White whole (WW) grades cashew kernel’s classification using artificial neural network (ANN)

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    In this paper, we introduce an algorithm for the fitting of bounding rectangle to a closed region of cashew kernel in a given image. We propose an algorithm to automatically compute the coordinates of the vertices closed form solution. Which is based on coordinate geometry and uses the boundary points of regions. The algorithm also computes directions of major and minor axis using least-square approach to compute the orientation of the given cashew kernel. More promising results were obtained by extracting shape features of a cashew kernel, it is proved that these features may predominantly use to make the better distinction of cashew kernels of different grades. The intelligent model was designed using Artificial Neural Network (ANN). The model was trained and tested using Back-Propagation learning algorithm and obtained classification accuracy of 89.74%.


  • Keywords

    Cashew kernel; Bounding rectangle; Shape features; Artificial Neural Network.

  • References

      [1] Bart-Plange, A., Mohammed-Kamil, A. P., Addo, A. and Teye, E., “Some physical and mechanical properties of cashew nut and kernel grown in Ghana,” International Journal of Science and Nature, VOL. 3(2), pp. 406-415, 2012.

      [2] B Dhakshinamurthy and Joycy Rodrigues Lyra Kokila, “Performance Valuation High-Speed Colour Sorter for Cashew Kernels,” Acta Agrophysica, Vol. 20(4), pp. 543-553,2013

      [3] Jayas, D.S., Paliwal, J. And Vision, N. S.” Multi-layer neural networks for image analysis of agricultural products,” Journal of Agricultural Engg. Research, vol. 7(2), pp. 119-128, 2000. https://doi.org/10.1006/jaer.2000.0559.

      [4] Rumelhart, D. E., G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323:533–536, 1986. https://doi.org/10.1038/323533a0.

      [5] D Chaudhari and A Samal, “A Simple Method for the fitting of bounding rectangle to closed regions,” Pattern Recognition Vol. 40, pp. 1981-89, 2007. https://doi.org/10.1016/j.patcog.2006.08.003.

      [6] Da-Wen-Sun, “Computer Vision Technology for Food Quality Evaluation,” Food Science and Technology, International series, Elsevier Inc., 2008.

      [7] Narendra V G and Hareesh KS “Cashew Kernels Classification using Color Features “International Journal of Machine Intelligence ISSN: 0975–2927 & E-ISSN: 0975–9166, Volume 3, Issue 2, pp-52-57, Sept. 2011

      [8] Li J, Rao X, Ying Y. Inspection and grading of surface defects of fruits by computer vision. Adv Mater Res 2011;317– 319:956–61. https://doi.org/10.4028/www.scientific.net/AMR.317-319.956.

      [9] Tomas UGJ. Size properties of mangos using image analysis. Int J Biosci Biotechnol 2014; 6:31–42. https://doi.org/10.14257/ijbsbt.2014.6.2.03.

      [10] Patel KK, Kar A, Jha SNJ, Khan MA. Machine vision system: a tool for quality inspection of food and agricultural products. J Food Sci Technol 2012; 49:123–41. https://doi.org/10.1007/s13197-011-0321-4.

      [11] Mahendran R, Jayashree GC, Alagusundaram K. Application of computer vision technique on sorting and grading of fruits and vegetables. J Food Process Technol 2012: S1-001.

      [12] Spreer W, Muller J. Estimating the mass of mango fruit from its geometric dimensions by optical measurement. Comput Electron Agric 2011; 75:125–31. https://doi.org/10.1016/j.compag.2010.10.007.

      [13] Chen YR, Chao K, Kim MS. Machine vision technology for agricultural applications. Comput Electron Agric 2002; 36:173–91. https://doi.org/10.1016/S0168-1699(02)00100-X.

      [14] Brodie JR, Hansen AC, Reid JF. Size assessment of stacked logs via the Hough transform. Trans ASABE 1994; 37:303–10. https://doi.org/10.13031/2013.28085.

      [15] Chhabra M, Gupta A, Mehrotra P, Reel S. Automated detection of fully and partially riped mango by machine vision. Adv Intell Soft Comput 2012; 131:153–64. https://doi.org/10.1007/978-81-322-0491-6_15.

      [16] Dıaz R, Faus G, Blasco M, Blasco J, Molto E. The application of a fast algorithm for the classification of olives by machine vision. Food Res Int 2000; 33:305–9. https://doi.org/10.1016/S0963-9969(00)00041-7.

      [17] Hahn F. Multi-spectral prediction of unripe tomatoes. Biosys Eng 2002; 81:147–55. https://doi.org/10.1006/bioe.2001.0035.

      [18] Momin MA, Kuramoto M, Kondo N, Ido K, Ogawa Y, Shiigi T, et al. Identification of UV-fluorescence components for detecting peel defects of lemon and yuzu using machine vision. Eng Agric Environ Food 2013; 6:165–71. https://doi.org/10.1016/S1881-8366(13)80004-3.

      [19] Kondo N, Kuramoto M, Shimizu H, Ogawa Y, Kurita M, Nishizu T, et al. Identification of fluorescent substance in the mandarin orange skin for machine vision system to detect rotten citrus fruits. Eng Agric Environ Food 2009; 2:54–9. https://doi.org/10.1016/S1881-8366(09)80016-5.

      [20] Blasco J, Aleixos N, Gomez J, Molto E. Citrus sorting by identification of the most common defects using multispectral computer vision algorithm. J Food Eng 2007; 83:384–91. https://doi.org/10.1016/j.jfoodeng.2007.03.027.

      [21] Kurita M, Kondo N, Ninomiya K. Defect detection for tomato grading by use of six color CCD cameras. Jpn Soc Sci High Technol Agric 2006; 18:135–44. https://doi.org/10.2525/shita.18.135.

      [22] Kleynen O, Leemans V, Destain MF. Development of a multispectral vision system for the detection of defects on apples. J Food Eng 2005; 69:41–9. https://doi.org/10.1016/j.jfoodeng.2004.07.008.

      [23] Steinmetz V, Roger JM, Molto E, Blasco J. On-line fusion of color camera and spectrophotometer for sugar content prediction of apples. J Agric Eng Res 1999;73: 207–16. https://doi.org/10.1006/jaer.1999.0407.

      [24] Teoh CC, Mohd Syaifudin AR. Image processing and analysis techniques for estimating the weight of Chokanan mangos. J Trop Agric Food Sci 2007; 35:183–90.

      [25] Haralick, R. M. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67, 786–804. https://doi.org/10.1109/PROC.1979.11328.




Article ID: 14878
DOI: 10.14419/ijet.v9i1.14878

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.