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DOI: 10.14569/IJACSA.2021.0120312
PDF

FishDeTec: A Fish Identification Application using Image Recognition Approach

Author 1: Siti Nurulain Mohd Rum
Author 2: Fariz Az Zuhri Nawawi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 3, 2021.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The underwater imagery processing is always in high demand, especially the fish species identification. This activity is as important not only for the biologist, scientist, and fisherman, but it is also important for the education purpose. It has been reported that there are more than 200 species of freshwater fish in Malaysia. Many attempts have been made to develop the fish recognition and classification via image processing approach, however, most of the existing work are developed for the saltwater fish species identification and used for a specific group of users. This research work focuses on the development of a prototype system named FishDeTec to the detect the freshwater fish species found in Malaysia through the image processing approach. In this study, the proposed predictive model of the FishDeTec is developed using the VGG16, is a deep Convolutional Neural Network (CNN) model for a large-scale image classification processing. The experimental study indicates that our proposed model is a promising result.

Keywords: Component; Freshwater Fish; fish species recognition; FishDeTec; Convolutional Neural Network (CNN); VGG16

Siti Nurulain Mohd Rum and Fariz Az Zuhri Nawawi, “FishDeTec: A Fish Identification Application using Image Recognition Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 12(3), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120312

@article{Rum2021,
title = {FishDeTec: A Fish Identification Application using Image Recognition Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120312},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120312},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {3},
author = {Siti Nurulain Mohd Rum and Fariz Az Zuhri Nawawi}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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