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http://dx.doi.org/10.5392/JKCA.2022.22.03.071

A Comparison of Pre-Processing Techniques for Enhanced Identification of Paralichthys olivaceus Disease based on Deep Learning  

Kang, Ja Young (목포대학교 컴퓨터공학과)
Son, Hyun Seung (목포대학교 컴퓨터공학과)
Choi, Han Suk (목포대학교 컴퓨터공학과)
Publication Information
The Journal of the Korea Contents Association / v.22, no.3, 2022 , pp. 71-80 More about this Journal
Abstract
In the past, fish diseases were bacterial in aqua farms, but in recent years, the frequency of fish diseases has increased as they have become viral and mixed. Viral diseases in an enclosed space called a aqua farm have a high spread rate, so it is very likely to lead to mass death. Fast identification of fish diseases is important to prevent group death. However, diagnosis of fish diseases requires a high level of expertise and it is difficult to visually check the condition of fish every time. In order to prevent the spread of the disease, an automatic identification system of diseases or fish is needed. In this paper, in order to improve the performance of the disease identification system of Paralichthys olivaceus based on deep learning, the existing pre-processing method is compared and tested. Target diseases were selected from three most frequent diseases such as Scutica, Vibrio, and Lymphocystis in Paralichthys olivaceus. The RGB, HLS, HSV, LAB, LUV, XYZ, and YCRCV were used as image pre-processing methods. As a result of the experiment, HLS was able to get the best results than using general RGB. It is expected that the fish disease identification system can be advanced by improving the recognition rate of diseases in a simple way.
Keywords
Aqua Farm; Object Detection; Disease Prediction; Deep Learning; Image Pre-processsing;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
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