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FishDeTec: a fish identification application using image recognition approach


Citation

Mohd Rum, Siti Nurulain and Nawawi, Fariz Az Zuhri (2021) FishDeTec: a fish identification application using image recognition approach. International Journal of Advanced Computer Science and Applications, 12 (3). 102 - 106. ISSN 2158-107X; ESSN: 2156-5570

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.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.14569/IJACSA.2021.0120312
Publisher: Science and Information Organization
Keywords: Component; Freshwater fish; Fish species recognition; FishDeTec; Convolutional Neural Network (CNN); VGG16
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 26 Aug 2022 08:56
Last Modified: 26 Aug 2022 08:56
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.14569/IJACSA.2021.0120312
URI: http://psasir.upm.edu.my/id/eprint/97350
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