Advertisement

A combination of IoT and cloud application for automatic shrimp counting

  • Chi-Tsai YehEmail author
  • Ming-Chih Chen
Technical Paper
  • 4 Downloads

Abstract

The pet market is getting growth rapidly in the world, and the ornamental fish occupy the third in the market ranking, behind dogs and cats. According to the statistics of the Ornamental Fish Association, Taiwan has exported 18 million ornamental shrimps annually since 2010. Almost six-tenths of global ornamental shrimps are from Taiwan. OpenCV (Open Source Computer Vision) provides plenty of machine vision applications and often cooperates with the Raspberry Pi to enhance the use of machine engineering for commercial products. This research is, therefore, mainly designed to apply the machine vision to undertake the counting of shrimps automatically. The steps of image processing for accurately counting shrimps are as follows: (1) read the image graphic, (2) filter and remain the sampling color, (3) threshold the image, (4) contour the shrimps in the image (5) count the number. Concerning the performance and reliability, we process the image using Amazon Web Service (AWS) Lambda function. Experimental results of counting shrimps (Neocaridina heteropoda var. red) show that it takes 0.1 s to count 150 shrimps and the precision rate is about 95%.

Notes

References

  1. Brosnan T, Sun D-W (2004) Improving quality inspection of food products by computer vision—a review. J Food Eng 61(1):3–16CrossRefGoogle Scholar
  2. Canalys (2018) Cloud market share q4 2018 and full year 2018. Technical report. https://www.canalys.com/newsroom/cloud-market-share-q4-2018-and-full-year-2018. Accessed 12 Aug 2019
  3. Friedland KD, Ama-Abasi D, Manning M, Clarke L, Kligys G, Chambers RC (2005) Automated egg counting and sizing from scanned images: rapid sample processing and large data volumes for fecundity estimates. J Sea Res 54(4):307–316CrossRefGoogle Scholar
  4. Fuad MAM, Ghani MRA, Ghazali R, Sulaima MF, Jali Mohd Hafiz, Sutikno Tole, Izzuddin Tarmizi Ahmad, Jano Zanariah (2017) A review on methods of identifying and counting aedes aegypti larvae using image segmentation technique. Telkomnika 15(3):1199–1206CrossRefGoogle Scholar
  5. Inc. Amazon Web Services (2019a) Amazon elastic compute cloud documentation. Technical report. https://docs.aws.amazon.com/ec2/index.html. Accessed 12 Aug 2019
  6. Inc. Amazon Web Services (2019b) Amazon simple storage service documentation. Technical report. https://docs.aws.amazon.com/s3/index.html. Accessed 12 Aug 2019
  7. Inc. Amazon Web Services (2019c) Aws lambda documentation. Technical report. https://docs.aws.amazon.com/lambda/index.html. Accessed 12 Aug 2019
  8. Inc. Amazon Web Services (2019d) Aws documentation. Technical report. https://docs.aws.amazon.com/index.html. Accessed 12 Aug 2019
  9. Jamieson P, Herdtner J (2015) More missing the boat–arduino, raspberry pi, and small prototyping boards and engineering education needs them. In: 2015 IEEE Frontiers in education conference (FIE), IEEE, pp 1–6Google Scholar
  10. Khantuwan W, Khiripet N (2012) Live shrimp larvae counting method using co-occurrence color histogram. In: 2012 9th International conference on electrical engineering/electronics, computer, telecommunications and information technology, IEEE, pp 1–4Google Scholar
  11. Li A, Yang X, Kandula S, Zhang M (2011) Comparing public-cloud providers. IEEE Internet Comput 15(2):50–53CrossRefGoogle Scholar
  12. Li A, Yang X, Kandula S, Zhang M (2010) Cloudcmp: comparing public cloud providers. In: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, ACM, pp 1–14Google Scholar
  13. Newbury PF, Culverhouse PF, Pilgrim DA (1995) Automatic fish population counting by artificial neural network. Aquaculture 133(1):45–55CrossRefGoogle Scholar
  14. Silvério FJ, Certal AC, de Ferro CM, Monteiro JF, Cruz JA, Ribeiro R, Silva JN (2016) Automatic system for zebrafish counting in fish facility tanks. In: Campilho A, Karray F (eds) Image analysis and recognition. Springer, Cham, pp 774–782CrossRefGoogle Scholar
  15. Szeliski R (2010) Computer vision: algorithms and applications. Springer Science & Business Media, BerlinzbMATHGoogle Scholar
  16. Ujjainiya L, Chakravarthi MK (2015) Raspberry-PI based cost effective vehicle collision avoidance system using image processing. ARPN J Eng Appl Sci 10(7):3001–3005Google Scholar
  17. Wikipedia (2019) Raspberry pi. Technical report. https://en.wikipedia.org/wiki/Raspberry_Pi. Accessed 12 Aug 2019
  18. Zion B (2012) The use of computer vision technologies in aquaculture—a review. Comput Electron Agric 88:125–132CrossRefGoogle Scholar
  19. Zion B, Doitch N, Ostrovsky V, Alchanatis V, Segev R, Barki A, Karplus I (2006) Ornamental fish fry counting by image processing. Agricultural Research Organization, Bet DaganGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Kaohsiung University of Science and TechnologyKaohsiungTaiwan
  2. 2.Shih Chien University Kaohsiung CampusKaohsiungTaiwan

Personalised recommendations