Identification of Freshwater Fish Types Using Linear Discriminant Analysis (LDA) Algorithm

 (*)Rini Nuraini Mail (Universitas Nasional, Jakarta Selatan, Indonesia)

(*) Corresponding Author

Submitted: November 21, 2022; Published: November 30, 2022

Abstract

Fish as aquatic animals have several physiological mechanisms that land animals do not have. Differences in habitat cause fish to adapt to environmental conditions, for example as animals that live in water, both in fresh and marine waters. The number of species or types of freshwater fish means knowledge of the types of freshwater fish. Identification of freshwater fish images is useful for the community, because the types of freshwater fish have different nutritional content, prices and processing for each type. Likewise for cultivators, identification of freshwater fish species can be useful for providing fish handling and management because each fish has a different cultivation method. The purpose of this study was to identify freshwater fish species using the Linear Discriminant Analysis (LDA) algorithm based on color feature extraction using HSV. The LDA algorithm has the ability to reduce dimensions by dividing data into several groups by maximizing the distance between groups that are different or more. To make the identification process easier, color feature extraction with HSV can be used to extract a variety of information from the color in the image. Based on the results of the accuracy test, it produces a value of 84.5%, which is included in the good category.

Keywords


Freshwater Fish; Image Identification; Linear Discriminant Analysis; Color Feature Extraction; HSV

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