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Wiley, Reviews in Aquaculture, 4(9), p. 369-387, 2016

DOI: 10.1111/raq.12143

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Application of Machine Vision Systems in Aquaculture with Emphasis on Fish: State-of-the-Art and Key Issues

Journal article published in 2016 by Mehdi Saberioon ORCID, Asa Gholizadeh, Petr Cisar, Aliaksandr Pautsina, Jan Urban
This paper is available in a repository.
This paper is available in a repository.

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Abstract

Demands of aquatic products are increasing dramatically during past decades.Also quality assurance has gradually received more attention by both producersand consumers. Thus, fish producers are exploring all possible approaches forimproving the productivity and profitability. Monitoring of fish state and beha-viour during cultivation may help to improve profitability for producers and alsoreduce the threat of severe loss because of disease and stress incidents. It is neces-sary to evaluate and measure quality of fish products in accurate, fast and objec-tive way for meeting the different demands of the fish-processing industry afterharvesting. Traditional methods are usually time-consuming, expensive, laboriousand invasive. Using rapid, inexpensive and noninvasive methods is thereforeimportant and desirable. Optical sensors and machine vision system provide thepossibility of developing faster, cheaper and noninvasive methods for in situ andafter harvesting monitoring of quality in aquaculture. This review describes themost recent technologies and the suitability of different optical sensors for the fishfarming management and also assessment, measurement and prediction of fishproducts quality. Two major areas of optical sensors applications in aquacultureare discussed in this review: (i) preharvesting and during cultivation; and (ii)post-harvesting. Finally, accuracy and uncertainty of optical sensors applicationsin aquaculture are discussed. This review showed that MVSs and optical sensorshave found real-world application based on tremendous possibility offered bydigital camera development and increasing the speed of computer-based process-ing; however, still new algorithms, methods and re-engineered sensors need to bedeveloped to meet real-world requirements.