The Prototype of a Smart Underwater Surveillance System for Shrimp Farming

Huang, Ing-Jer; Hung, Chin‐Chang; Kuang, Shiann-Rong; Chang, Yun-Nan; Huang, Kuo-Feng; Tsai, Chang-Ru; Feng, Kai-Lin

doi:10.1109/amcon.2018.8614976

Cited by 18 publications

(9 citation statements)

References 10 publications

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“…Those authors also measured organic matter residues in pond sediments to estimate feeding behavior at night time. The remaining pellets can be used as an indicator of the feeding intensity, thereby saving the amount of feed and effectively reducing pollution in culture ponds, but the accuracy of the results cannot be quantified [ 115 ]. Although indirect information can be used to monitor behavior, compared with the direct monitoring method, it is less accurate and prone to errors.…”

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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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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“…For example, Huang et al . (2018) recently presented a prototype of a real‐time underwater surveillance system for shrimp ponds and tanks. This apparatus included an underwater camera, an image enhancement algorithm for image haze removal and the use of YOLO to detect shrimp present inside the camera field.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the use of YOLO to detect shrimp, Huang et al . (2018) presented a program based on neural networks to separate feed from non‐feed pixels, thereby automatically assessing the surface area of the feeding tray covered by feed through time. Similarly, Chirdchoo and Cheunta (2019) successfully developed and trialled a cheap software to automatically detect feed pellets remaining on shrimp feeding trays through the use of a segmentation program which separates pixels based on the colour of feed samples.…”

Section: Introductionmentioning

confidence: 99%

“…2016; Huang et al . 2018). Tracking using telemetry techniques is often not appropriate due to the regular moulting of crustaceans during which external tags can be lost (Freire & González‐Gurriarán 1998; Haddaway et al .…”

Section: Introductionmentioning

confidence: 99%

“…2017; Huang et al . 2018; Chirdchoo & Cheunta 2019), which in return could lead to a decrease in feeding costs. In addition to the use of YOLO to detect shrimp, Huang et al .…”

Section: Introductionmentioning

confidence: 99%

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Monitoring methods of feeding behaviour to answer key questions in penaeid shrimp feeding

Tailly

Keitel

Owen

et al. 2021

Reviews in Aquaculture

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The penaeid shrimp farming industry is a fast-growing sector which continues to suffer from significant feeding inefficiencies. Shrimp are slow to feed on pellets, with consumption dependent on a wide range of environmental and physiological parameters. Feed management on farms remains mainly based on feeding trays which can be difficult to observe and often result in overfeeding. While our understanding of shrimp feeding behaviour is beginning to improve under laboratory conditions, much less is known about shrimp behaviour in production ponds. Consequently, there is a growing interest within the industry to improve observations of shrimp feeding behaviour in situ, although this can be difficult due to high water turbidity and the benthic nature of shrimp. This review identifies key questions that remain unanswered in relation to shrimp feeding behaviour under commercial aquaculture conditions, and considers how they could be addressed using state-of-the-art applications based on three technologies commonly used in other areas of aquaculture. The use of passive acoustics, computer vision and telemetry are highlighted, alongside their potential to help farmers achieve better feeding efficiencies and sustainability as well as to help understand shrimp feeding behaviour in relation to various biotic and abiotic parameters.

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