Journal of Biosystems Engineering

  • 제36권1호
  • /
  • Pages.40-47
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  • 2011
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  • 1738-1266(pISSN)
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  • 2234-1862(eISSN)
한국농업기계학회 (Korean Society for Agricultural Machinery)
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SVM(Support Vector Machine)을 이용한 묘삼 자동등급 판정 알고리즘 개발에 관한 연구

Study on the Development of Auto-classification Algorithm for Ginseng Seedling using SVM (Support Vector Machine)

  • Oh, Hyun-Keun (Dept. of Biosystems Machinery Engineering, Chungnam National University) ;
  • Lee, Hoon-Soo (Dept. of Biosystems Machinery Engineering, Chungnam National University) ;
  • Chung, Sun-Ok (Dept. of Biosystems Machinery Engineering, Chungnam National University) ;
  • Cho, Byoung-Kwan (Dept. of Biosystems Machinery Engineering, Chungnam National University)
  • 투고 : 2010.09.14
  • 심사 : 2011.01.14
  • 발행 : 2011.02.25
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초록

Image analysis algorithm for the quality evaluation of ginseng seedling was investigated. The images of ginseng seedling were acquired with a color CCD camera and processed with the image analysis methods, such as binary conversion, labeling, and thinning. The processed images were used to calculate the length and weight of ginseng seedlings. The length and weight of the samples could be predicted with standard errors of 0.343 mm, and 0.0214 g respectively, $R^2$ values of 0.8738 and 0.9835 respectively. For the evaluation of the three quality grades of Gab, Eul, and abnormal ginseng seedlings, features from the processed images were extracted. The features combined with the ratio of the lengths and areas of the ginseng seedlings efficiently differentiate the abnormal shapes from the normal ones of the samples. The grade levels were evaluated with an efficient pattern recognition method of support vector machine analysis. The quality grade of ginseng seedling could be evaluated with an accuracy of 95% and 97% for training and validation, respectively. The result indicates that color image analysis with support vector machine algorithm has good potential to be used for the development of an automatic sorting system for ginseng seedling.

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참고문헌

  1. C. S. Kim and J. Y. Rhee. 1997. Computer Vision System for Automatic Grading of Ginseng. Journal of the Korean Society for Agricultural Machinery. 22(2):227-236
  2. D. J. Kim and J. E. Ha. 2005. Digital image processing using Visual C++. Sitech Media.
  3. G. M. Yang, K. H. Cho and J. R. Park. 2005. Development of an Automatic Sweet Potato Sorting System using Image Processing. Journal of Biosystems Engineering 30(3):172-178. https://doi.org/10.5307/JBE.2005.30.3.172
  4. H. K. Lee. 2007. Digital Image Processing Theory and Practice (Visual C++ approach). Sitech Media.
  5. J. C. Lee, D. J. Ahn, J. S. Byen and J. S. Jo. 1998. Relationships Between Growth Characteristics as well as Mineral Consents of Ginseng Seedlings and Yield of Ginseng Roots. J. Ginseng Res. 22(4):294-298.
  6. M. J. Koo, D. K. Hwang, W. R. Lee, J. H. Kim and J. M. Seo. 2008. Developmemt of a low-cost fruit classification system based on Digital images. Journal of the Korea Society of Computer and Information 13(6):155-162.
  7. S. B. Kim, H. S. Kim, S. K. Kim and Y. B. Jeon. 1998. Development of a Fish Size Grading Machine Using an Image Processing Method. Korean Fish, Soc. 31(3):317-322.
  8. S. H. Noh, J. W. Lee and I. G. Hwang. 1995. Fruit Grading Algorithms of Multi-purpose Fruit Grader Using Black & White Image Processing System. Journal of the Korean Society for Agricultural Machinery 20(1):95-103.