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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 31103

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Paddy/Rice Singulation for Determination of Husking Efficiency and Damage Using Machine Vision
In this study a system of machine vision and singulation was developed to separate paddy from rice and determine paddy husking and rice breakage percentages. The machine vision system consists of three main components including an imaging chamber, a digital camera, a computer equipped with image processing software. The singulation device consists of a kernel holding surface, a motor with vacuum fan, and a dimmer. For separation of paddy from rice (in the image), it was necessary to set a threshold. Therefore, some images of paddy and rice were sampled and the RGB values of the images were extracted using MATLAB software. Then mean and standard deviation of the data were determined. An Image processing algorithm was developed using MATLAB to determine paddy/rice separation and rice breakage and paddy husking percentages, using blue to red ratio. Tests showed that, a threshold of 0.75 is suitable for separating paddy from rice kernels. Results from the evaluation of the image processing algorithm showed that the accuracies obtained with the algorithm were 98.36% and 91.81% for paddy husking and rice breakage percentage, respectively. Analysis also showed that a suction of 45 mmHg to 50 mmHg yielding 81.3% separation efficiency is appropriate for operation of the kernel singulation system.
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[1] M. H. Payman, T. Tavakkoli Hashtjin, and S. Minaei, “Determining the distance between the rollers in rubber roller dehuskers for milling of three popular varieties of rice in Gilan province,” J. Agric. Sci., 1999, vol. 20, pp. 37- 48.
[2] N. Sakai, S. Yonekawa, and A. Matsuzaki, “Two-dimensional image analysis of the shape of rice and its application to separating varieties,” J. Food Engng, 1996, vol. 27, pp. 397-407.
[3] W. Liu, Y. Tao, T. J. Sieben morgen, and H. Chen, “Digital image analysis method for rapid measurement of rice degree of milling,” Cereal Chem., 1998, vol. 75, no. 3, pp. 380-385.
[4] B. J. Lloyd, A. G. Cnossen, and T. J. Sieben morgen, “Evaluation of two methods for separating head rice from brokens for head rice yield determination,” Applied Eng. in Agric., 2001, vol. 17, no. 5, pp. 643-648.
[5] Y. N. Wan, C. M. Lin, and J. F. Chiou, “Rice quality classification using an automatic grain quality inspection system,” Trans. ASAE, 2002, vol. 45, no. 2, pp. 379–387.
[6] M. Tanska, D. Rotkiewicz, W. Kozirok, and I. Konopka, “Measurement of the geometrical features and surface color of rapeseeds using digital image analysis,” Food Res. Inter., 2005, vol. 38, pp. 741-750.
[7] S. Kiani, and A. Jafari, “Crop Detection and Positioning in the Field Using Discriminant Analysis and Neural Networks Based on Shape Features,” J. Agr. Sci. Tech., 2012, vol. 14, pp. 755-765.
[8] I. Golpour, R. Amiri Chayjan, J. Amiri Parian, and J. Khazaei, “Prediction of paddy moisture content during thin layer drying using machine vision and artificial neural networks,” J. Agr. Sci. Tech., 2015, vol. 17, pp. 287-298.
[9] H. J. Shei, and C. S. Lin, “An optical automatic measurement method for the moisture content of rough rice using image processing techniques,” Com. and Elec. in Agric., 2012, vol. 85, pp. 134-139.
[10] Q. Yao, D. X. Xian, Q. J. Liu, B. J. Yang, G. Q. Diao, and J. Tang, “Automated counting of rice planthoppers in paddy fields based on image processing,” J. Integ. Agric., 2014, vol. 13, no. 8, pp. 1736-1745.
[11] C. Sun, T. Liu, C. Ji, M. Jiang, T. Tian, D. Guo, L. Wang, Y. Chen, and X. Liang, “Evaluation and analysis the chalkiness of connected rice kernels based on image processing technology and support vector machine,” J. Cereal Sci., 2014, vol. 60, no. 2, pp. 426-432.
[12] S. Taghadomi-Saberi, M. Omid, Z. Emam-Djomeh, and Kh. Faraji-Mahyari, “Determination of Cherry Color Parameters during Ripening by Artificial Neural Network Assisted Image Processing Technique,” J. Agr. Sci. Tech., 2015, vol. 17, pp. 589-600.
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