Automatic recognition methods of fish feeding behavior in aquaculture: A review

Li, Daoliang; Zhen-hu, Wang; Wu, Suyuan; Miao, Zheng; Du, Ling; Duan, Yanqing

doi:10.1016/j.aquaculture.2020.735508

Cited by 103 publications

(59 citation statements)

References 94 publications

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“…In recent years advanced technologies such as machine vision, acoustic technology and sensor data fusion have been gradually applied to large-scale and refined aquaculture (Li et al 2020a , b ; Wu et al 2015 ). In order to accurately control and judge the reasonable feeding time, and improve the efficiency of feeding, Wang et al ( 2015 ) proposed a method of using machine vision technology to obtain the feeding parameters of the ingested fish to realize the accurate feeding of fish in aquaculture.…”

Section: Data-driven Decision-makingmentioning

confidence: 99%

“…This method has great error and is also a labor-intensive work, which will bring great stress response and high mortality to fish. With the rapid development of sensor and intelligent computing technology, machine vision has emerged many results in the field of online non-destructive biomass estimation in aquaculture (Li et al 2019 ; Dios et al 2003 ; Li et al 2020a , b ). The length, width, area and circumference of fish in different growth periods are closely related to their weight (Shi et al 2020 ).…”

Section: Data-driven Decision-makingmentioning

confidence: 99%

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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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Section: Data-driven Decision-makingmentioning

confidence: 99%

Section: Data-driven Decision-makingmentioning

confidence: 99%

Intelligent fish farm—the future of aquaculture

Wang

et al. 2021

Aquacult Int

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“…Intraspecific variation linked to ontogenetic shifts in diet could be accounted for by assigning diet to an individual fish count based both on species information and size, where that species-level data exists. Further, technological developments such as video surveys coupled with deep learning may allow the automated estimation of diet and other individual fish characteristics beyond size (Villon et al, 2018;Li et al, 2020). Progress has also been made using gut content DNA metabarcoding to rapidly and confidently estimate diet across diverse food webs.…”

Section: Favor Continuous Traits or Standardized Trait Categories Anmentioning

confidence: 99%

Response and Effect Traits of Coral Reef Fish

2021

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The response-and-effect framework is a trait-based approach that seeks to break down the mechanistic links between ecosystem disturbances, species' traits, and ecosystem processes. We apply this framework to a review of the literature on coral reef fish traits, in order to illustrate the research landscape and structure a path forward for the field. Traits were categorized into five broad groupings: behavioral, life history, morphological, diet, and physiological. Overall, there are fewer studies linking effect traits to ecosystem processes (number of papers on herbivory, n = 14; predation, n = 12; bioerosion, n = 2; nutrient cycling, n = 0) than there are linking response traits to disturbances (climate change, n = 26; fishing, n = 20; pollution, n = 4). Through a network analysis, we show that the size and diet of fish are two of the most common response and effect traits currently used in the literature, central to studies on both ecosystem disturbances and processes. Behavioral and life history traits are more commonly shown to respond to disturbances, while morphological traits tend to be used in capturing ecosystem processes. Pearson correlation coefficients quantifying the strength of the relationships between the most commonly studied process, herbivory, and key effect traits (size, gregariousness, and diel activity) are provided. We find that the most popular cluster of traits used in functional diversity metrics (e.g., functional richness, functional dispersion) is comprised of size, diet, space use/position in the water column, diel activity, gregariousness, and mobility, which encompass three of the broad trait categories. Our assessment of the literature highlights that more research is needed to support an evidence-based selection of traits to understand and predict ecosystem functioning. In synthesizing the literature, we identify research gaps and provide an avenue toward a more robust trait-selection process.

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“…The wavelength range is greater than 760 nm and less than 380 nm. The principle applied in aquaculture is based on the absorption of invisible light in water, resulting in variable brightness, which is not affected by visible light intensity and can yield good imaging results in dark places such as inside animal shelters [ 116 , 117 ]. Most crustacean species are nocturnal, remaining inside shelters during the day and actively foraging outside at night [ 118 ].…”

Section: Behavior Monitoring Based On Machine Visionmentioning

confidence: 99%

Automatic Monitoring of Relevant Behaviors for Crustacean Production in Aquaculture: A Review

Liu

Song

et al. 2021

Animals

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Crustacean farming is a fast-growing sector and has contributed to improving incomes. Many studies have focused on how to improve crustacean production. Information about crustacean behavior is important in this respect. Manual methods of detecting crustacean behavior are usually infectible, time-consuming, and imprecise. Therefore, automatic growth situation monitoring according to changes in behavior has gained more attention, including acoustic technology, machine vision, and sensors. This article reviews the development of these automatic behavior monitoring methods over the past three decades and summarizes their domains of application, as well as their advantages and disadvantages. Furthermore, the challenges of individual sensitivity and aquaculture environment for future research on the behavior of crustaceans are also highlighted. Studies show that feeding behavior, movement rhythms, and reproduction behavior are the three most important behaviors of crustaceans, and the applications of information technology such as advanced machine vision technology have great significance to accelerate the development of new means and techniques for more effective automatic monitoring. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Our purpose is to provide researchers and practitioners with a better understanding of the state of the art of automatic monitoring of crustacean behaviors, pursuant of supporting the implementation of smart crustacean farming applications.

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Automatic recognition methods of fish feeding behavior in aquaculture: A review

Cited by 103 publications

References 94 publications

Intelligent fish farm—the future of aquaculture

Intelligent fish farm—the future of aquaculture

Response and Effect Traits of Coral Reef Fish

Automatic Monitoring of Relevant Behaviors for Crustacean Production in Aquaculture: A Review

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