Open AccessA review on image processing for fish disease detection
Author(s) -
Siti Naquiah Binti Md Pauzi,
Mustafa Hassan,
Nursyafinaz Md Yusoff,
Nor Hazlyna Harun,
Asyraf Hakimi Abu Bakar,
Beng Chu Kua
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1997/1/012042
Subject(s) - image processing , computer science , artificial intelligence , automation , field (mathematics) , digital image processing , object detection , image segmentation , feature extraction , feature detection (computer vision) , computer vision , segmentation , machine learning , pattern recognition (psychology) , image (mathematics) , engineering , mechanical engineering , mathematics , pure mathematics
Fish disease is considered the main cause for production and economic losses by fish farmers. Fish disease detection and health monitoring is a demanding task by manual method of human visualization. Therefore, any potential approach that is fast, reliable and possesses high automation supports an interest in this issue. Nowadays, with the current emergence in the technology revolution, image processing has been extensively used in disease detection field, especially in human and plant, aiding the human experts in providing the right treatment. Image processing technique offers opportunities to improve the traditional approach in achieving accurate results. Besides, several steps in image processing are adopted including image acquisition, image pre-processing, image segmentation, object detection, feature extraction and classification. The objective of this paper is to briefly review the work established in the fish disease detection field with the use of numerous classification techniques of image processing, including rule-based expert system, machine learning, deep learning, statistical method and hybrid method. The present review recognizes the need for improvement in these image processing approaches that would be valuable for further advancement in terms of performance.