Survey of Fish Behavior Analysis by Computer Vision

Niu, Bingshan; Li, Guangyao; Peng, Fang-Le; Wu, Jing; Long, Zhang; Li, Zhenbo

doi:10.4172/2155-9546.1000534

Cited by 27 publications

(17 citation statements)

References 140 publications

(170 reference statements)

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“…Previous ecological work has tracked fish in controlled environments (Bingshan et al, 2018;Papadakis et al, 2014;Qian et al, 2016;Sridhar et al, 2019), used automated detections and counts as proxies for movement (Marini et al, 2018), and, most recently, used automated movement tracking algorithms to quantify movement (Francisco et al, 2020). Automated approaches tested in "real-world" scenarios provide the best indication and evidence that computer vision is a robust technique for fish monitoring in aquatic ecosystems.…”

Section: Discussionmentioning

confidence: 99%

Automatic detection of fish and tracking of movement for ecology

Lopez‐Marcano

Jinks

Buelow

et al. 2021

Ecology and Evolution

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Section: Discussionmentioning

confidence: 99%

Automatic detection of fish and tracking of movement for ecology

Lopez‐Marcano

Jinks

Buelow

et al. 2021

Ecology and Evolution

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“…It is affected by many factors such as fish species, poison type and exposure environment. The research on fish behavior can provide important theoretical data for aquaculture (Niu et al 2018 ; Saberioon et al 2016 ). In the process of aquaculture the main concerns of fish behavior are swimming behavior, feeding behavior, reproductive behavior, aggression and carnivorous behavior.…”

Section: Data-driven Decision-makingmentioning

confidence: 99%

Intelligent fish farm—the future of aquaculture

Wang

et al. 2021

Aquacult Int

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With the continuous expansion of aquaculture scale and density, contemporary aquaculture methods have been forced to overproduce resulting in the accelerated imbalance rate of water environment, the frequent occurrence of fish diseases, and the decline of aquatic product quality. Moreover, due to the fact that the average age profile of agricultural workers in many parts of the world are on the higher side, fishery production will face the dilemma of shortage of labor, and aquaculture methods are in urgent need of change. Modern information technology has gradually penetrated into various fields of agriculture, and the concept of intelligent fish farm has also begun to take shape. The intelligent fish farm tries to deal with the precise work of increasing oxygen, optimizing feeding, reducing disease incidences, and accurately harvesting through the idea of “replacing human with machine,” so as to liberate the manpower completely and realize the green and sustainable aquaculture. This paper reviews the application of fishery intelligent equipment, IoT, edge computing, 5G, and artificial intelligence algorithms in modern aquaculture, and analyzes the existing problems and future development prospects. Meanwhile, based on different business requirements, the design frameworks for key functional modules in the construction of intelligent fish farm are proposed.

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“…When it comes to building computational models for behavior analysis, Niu et al 38 provide a comprehensive survey of recent studies analyzing fish behavior through computer vision. The fish behaviors described in this study include schooling, swimming, stress response, feeding, taxis, reproduction and migration.…”

Section: Classifier Accuracy Precisionmentioning

confidence: 99%

An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor

Banerjee

Alvey

Brown

et al. 2021

Sci Rep

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The analysis of fish behavior in response to odor stimulation is a crucial component of the general study of cross-modal sensory integration in vertebrates. In zebrafish, the centrifugal pathway runs between the olfactory bulb and the neural retina, originating at the terminalis neuron in the olfactory bulb. Any changes in the ambient odor of a fish’s environment warrant a change in visual sensitivity and can trigger mating-like behavior in males due to increased GnRH signaling in the terminalis neuron. Behavioral experiments to study this phenomenon are commonly conducted in a controlled environment where a video of the fish is recorded over time before and after the application of chemicals to the water. Given the subtleties of behavioral change, trained biologists are currently required to annotate such videos as part of a study. This process of manually analyzing the videos is time-consuming, requires multiple experts to avoid human error/bias and cannot be easily crowdsourced on the Internet. Machine learning algorithms from computer vision, on the other hand, have proven to be effective for video annotation tasks because they are fast, accurate, and, if designed properly, can be less biased than humans. In this work, we propose to automate the entire process of analyzing videos of behavior changes in zebrafish by using tools from computer vision, relying on minimal expert supervision. The overall objective of this work is to create a generalized tool to predict animal behaviors from videos using state-of-the-art deep learning models, with the dual goal of advancing understanding in biology and engineering a more robust and powerful artificial information processing system for biologists.

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Survey of Fish Behavior Analysis by Computer Vision

Cited by 27 publications

References 140 publications

Automatic detection of fish and tracking of movement for ecology

Automatic detection of fish and tracking of movement for ecology

Intelligent fish farm—the future of aquaculture

An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor

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