Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Lee, Donggil;Kim, Seonghoon;Park, Miseon;Yang, Yongsu

  • Received : 2014.04.24
  • Accepted : 2014.08.13
  • Published : 2014.11.01


Growth analysis and selection of sea cucumbers (Stichopus japonicas) is typically performed through length or weight measurements. However, because sea cucumbers continuously change shape depending on the external environment, weight measurement has been the preferred approach. Weight measurements require extensive time and labor, moreover it is often difficult to accurately weigh sea cucumbers because of their wet surface. The present study measured sea cucumber features, including the body length, width, and thickness, by using a vision system and regression analysis to generate $R^2$ values that were used to develop a weight estimation algorithm. The $R^2$ value between the actual volume and weight of the sea cucumbers was 0.999, which was relatively high. Evaluation of the performance of this algorithm using cross-validation showed that the root mean square error and worst-case prediction error were 1.434 g and ${\pm}5.879g$, respectively. In addition, the present study confirmed that the proposed weight estimation algorithm and single slide rail device for weight measurement can measure weights at approximately 4,500 sea cucumbers per hour.


Sea cucumber;Weight;Volume;Vision system


  1. T. W. Fulton, The Rate of Growth of Fishers. Edinburgh: Fisheries Board Scotland, Edinburgh, 1904, pp. 141-241.
  2. M. Y. Ibrahim and J. Wang, "Mechatronics applications to fish sorting - part 1: fish size identification," IEEE International Symposium on Industrial Electronics, pp. 1978-1983, 2009.
  3. S. J. Jeong, Y. S. Yang, K. Lee, J. G. Kang, and D. G. Lee, "Vision-based automatic system for non-contact measurement of morphometric characteristics of flatfish," Journal of Electrical Engineering & Technology, vol. 8, pp. 1194-1201, 2013.
  4. N. J. C. Strachan, "Sea trials of a computer vision based fish species sorting and size grading machine," Mechatronics, vol. 4, pp. 773-783, 1994.
  5. D. J. White, C. Svellingen, and N. J. C. Strachan, "Automated measurement of species and length of fish by computer vision," Fish. Res., vol. 80, pp. 203-210, 2006.
  6. M. O. Balaban, G. F. Unal Sengor, M. G. Soriano, and E. G. Ruiz, "Using image analysis to predict the weight of Alaskan salmon of different species," J. Food Sci., vol. 75, pp. E157-E162, 2010a.
  7. S. Damar, Y. Yagiz, M.O. Balaban, S. Ural, A. C. M. Oliveira, and C.A. Crapo, "Prediction of oyster volume and weight using machine vision," J. Aquat. Food Prod. Technol., vol. 15, pp. 3-15, 2007.
  8. M. O. Balaban, M. Chombeau, D. Cirban, and B. Gumus, "Prediction of the weight of Alaskan pollock using image analysis," J. Food Sci., vol. 75, pp. E552-E556, 2010b.
  9. D. J. Lee, R. M. Lane, and G. H. Chang, "Threedimensional reconstruction for high-speed volume measurement," Intelligent Systems and Smart Manufacturing. International Society for Optics and Photonics, 2001.
  10. D.J. Lee, J. Eifert, P. Zhan, and B. Westover, "Fast surface approximation for volume and surface area measurements using distance transform," Opt. Eng., vol. 42, pp. 2947-2955, 2003.
  11. S. Feindel, T. Bennett, and K. Kanwit, "The Maine sea cucumber (Cucumaria frondosa) fishery," Submitted to Standing Legislative Committee on Marine Resources. Department of Marine Resources, Maine,, 2011.
  12. S. Choe and Y. Ohshima, "On the morphological and ecological differences between two commercial forms. 'green' and 'red', of the Japanese common sea cucumber, Stichopus japonicas Selenka," Nippon Suisan Gakkaishi, vol. 27, pp. 97-106, 1961.
  13. B. Gumus and M. O. Balaban, "Prediction of the weight of aquacultured rainbow trout (Oncorhynchus mykiss) by image analysis," J. Aquat. Food Prod. Technol., vol. 19, pp. 227-237, 2010.
  14. Y. Yamana and T. Hamano, "New size measurement for the Japanese sea cucumber Apostichopus japonicas (Stichopodidae) estimated from the body length and body breadth," Fish. Sci., vol.72, pp.585-589, 2006.
  15. J. Y. Yingst, "Factors influencing rates of sediment ingestion by Parastichopus parvimensis (Clark), an epibenthic deposit-feeding holothurians," Estuar. Coast. Shelf Sci., vol. 14, pp. 119-134, 1982.
  16. K. Mitsukuri, "Notes on the habits and life-history of Stichopus japonicus Selenka," 1903.
  17. Y. W. Dong, S. L. Dong, X. L. Tian, F. Wang, and M. Z. Zhang, "Effects of diel temperature fluctuations on growth, oxygen consumption and proximate body composition in the sea cucumber Apostichopus japonicus Selenka," Aquaculture, vol. 255, pp. 514-521, 2006.
  18. R. Gonzalez and R. Woods, Digital Image Processing, New Jersey: Prentice Hall, 2007.
  19. L. N. Zamora and A. G. Jeffs, "Feeding metabolism and growth in response to temperature in juveniles of the Australasian sea cucumber, Australostichopus mollis," Aquaculture, vol. 15, pp. 358-359, 2012.
  20. T. Ji, Y. Dong, and S. Dong, "Growth and physiological responses in the sea cucumber, Apostichopus japonicus Selenka: aestivation and temperature," Aquaculture, vol. 283, pp. 180-187, 2008.
  21. D. L. Paltzat, C. M. Pearce, P. A. Barnes, and R. S. McKinley, "Growth and production of California sea cucumbers (Parastichopus californicus Stimpson) cocultured with suspended Pacific oysters (Crassostrea gigas Thunberg)," Aquaculture, vol. 275, pp. 124-137, 2008.
  22. P. A. Lachenbruch and R. M. Mickey, "Estimation of error rates in discriminant analysis," Technometrics, vol. 10, pp. 1-11, 1968.
  23. S. Theodoridis and K. Koutroumbas, Pattern recognition, San Diego, California: Academic Press, 1999.
  24. J. R. Mathiassen, E. Misimi, B. Toldnes, M. Bondø, and S. O. Ostvik, "High-speed weight estimation of whole herring (Clupea harengus) using 3D machine vision," J. Food Sci., vol. 76, pp. E458-E464, 2011.
  25. F. Stobeck and B. Daan, "Weight estimation of flatfish by means of structured light and image analysis," Fish. Res., vol. 11, pp. 99-108, 1991.

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Supported by : National Fisheries Research and Development Institute (NFRDI)