Spatial behavioral characteristics and statistics-based kinetic energy modeling in special behaviors detection of a shoal of fish in a recirculating aquaculture system

Zhao, Jian; Gu, Zhenyang; Shi, Mingming; Lu, Huishan; Li, Jianping; Shen, M.; Ye, Zhangying; Zhu, Songming

doi:10.1016/j.compag.2016.06.025

Cited by 54 publications

(28 citation statements)

References 47 publications

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“…As long as the length of the fish is calculated, the weight of the fish can be roughly inferred according to the growth model formula (Irigoyen‐Arredondo et al., 2016; Lamprakis & Kallianiotis, 2003). The use of cameras and other equipment is an important part of fish monitoring and behaviour analysis (Zhao et al., 2016, 2019). We can only use machines to analyse how they move in relation to the state of fish.…”

Section: Resultsmentioning

confidence: 99%

“…By modifying these two indicators based on fish behaviour, relevant behavioural changes can be quantified. Zhao's kinetic energy model can be adjusted based on the speed of the fish, the steering angle and the area in which it is located so that it has improved performance in the detection of special behaviours (Zhao et al., 2016). This study avoids the complex tracking of individual fish due to the reflection of the surface light flow and can be used to determine the overall condition of the fish population.…”

Section: Fish Behaviour Analysismentioning

confidence: 99%

“…(2018) used koi as the experimental object and proposed a method of monitoring fish feeding behaviour based on the shape and texture characteristics of fish images and a back propagation (BP) neural network, and this method provided guidance of precise feeding control in aquaculture. The improved kinetic energy model used by Zhao can quantify the feeding intensity of fish (Zhao et al., 2016). The model is obtained from the discrete degree of fish school and spatial behaviour characteristics.…”

Section: Fish Intelligent Feeding Decisionmentioning

confidence: 99%

See 2 more Smart Citations

Application of computer vision in fish intelligent feeding system—A review

Jiang

Wei

et al. 2020

Aquac. Res.

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Section: Resultsmentioning

confidence: 99%

Section: Fish Behaviour Analysismentioning

confidence: 99%

Section: Fish Intelligent Feeding Decisionmentioning

confidence: 99%

See 1 more Smart Citation

Application of computer vision in fish intelligent feeding system—A review

Jiang

Wei

et al. 2020

Aquac. Res.

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“…Later, the acceleration feature (Chen et al, 2017) and motion features with higher discrimination (e.g. kinetic energy (Zhao et al, 2016), displacement, etc.) have been applied with success and can be further developed and combined with the position features of aggressive pigs (e.g.…”

Section: Image Analysismentioning

confidence: 99%

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

Norton

Chen

Larsen

et al. 2019

Animal

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Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems. Contrary to some current opinions, it can offer an alternative philosophy to 'farming by numbers'. This review addresses the key technology-oriented approaches to monitor animals and demonstrates how image and sound analyses can be used to build 'digital representations' of animals by giving an overview of some of the core concepts of PLF tool development and value discovery during PLF implementation. The key to developing such a representation is by measuring important behaviours and events in the livestock buildings. The application of image and sound can realise more advanced applications and has enormous potential in the industry. In the end, the importance lies in the accuracy of the developed PLF applications in the commercial farming system as this will also make the farmer embrace the technological development and ensure progress within the PLF field in favour of the livestock animals and their well-being. ImplicationsThis work address the main approaches utilised in developing precision livestock farming tools. Precision livestock farming is an approach that enables the farmer with more objective information about the animal to make better choices about the sustainability of their production system. This paper demonstrates some of the key solutions and the approaches taken to develop technologies with sound and image analyses. †

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“…Abnormal behaviours, especially rapid acceleration and agglomeration, by a whole school of fish may reflect information relevant to feeding operations and fish appetite (von Campenhausen et al 1981;Conrad et al 2011). Velocity and position are often used to quantify and analyse fish feeding behaviour, and the scatter and movement intensity of populations of rainbow trout (Sadoul et al 2014), salmon (Liu et al 2014), tilapia (Zhangying et al 2016;Zhao et al 2016) and sole (Duarte et al 2009) have been quantified. This analysis can be performed during image processing and feature extraction by summing the changes in the intensity of adjacent frames caused by fish movement of the fish or quantifying the difference between two consecutive frames (Gonzalez & Woods 2006).…”

Section: Group Features Analysismentioning

confidence: 99%

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

Zhou

Kai

et al. 2017

Reviews in Aquaculture

112

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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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Spatial behavioral characteristics and statistics-based kinetic energy modeling in special behaviors detection of a shoal of fish in a recirculating aquaculture system

Cited by 54 publications

References 47 publications

Application of computer vision in fish intelligent feeding system—A review

Application of computer vision in fish intelligent feeding system—A review

Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer

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

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