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

Authors
I. Golpour 1
R. Amiri Chayjan 1
J. Amiri Parian 1
J. Khazaei 2
1 Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Islamic Republic of Iran.
2 Department 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

1. Afshari-Jouybari, H. and Farahnaky, A. 2011. Evaluation of Photoshop Software Potential for Food Colorimetry. J. Food Eng., 106: 170-175.
2. Akin, D. and Akba, B. 2010. A Neural Network (NN) Model to Predict Intersection Crashes Based upon Driver, Vehicle and Roadway Surface Characteristics. Sci. Res. Essays, 5(19): 2837-2847.
3. Aguilera, J. M. 2003. Drying and Dried Products under the Microscope. Int. J. Food Sci. Tech., 9(3):137–143.
4. Arabhosseini, A., Huisman, W., Van Boxtel, A. and Muller, J. 2009. Modeling of Thin Layer Drying of Tarragon (Artemisia dracunculus L.). Indust. Crops Prod., 29: 53–59.
5. Arumuganathan, T., Manikantan, M.R., Rai, R. D., Anandakumar, S. and Khare, V. 2009. Mathematical Modeling of Drying Kinetics of Milky Mushroom in a Fluidized Bed Dryer. Int. Agrophysics, 23: 1-7.
6. ASAE. 2000. ASAE Standard S352.2. Moisture Measurement: Unground Grain and Seeds. 47th Edition. ST. Joseph, MI, USA.
7. Bakhshipour, A., Jafari, A. and Zomorodian, A. 2012. Vision Based Features in Moisture Content Measurement during Raisin Production. World Appl. Sci. J., 17 (17): 860-869.
8. Bala, B. K., Ashraf. M. A., Uddin, M. A. and Janjai, S. 2005. Experimental and Neural Network Prediction of the Performance of a Solar Tunnel Drier for Drying Jackfruit Bulbs and Leather. J. Food Process Eng., 28: 552-566.
9. Bingol, G., Roberts, J. S., Murat, O., Balaban, M. and Devres, Y. O. 2012. Effect of Dipping Temperature and Dipping Time on Drying Rate and Color Change of Grapes. Drying Technol., 30: 597-606.
10. Cao, C. and Wang, X. B. 2002. Automatic Control of Grain Driers. Modernizing Agric., 2: 40–44.
11. Chayjan, R. A., Khoshtaghaza, M. H., Montazer, G. A. and Minaei, S. 2007. Estimation of Paddy Layers Moisture Content Using Artificial Neural Networks. Iran. J. Agric. Sci., 38(1): 113-123.
12. Couto, S. M. 2002. Modeling Grain Drying as Discharge of an RC Electrical Circuit. Trans. ASAE, 45: 1445–54.
13. Doymaz, I. 2007. Air-drying Characteristics of Tomatoes. J. Food Eng., 78: 1291-1297.
14. Ertekin, C. and Yaldiz, O. 2004. Drying of Eggplant and Selection of a Suitable Thin Layer Drying Model. J. Food Eng., 63: 349-359.
15. Gupta, T., Ahmed, J., Shivhare, U. S. and Raghavan, G. S. V. 2002. Drying Characteristics of Red Chili. Drying Technol., 20: 1975–1987.
16. Hunt, R.W.G. 1991. Measuring Color. 2nd Edition, Ellis Horwood, New York, PP. 313.
17. Kaleta, A. and Górnicki, K. 2010. Some Remarks on Evaluation of Drying Models of Red Beet Particles. Energy Conv. Manage., 51: 2967–2978.
18. Kassem, A. S., Shokr, A. Z., Aboukarima, A. M. and Hamed, I. Y. 2010. Estimation of Moisture Ratio of Thompson Seedless Grapes Undergoing Microwave Drying Using Artificial Neural Network and Regression Models. J. Saudi Soc. Agric. Sci., 9(1): 23–47.
19. Kim, H. K., Jo, K. M., Kwon, D. Y. and Park, M. H. 1992. Effects of Drying tTemperature and Sulfiting on the Qualities of Dried Garlic Slices. J. Korean Agric. Chem. Soc., 35(1): 6–9.
20. Lee, H. S. and Coates, G. A. 1999. Thermal Pasteurization Effects on Colour of Red Grape Fruit Juices. J. Food Sci., 64: 663–666.
21. Madamba, P. S., Driscoll, R. H. and Buckle, K. A. 1996. The Thin-layer Drying Characteristics of Garlic Slices. J. Food Eng., 29(1): 75–97.
22. Mehdizadeh, Z. and Zomorodian, A. 2009. A Study of the Effect of Solar Drying System on Rice Quality. J. Agr. Sci. Tech., 11: 527-534.
23. Menli, T., Kirmaci, V. and Usta, H. 2009. Modeling of Freeze Drying Behaviors of Strawberries by Using Artificial Neural Network. J. Thermal Sci. Technol., 29(2): 11-21.
24. Midilli, A., Olgun, H. and Ayhan, T. 1999. Experimental Studies on Mushroom and Pollen Drying. Int. J. Energy Res., 23(13): 1143–1152.
25. Mohammed, A. A. 2011. Prediction of Quality Indices during Drying of Okra Pods in a Domestic Microwave Oven Using Artificial Neural Network Model. African J. Agric. Res., 6(12): 2680–2691.
26. Mohebbi, M., Akbarzadeh, M.R., Shahidi, F., Moussavi, M. and Ghoddusi, H. B. 2009. Computer Vision Systems (CVS) for Moisture Content Estimation in Dehydrated Shrimp. Comput. Elect. Agric., 69: 128–134.
27. Mohebbi, M., Shahidi, F., FathI, M., Ehtiati, A. and Noshad, M. 2011. Prediction of Moisture Content in Pre-osmosed and Ultrasounded Dried Banana Using Genetic Algorithm and Neural Network. Food Bioprod. Proces., 84(4): 362-366.
28. Momenzadeh, L., Zomorodian, A. and Mowla, A. 2012. Applying Artificial Neural Network for Drying Time Prediction of Green Pea in a Microwave Assisted Fluidized Bed Dryer. J. Agric. Sci. Technol.,14: 513–522.
29. Patak, P. T. 1991. Thin layer Drying Model for Rapeseed. Trans. ASAE, 34: 2505–2508.
30. Poonnoy, P., Tansakul, A. and Chinnan, M. S. 2007a. Artificial Neural Network Modeling for Temperature and Moisture Content Prediction in Tomato Slices Undergoing Microwave-vacuum Drying. J. Food Sci., 72(1): 41– 47.
31. Poonnoy, P., Tansakul, A. and Chinnan, M. S. 2007b. Estimation of Moisture Ratio of a Mushroom Undergoing Microwave-vacuum Drying Using Artificial Neural Network and Regression Models. Chem. Prod. Process Model., 2(3): 1-13.
32. Ratti, C., Araya-Farias, M., Mendez-Lagunas, L. and Makhlouf, J. 2007. Drying of Garlic (Allium sativum) and Its Effect on Allicin Reterntion. Drying Technol., 25(2): 349–356.
33. Sacilik, K. and Elicin, A. K. 2006. The Thin Layer Drying Characteristics of Organic Apple Slices. J. Food Eng., 73: 281–289.
34. Shafafi Zenozian, M., Feng, H., Razavi, S. M. A., Shahidi, F. and Pourreza, H. R. 2008. Image Analysis and Dynamic Modeling of Thin-layer Drying of Osmotically Dehydrated Pumpkin. J. Food Process. Preserv., 32: 88–102.
35. Sun, D. W. and Woods, J. L. 1994. Low Temperature Moisture Transfer Characteristics of Wheat in Thin Layers. Trans. ASAE., 37: 1919–28.
36. Wan, P., Long, C. and Huang, X. 2011. A Detection Method of Rice Process Quality Based on the Color and BP Neural Network. Conference of the 4th Computer and Computing Technologies in Agriculture, 22-25 October, Nanchang, China, 344: 25–34.
37. Yaldiz, O., Ertekin, C. and Uzun, H. I. 2001. Mathematical Modeling of Thin Layer Solar Drying of Sultan Grapes. Energy, 26: 457–465.
38. Yam, K. L. and Papadakis, S. E. 2004. A Simple Digital Imaging Method for Measuring and Analyzing Color of Food Surfaces, J. Food Eng., 61: 137–142.
Journal of Agricultural Science and Technology
Volume 17, Issue 2
March and April 2015
Pages 287-298