Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Automatic Extraction of Lean Tissue for Pork Grading

  • Cho, Sung-Ho (Agri. Machinery Certification Team, Foundation of Agri. Tech. Commercialization & Transfer) ;
  • Huan, Le Ngoc (Robotics Lab.) ;
  • Choi, Sun (USDA, ARS. Russell Research Center) ;
  • Kim, Tae-Jung (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Shin, Wu-Hyun (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University) ;
  • Hwang, Heon (Department of Bio-Mechatronic Engineering, College of Biotechnology & Bioengineering, Sungkyunkwan University)
  • Received : 2014.07.11
  • Accepted : 2014.08.20
  • Published : 2014.09.01
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Abstract

Purpose: A robust, efficient auto-grading computer vision system for meat carcasses is in high demand by researchers all over the world. In this paper, we discuss our study, in which we developed a system to speed up line processing and provide reliable results for pork grading, comparing the results of our algorithms with visual human subjectivity measurements. Methods: We differentiated fat and lean using an entropic correlation algorithm. We also developed a self-designed robust segmentation algorithm that successfully segmented several porkcut samples; this algorithm can help to eliminate the current issues associated with autothresholding. Results: In this study, we carefully considered the key step of autoextracting lean tissue. We introduced a self-proposed scheme and implemented it in over 200 pork-cut samples. The accuracy and computation time were acceptable, showing excellent potential for use in online commercial systems. Conclusions: This paper summarizes the main results reported in recent application studies, which include modifying and smoothing the lean area of pork-cut sections of commercial fresh pork by human experts for an auto-grading process. The developed algorithms were implemented in a prototype mobile processing unit, which can be implemented at the pork processing site.

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References

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