image processing; Rice
References
Statistics
Article View: 123
PDF Download: 52
Statistics
Number of Journals45
Number of Issues2,160
Number of Articles24,560
Article View19,404,243
PDF Download15,847,358