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.

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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.