Volume 11 Number 5 (Sep. 2016)
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JCP 2016 Vol.11(5): 423-431 ISSN: 1796-203X
doi: 10.17706/jcp.11.5.423-431

Computer Assisted Counter System for Larvae and Juvenile Fish in Malaysian Fishing Hatcheries by Machine Learning Approach

Valliappan Raman1, Sundresan Perumal2, Sujata Navaratnam3, Siti Fazilah3
1Multimedia Research Group, School of Computer Science, Universiti Sains Malaysia, Malaysia.
2Faculty of Science and Technology, Universiti Sains Islamic Malaysia, Malaysia.
3Department of Computing, KDU University College, Malaysia.


Abstract—The increased in number and size of larvae and juvenile growth are estimated based on manual approach in fishing hatcheries. There is a high demand for computer assisted software solution for aquaculture research in early detection and recognition of fish population. There exist several companies who have introduced fish detection technologies into the market. Although able to count the number of larvae with a high accuracy rate, the fish counter software’s may encounter difficulties when detecting smaller larvae’s and ants in very early stage of birth period. The main aim of the paper is to propose a framework using machine learning techniques that can be of low cost and efficient system for fish counting and growth study. The expected final result will be a complete preliminary prototype with basic camera setup, focus on larval fish. medium term, improve camera setup and quality; focus on larval and juvenile fish. For the Long term, full fish growth tracking and data mining is implemented. The proposed research in this paper will assist the Malaysian fisheries department to have accuracy on detecting the larvae, juvenile and ants in the hatcheries.

Index Terms—Aquatic, enhancement, segmentation and counting.

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Cite: Valliappan Raman, Sundresan Perumal, Sujata Navaratnam, Siti Fazilah, "Computer Assisted Counter System for Larvae and Juvenile Fish in Malaysian Fishing Hatcheries by Machine Learning Approach," Journal of Computers vol. 11, no. 5, pp. 423-431, 2016.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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