A simple method to quantify fish behavior by forming time-lapse images

Jin, Bong; Bae, Bong Seong; Cho, Sam Kwang; Oh, Jung Keun

doi:10.1016/j.aquaeng.2012.05.001

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…Additionally, the speed of fish during feeding is an important parameter (Cha et al . ), and a single camera‐based system has been developed to quantify behavioural variations (Papadakis et al . ) at each feeding stage with minimal frame loss to provide data to inform intelligent feeding.…”

Section: Feeding Control Methods Based On Computer Visionmentioning

confidence: 99%

“…Machine vision has been used to detect the presence and number of fish to estimate appetite and provide feed automatically (Loo 2013). Additionally, the speed of fish during feeding is an important parameter (Cha et al 2012), and a single camera-based system has been developed to quantify behavioural variations (Papadakis et al 2012) at each feeding stage with minimal frame loss to provide data to inform intelligent feeding. Furthermore, changes in behaviour when fish are under stress are also a focus of single-camera computer vision research.…”

Section: Individual Feature Analysismentioning

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: Feeding Control Methods Based On Computer Visionmentioning

confidence: 99%

Section: Individual Feature Analysismentioning

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

“…The process was validated by artificially increasing step-by-step cohesion (or swimming speed) of the group and comparing reference values of dispersion and swimming speed with estimated values ( Figure 6). Cha et al, proposed a simple vision method for quantifying fish swimming behavior that sampled frames from a video recording of swimming fish and combined them into a time-lapse composite image [113]. Barry used a program called Ctrax based on computer vision to detect fish behaviors [35].…”

Section: Swimming Behaviourmentioning

confidence: 99%

Survey of Fish Behavior Analysis by Computer Vision

Niu¹,

Li²,

Peng³

et al. 2018

J Aquac Res Development

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“…In another report, Cha et al [80] presented a simple method for quantifying behavior using different frames. In this experiment, the authors differentiated the attraction of fish toward two lights (blue and white) and concluded that the blue light attracts more fish.…”

Section: Use Of Sensor Technologymentioning

confidence: 99%

Information fusion in aquaculture: a state-of the art review

Hassan¹,

Hasan²,

Li³

2016

Front. Agr. Sci. Eng.

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Efficient fish feeding is currently one of biggest challenges in aquaculture to enhance the production of fish quality and quantity. In this review, an information fusion approach was used to integrate multisensor and computer vision techniques to make fish feeding more efficient and accurate. Information fusion is a well-known technology that has been used in different fields of artificial intelligence, robotics, image processing, computer vision, sensors and wireless sensor networks. Information fusion in aquaculture is a growing field of research that is used to enhance the performance of an "industrialized" ecosystem. This review study surveys different fish feeding systems using multi-sensor data fusion, computer vision technology, and different food intake models. In addition, different fish behavior monitoring techniques are discussed, and the parameters of water, pH, dissolved oxygen, turbidity, temperature etc., necessary for the fish feeding process, are examined. Moreover, the different waste management and fish disease diagnosis techniques using different technologies, expert systems and modeling are also reviewed.

show abstract

A simple method to quantify fish behavior by forming time-lapse images

Cited by 16 publications

References 25 publications

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

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

Survey of Fish Behavior Analysis by Computer Vision

Information fusion in aquaculture: a state-of the art review

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