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Echelon Feeder of Brown Rice for On-line Inspection Using Image Processing

영상처리식 온라인 품위판정을 위한 현미의 정렬공급장치

  • Kim, Tae-Min (Korea Advanced Institute of Science and Technology) ;
  • Noh, Sang-Ha (Department of Biosystems Engineering, Seoul National University)
  • Received : 2010.02.17
  • Accepted : 2010.06.15
  • Published : 2010.06.25

Abstract

An automatic echelon feeder of brown rice was presented for quality inspection system using color image processing. A echelon feeder was developed with vibratory feeder and cylindrical indent singulator having oblique light. The vibratory feeder consisted of a hopper, electromagnetic vibrator and multichannel grooves and supply the grain sample to the singulator. The feeding performance such as feed rate, blocking frequency of the channel was dependent on the size of groove and vibration pattern. A cylindrical indent singulator consisted of a rotating cylinder, prisms and a tungsten-halogen light source. It delivered grain kernels under the camera in a echelon form and illuminate the kernels with oblique ray and ambient light. The size of the indents installed on the surface of the rotating cylinder was determined by the dimensions of the paddy and a small triangular prism was placed in each indent to apply $ 20^{\circ}$ oblique light to the grain kernel.

Keywords

References

  1. Ballard, D. H. and C. M. Brown. 1982. Computer Vision. Prentice-Hall, Englewood Cliffs, USA.
  2. Casady, W. W. and M. R. Paulsen. 1988. An automated kernel positioning device for computer vision analysis of grain. ASAE Paper No. 88-3051. ASAE, St. Josept, MI, USA.
  3. Cooper, T. M., A. G. Berlage. 1986. Integrating database and machine vision seed measuring process. ASAE Paper No. 86-3062. St. Joseph, MI, USA.
  4. Ding, K., R. V. Morey, W. F. Wilcke and D. J. Hansen. 1990. Corn quality evaluation with computer vision. ASAE Paper No. 90-3532. St. Joseph, MI, USA.
  5. Gonzalez, R. C. and R. E. Woods. 1992. Digital Image Processing. Addison Wesley, New York, USA.
  6. Gunasekaran, S., T. M. Cooper and A. G. Berlage. 1988. Evaluating quality factors of corn and soybeans using a computer vision system. Transactions of ASAE 31(4):1264-1271. https://doi.org/10.13031/2013.30856
  7. Hwang, C. S. 1996. An Algorithm for Discrimination of Brown Rice Kernels using Image Analysis. Seoul National University Master Thesis. (In Korean)
  8. Inoue, S. 1986. Video Microscopy. Plenum Press, New York, USA.
  9. Kranzler, G. A. 1985. Applying digital image processing in agriculture. Agricultural Engineering 66(3):11-13.
  10. Kim, T. M. and S. H. Noh. 2010. On-line inspection algorithm of brown rice using image processing. Journal of Biosystem Engineering 35(2):138-145 (In Korean) https://doi.org/10.5307/JBE.2010.35.2.138
  11. Lee, J. W. 1992. Computer Vision System for Analysis of Geometrical Characteristics of Agricultural Products and Microscopic Particles. Seoul National University Ph.D. thesis. (In Korean)
  12. Liao, K., M. R. Paulsen, J. F. Reid, B. C. Ni and E. P. Bonifacio-Maghirang. 1993. Corn kernel breakage classification by machine vision using a neural network classifier. Transactions of ASAE 36(6):1949-1953. https://doi.org/10.13031/2013.28547
  13. Matsuhisa T. and A. Hosokawa. 1983. Quantitative measurement of cracks and glutinous rice grain quality by image processing. Japanese Journal of Agricultural Machinery 45 (3):357-368
  14. McDonald, T. and Y. R. Chen. 1990. Application of morphological image processing in agriculture. Transactions of ASAE 33(4):1345-1352.
  15. Miller, B. K. and M. J. Delwiche. 1991. Peach defect detection with machine vision. Transactions of ASAE 34(6): 2588-2597. https://doi.org/10.13031/2013.31911
  16. Ni B., M. R. Paulsen, K. Liao and J. F. Reid. 1993. An automated corn kernel inspection system using machine vision. ASAE paper No. 93-3032. St. Joseph, MI, USA.
  17. Noh, S. H., C. S. Hwang and J. W. Lee. 1997. Algorithm for discriminating of brown rice kernels using machine vision. Korean Society for Agricultural Machinery 22(3):295-302. (In Korean)
  18. Paulsen, M. R. and W. F. McClure. 1985. Illumination for computer vision systems. Transactions of ASAE 29(5):1398-1404.
  19. Zayas, I., H. Converse and J. Steele. 1990. Discrimination of whole from broken corn kernels with image analysis. Transactions of ASAE 33(5):1642-1646. https://doi.org/10.13031/2013.31521

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