Fish as a source of acoustic signal measurement in an aquaculture tank: Acoustic sensor based time frequency analysis

Hassan, Shahbaz Gul; Ahmad, Shakeel; Iqbal, Shafqat; Elahi, Ehsan; Hasan, Murtaza; Li, Bingbing; Zhou, Zhiyan; Abbas, Adnan; Song, Cancan

doi:10.25165/j.ijabe.20191203.4238

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Therefore, the hydrophones in the passive acoustic system placed into farm ponds will be equipped with an amplifier attached to a digital acquisition unit [ 53 ]. The digital acquisition unit is attached to a personal computer, which is used to provide valuable information and this process is often undertaken by complex and specific algorithms [ 54 ]. Many investigations have indicated that when some behaviors occur, crustaceans emit different sound frequencies, including feeding [ 55 ], mating [ 56 ], carapace vibrations [ 57 ], snap [ 58 ], and stick and slip friction [ 59 , 60 , 61 ].…”

Section: Behavior Monitoring Methods Based On Acoustic Technologymentioning

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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Section: Behavior Monitoring Methods Based On Acoustic Technologymentioning

confidence: 99%

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

Liu

Song

et al. 2021

Animals

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“…22 The method is widely used for spatiotemporal distribution behaviour, species detection and aquatic organism population assessment. 23,24 In recent years, with the rapid development of microelectronics, information technology and computer hardware, numerous acoustic systems like multi-beam echo sounder systems (MBES), 25 sonar imaging systems, 26 hydrophone monitoring systems 27 and acoustic telemetry systems, have been widely used. 28 MBES and the sonar imaging system, which are based on active acoustic techniques, have shown superiority in aquatic organism biomass estimation, behavioural analysis and spawning and reproductive behaviour studies in cultured water bodies.…”

Section: Introductionmentioning

confidence: 99%

Recent advances in acoustic technology for aquaculture: A review

Wang

et al. 2023

Reviews in Aquaculture

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Acoustic technology has great application prospects in aquaculture. In particular, two indispensable, critical technologies for the future aquaculture industry are multi‐sensor acquisition that can achieve multi‐scale information fusion, collection and establishment of a global acoustic fish database and highly developed deep learning intelligent algorithms that can establish a correlation mechanism between fish acoustic and behaviour characteristics. Acoustic technology offers remarkable advantages in large and turbid water bodies for studying spatial and temporal distribution patterns of aquatic organism populations, developing on‐demand feeding systems and estimating biomass. This article reviews the development of acoustic technology and its application in aquaculture over the last 30 years. It further analyses, in detail, the advantages and disadvantages of acoustic technology in evaluating aquatic organism biomass and morphological and physical indicators, aquatic organism behaviour and welfare improvement. Challenges of acoustic technology in acquiring dynamic target data accurately, building a global acoustic fish database and establishing connections between fish behaviour and acoustic characteristics are also discussed. In brief, this article aims to help researchers and practitioners better understand the current state‐of‐the‐art acoustic technologies, which can provide strong support for smart aquaculture applications.

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“…The novel sensor's sensitivity, repeatability, precision and environmental stability are higher than those used for ordinary spectrophotometry. Hassan et al (2019) introduced a novel sensor for measuring acoustic signals to detect fish stock. The technique will help control and prevent excessive waste of feed and promote fish growth in aquaculture.…”

Section: Introductionmentioning

confidence: 99%

Intelligent feeding technique based on predicting shrimp growth in recirculating aquaculture system

Chen

Sun

et al. 2022

Aquaculture Research

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Precise feeding in the recirculating aquaculture mode is a critical scientific problem that urgently needs a solution. This study aimed to develop an intelligent feeding technique in a recirculating aquaculture system for rearing Litopenaeus vannamei. The core of the intelligent feeding technique is the shrimp biomass prediction model. Accurate prediction of shrimp biomass could determine the appropriate feeding amount and ensure stable water quality. The data‐driven prediction model was developed based on water quality indicators and aquaculture management data collected during shrimp rearing. Multiple linear regression, artificial neural networks and a support vector machine (SVM) were introduced to develop the shrimp biomass predicting model. Results showed that the SVM model gave the lowest root mean square error (0.6500), mean absolute error (0.4368) and mean absolute percentage error (3.70%), as well as the highest accuracy (90.91%). By analysing the predictive ability of the machine learning models, it was determined that the SVM model was the optimal model for predicting biomass. The intelligent feeding machine can apply the optimal model to calculate the shrimp biomass and determine the appropriate feeding amount by reading the sensors in real time.

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Fish as a source of acoustic signal measurement in an aquaculture tank: Acoustic sensor based time frequency analysis

Cited by 4 publications

References 26 publications

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

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

Recent advances in acoustic technology for aquaculture: A review

Intelligent feeding technique based on predicting shrimp growth in recirculating aquaculture system

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