Water quality monitoring using abnormal tail-beat frequency of crucian carp

Xiao, Gang; Mao, Feng; Cheng, Ziyong; Zhao, Meirong; Mao, Jiafa; Mirowski, Luke

doi:10.1016/j.ecoenv.2014.09.028

Cited by 22 publications

(16 citation statements)

References 39 publications

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“…Furthermore, changes in behaviour when fish are under stress are also a focus of single-camera computer vision research. By increasing stressful conditions, researchers can study how changes in the environment are reflected in behaviour (Kane et al 2004;Xu et al 2006;Xiao et al 2015), which can provide a reference for the analysis of fish feeding behaviour.…”

Section: Individual Feature Analysismentioning

confidence: 99%

“…Therefore, it is difficult to develop these systems and their further application is limited. Finally, light is not always sufficient in most aquaculture facilities, so computer vision methods often require additional light sources that can affect the normal growth of fish and, in serious cases, cause algae outbreaks (Dios et al, 2003;Kane et al 2004;Xiao et al 2015;. This problem can be improved using near-infrared vision.…”

Section: Feed Detectionmentioning

confidence: 99%

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Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

Zhou

Kai

et al. 2017

Reviews in Aquaculture

134

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In aquaculture, feeding is the primary factor determining efficiency and cost, so it is important to know when to stop feeding to maximize efficiency. Until now, fish feeding has been mostly based on artificial discrimination, which is usually time‐consuming and laborious. In recent years, intelligent feeding control according to changes in behaviour and growth status has gained increasing attention. This approach involves many methods as well as monitoring and feedback equipment and can automatically determine the feeding demands of fish. This review summarizes the development of intelligent feeding control methods, such as mathematical models, acoustic methods and computer vision, in aquaculture over the past three decades. All methods have unique application scenarios and models for the culture to which they are most suitable, and the advantages and disadvantages of each method in the laboratory as well as in pond, cage and recirculating aquaculture systems are analysed. Studies show that improvements in sensor accuracy and hardware and software processing speed have promoted the development of new technologies and methods, providing effective or potential support for intelligent feeding control. However, its accuracy and intelligent are still need to be improved to meet the needs of actual feeding scenarios. Through close collaborations between engineers and fish behaviourists, the feeding machine and system will be more elaborate and precise on the basis of the above methods, and the level of intelligence will be further improved.

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Section: Individual Feature Analysismentioning

confidence: 99%

Section: Feed Detectionmentioning

confidence: 99%

Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

Zhou

Kai

et al. 2017

Reviews in Aquaculture

134

View full text Add to dashboard Cite

show abstract

“…Xiao et al . () also exhibited change in swimming speed and tail beat frequently (TBF) as a result of the presence of chemicals in water. Laboratory analysis in case of water quality monitoring provides accurate information, but it is time‐consuming and expensive.…”

Section: Preharvestmentioning

confidence: 99%

“…Kane et al (2004) showed the behavioural changes in fish when exposed with m-amino-benzoic acid ethyl ester methanesulfonate (MS222). Xiao et al (2015) also exhibited change in swimming speed and tail beat frequently (TBF) as a result of the presence of chemicals in water. Laboratory analysis in case of water quality monitoring provides accurate information, but it is time-consuming and expensive.…”

Section: Preharvestmentioning

confidence: 99%

Application of machine vision systems in aquaculture with emphasis on fish: state‐of‐the‐art and key issues

Saberioon

Gholizadeh

Císař

et al. 2016

Reviews in Aquaculture

179

109

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Demands of aquatic products are increasing dramatically during past decades. Also quality assurance has gradually received more attention by both producers and consumers. Thus, fish producers are exploring all possible approaches for improving the productivity and profitability. Monitoring of fish state and behaviour during cultivation may help to improve profitability for producers and also reduce the threat of severe loss because of disease and stress incidents. It is necessary to evaluate and measure quality of fish products in accurate, fast and objective way for meeting the different demands of the fish‐processing industry after harvesting. Traditional methods are usually time‐consuming, expensive, laborious and invasive. Using rapid, inexpensive and noninvasive methods is therefore important and desirable. Optical sensors and machine vision system provide the possibility of developing faster, cheaper and noninvasive methods for in situ and after harvesting monitoring of quality in aquaculture. This review describes the most recent technologies and the suitability of different optical sensors for the fish farming management and also assessment, measurement and prediction of fish products quality. Two major areas of optical sensors applications in aquaculture are discussed in this review: (i) preharvesting and during cultivation; and (ii) post‐harvesting. Finally, accuracy and uncertainty of optical sensors applications in aquaculture are discussed. This review showed that MVSs and optical sensors have found real‐world application based on tremendous possibility offered by digital camera development and increasing the speed of computer‐based processing; however, still new algorithms, methods and re‐engineered sensors need to be developed to meet real‐world requirements.

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“…The behavior analysis of one fish in a small aquarium can be accomplished easily by the expert though it can answer only limited number of research questions (Cha et al, 2012;Xiao et al, 2015). A more important topic is to study fish behavior at a commercial scale with high fish density.…”

Section: Introductionmentioning

confidence: 99%