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Automatic Grader for Flatfishes Using Machine Vision

  • Control Theory and Applications
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Abstract

Labor shortage and an aging population are driving automation in the aqua and agricultural industries. In a flatfish farm, it is important to sort flatfishes according to their sizes for effective and stable growth. This sorting is often done by human eye-estimation, thereby making it difficult to carry out total inspection of the fishes on the farm. Most fish graders in the industries sort round fishes (fishes with high length to height ratios) but they are inadequate to sort flatfishes. Thus, in this study, an automatic grader for flatfish using machine vision is developed. The grader has three main parts: conveyor belt, machine vision, and sorter. The conveyor belt transfers the fishes to the measurement and sorter parts. When the fish is detected and its length is calculated by image processing, the position of the sorter is controlled by length classification. A low-cost commercial webcam is used, and the sorter of the grader has a simple structure that consists of a single actuator. After several experiments, it was verified that length measurement using machine vision is accurate to within 10 mm, and the grader can sort 30 fishes in a minute. The developed grader minimizes the out-of-water exposure of the fishes compared to the conventional eye-measurement. Hence, it is effective to maintain the quality and freshness of the fishes.

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References

  1. S. H. Eom and J. H. Cho, The 2014 Study on the Policy Measures to Promote the Fishery Equipment Industry, Korea Maritime Institute, Seoul, 2014.

    Google Scholar 

  2. J. Lim, G. Kim, C. Mo, and I. Choi, “Development and performance evaluation of falling-type dried-persimmon weight sorting system utilizing load cell,” Journal of Biosystems Engineering, vol. 40 no. 4, pp. 327–334, December 2015.

    Article  Google Scholar 

  3. N. N. Gaikwad, D. V. K. Samuel, M. K. Grewal, and M. Manjunatha, “Development of orange grading machine on weight basis,” Journal of Agricultural Engineering, vol. 51, no. 3, pp. 1–8, 2014.

    Google Scholar 

  4. P. Ge, Q. Wu, and Y. Sun, “The design of fruit automated sorting system,” Proc. of International Conference on Computer and Computing Technologies in Agriculture, Springer, Boston, pp. 165–170, August 2007.

    Google Scholar 

  5. K. N. Choi, “Noise in load cell signal in an automatic weighing system based on a belt conveyor,” Journal of Sensors, vol. 2017, August 2017.

  6. H. He, P. Huang, W. Cai, Z. Liu, and G. Zhang, “An intelligent signal processing method for high-speed weighing system,” International Journal of Food Engineering, vol. 8, no. 4, 2013.

  7. B. Bayhan, T. M. Sever, and E. Taşkavak, “Length-weight relationships of seven flatfishes (Pisces: Pleuronectiformes) from Aegean Sea,” Turkish Journal of Fisheries and Aquatic Sciences, vol. 8, no. 2, pp. 377–379, 2008.

    Google Scholar 

  8. F. E. Lux, “Length-weight relationships of six New England flatfishes,” Transactions of the American Fisheries Society, vol. 98, no. 4, pp. 617–621, 1969.

    Article  Google Scholar 

  9. U. Özekinci, Ö. Cengiz, A. Ismen, U. Altinagac, and A. Ayaz, “Length-weight relationships of thirteen flatfishes (Pisces: Pleuronectiformes) from Saroz Bay (North Aegean Sea, Turkey),” Journal of Animal and Veterinary Advances, vol. 8, no. 9, pp. 1800–1801, September 2009.

    Google Scholar 

  10. L. A. Jawad, A. Ambuali, J. M. Al-Mamry, and H. K. Al-Busaidi, “Relationships between fish length and otolith length, width and weight of the Indian mackerel Rastrelliger kanagurta (Cuvier, 1817) collected from the Sea of Oman,” Croatian Journal of Fisheries, vol. 69, no. 2, pp. 51–61, 2011.

    Google Scholar 

  11. Ö. Gaygusuz, H. Aydın, Ö. Emiroğlu, N. Top, Z. Dorak, C. G. Gaygusuz, S. Baskurt, and A. S. Tarkan, “Lengthweight relationships of freshwater fishes from the western part of Anatolia, Turkey,” Journal of Applied Ichthyology, vol. 29, no. 1, pp. 285–287, 2013.

    Article  Google Scholar 

  12. M. Saberioon, A. Gholizadeh, P. Cisar, A. Pautsina, and J. Urban, “Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues,” Reviews in Aquaculture, vol. 9, no. 4, pp. 369–387, 2017.

    Article  Google Scholar 

  13. M. Dowlati, M. de la Guardia, and S. S. Mohtasebi, “Application of machine-vision techniques to fish-quality assessment,” TrAC Trends in Analytical Chemistry, vol. 40, pp. 168–179. 2012.

    Article  Google Scholar 

  14. N. J. C. Strachan, “Length measurement of fish by computer vision,” Computers and Electronics in Agriculture, vol. 8, no. 2, pp. 93–104, 1993.

    Article  Google Scholar 

  15. D. A. Luzuriaga, M. O. Balaban, and S. Yeralan, “Analysis of visual quality attributes of white shrimp by machine vision,” Journal of Food Science, vol. 62, no. 1, pp. 113–118, 1997.

    Article  Google Scholar 

  16. C. Costa, F. Antonucci, C. Boglione, P. Menesatti, M. Vandeputte, and B. Chatain, “Automated sorting for size, sex and skeletal anomalies of cultured seabass using external shape analysis,” Aquacultural Engineering, vol. 52, pp 58–64, 2013.

    Article  Google Scholar 

  17. R. Fablet and N. Le Josse, “Automated fish age estimation from otolith images using statistical learning,” Fisheries Research, vol. 72, no. 2–3, pp. 279–290, 2005.

    Article  Google Scholar 

  18. D. Lee, S. Kim, M. Park, and Y. Yang, “Weight estimation of the sea cucumber (Stichopus japonicas) using vision-based volume measurement,” Journal of Electrical Engineering & Technology, vol. 9, no. 6, pp. 2154–2161. 2014.

    Article  Google Scholar 

  19. S. Viazzi, S. Van Hoestenberghe, B. M. Goddeeris, and D. Berckmans, “Automatic mass estimation of Jade perch Scortum barcoo by computer vision,” Aquacultural Engineering, vol. 64, pp 42–48, 2015.

    Article  Google Scholar 

  20. M. B. R. Mollah, M. A. Hasan, M. A. Salam, and M. A. Ali, “Digital image analysis to estimate the live weight of broiler,” Computers and Electronics in Agriculture, vol. 72, no. 1, pp. 48–52, 2010.

    Article  Google Scholar 

  21. D. J. White, C. Svellingen, and N. J. Strachan, “Automated measurement of species and length of fish by computer vision,” Fisheries Research, vol. 80, no. 2–3, pp. 203–210, 2006.

    Article  Google Scholar 

  22. F. Storbeck and B. Daan, “Fish species recognition using computer vision and a neural network,” Fisheries Research, vol. 51, no. 1, pp. 11–15, 2001.

    Article  Google Scholar 

  23. J. Clement, N. Novas, J. A. Gázquez, and F. Manzano-Agugliaro, “High speed intelligent classifier of tomatoes by colour, size and weight,” Spanish Journal of Agricultural Research, vol. 10, no. 2, pp. 314–325. 2012.

    Article  Google Scholar 

  24. S. V. Rautu, A. P. Shinde, N. R. Darda, A. V. Vaghule, C. B. Meshram, and S. S. Sarawade, “Sorting of objects based on colour, weight and type on a conveyor line using PLC,” IOSR Journal of Mechanical Civil Engineering, pp. 4–7. 2017.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Busan National University, 635-4 Jangjeon-dong, Geumjeong-gu, Busan, 135703, Korea

    Hee-Jee Sung, Myeong-Kwan Park & Jae Weon Choi

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  1. Hee-Jee Sung
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  2. Myeong-Kwan Park
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  3. Jae Weon Choi
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Correspondence to Myeong-Kwan Park.

Additional information

This work was supported for two years by Pusan National University Research Grant.

Hee-Jee Sung received his B.S. degree in mechanical engineering from Pusan National University, Korea, in 2013. His research interests are in hydraulic components design, hydraulic control systems, and smart fluids such as magnetorheological, electrorheological, and magnetic fluids.

Myeong-Kwan Park received his M.S. and Ph.D. degrees in mechanical engineering from the Tokyo Institute of Technology, Tokyo, Japan, in 1988 and 1991, respectively. He is currently a Full Professor with the Department of Mechanical Engineering and a researcher at the Research Institute of Mechanical Technology at Pusan National University. His research interests are in hydraulic systems and smart fluids such as magnetic, ER, and MR fluids.

Jae Weon Choi received his B.S., M.S., and Ph.D. degrees all in control and instrumentation engineering from Seoul National University, Seoul, Korea, in 1987, 1989, and 1995, respectively. Since 1996, he has been with School of Mechanical Engineering, Pusan National University. He was a visiting professor at M.I.T., Cambridge, MA, from 2003 to 2004, and at The George Washington University, Washington D.C., from 2011 to 2012. He served as Dean of Office for Planning and Finance from 2013 to 2014, Pusan National University. Since 2018, he has been serving as Dean of both the Research Institute of Mechanical Technology, and the Office for Education Accreditation, Pusan National University, Korea. He served from 2003 to 2011 as an Associate and Editor for the International Journal of Control, Automation and Systems, and served as an Associate Editor for over ten years in Conference Editorial Board of IEEE Control Systems Society since 2000. His current research interests include spectral theory for linear time-varying systems, tracking filter design, and control and sensor network technologies with applications to aquaculture plants.

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Sung, HJ., Park, MK. & Choi, J.W. Automatic Grader for Flatfishes Using Machine Vision. Int. J. Control Autom. Syst. 18, 3073–3082 (2020). https://doi.org/10.1007/s12555-020-0007-7

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  • DOI: https://doi.org/10.1007/s12555-020-0007-7

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