Evolution of Intelligent Feeding System for Aquaculture: A Review

Wang, Gaoxiang; Hu, Lingxi; Long, Wei; Jiang, Linhua

doi:10.1109/aiam54119.2021.00085

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…By 2025, China's aquaculture mechanization level needs to reach more than 50%. It is essential to enhance investment in the facilities and equipment for critical areas such as breeding and seedling cultivation, epidemic prevention and treatment, planting and harvesting, and tail water treatment [44]. For example, the research and development of new equipment, such as the precise management and control of pond culture, efficient catching, light and simplified seedling planting and harvesting in raft culture, the counting and sorting of seedlings, and the harmless treatment of sick and dead aquatic products, should be focused on.…”

Section: Innovative Research and Development Of Aquaculture Machinery...mentioning

confidence: 99%

The Influencing Factors Analysis of Aquaculture Mechanization Development in Liaoning, China

Yu,

Wang,

Ren

et al. 2024

AgriEngineering

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Promoting the mechanization of aquaculture is one of the most important supporting measures to ensure the high-quality development of the aquaculture industry in China. In order to solve the problems of predominantly manual work and to decrease the costs of aquaculture, the influencing factors of China’s aquaculture mechanization were systematically analyzed. The triple bottom theory was selected, and three aspects were identified, including environmental, economic, and social aspects. Through the literature review, the Delphi method, and the analytic hierarchy process, the comprehensive evaluation indicator system, including 18 influencing factors, was proposed. Moreover, the fuzzy comprehensive evaluation method was combined with the model to solve the evaluation results. A case study in Liaoning Province was offered and, according to the analysis results, the economic aspect at the first level was the most critical factor; the financial subsidy for the purchase of aquaculture machinery, the energy consumption of the machinery and equipment, and the promotion and use of aquaculture technology were the most important factors and had the greatest impact on the development of aquaculture mechanization in China. The effective implementation paths and countermeasures were proposed, such as the promotion of mechanized equipment and the enhancement of the machinery purchase subsidies, in order to provide an important decision-making basis for the improvement of the level of aquaculture mechanization.

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Section: Innovative Research and Development Of Aquaculture Machinery...mentioning

confidence: 99%

The Influencing Factors Analysis of Aquaculture Mechanization Development in Liaoning, China

Yu,

Wang,

Ren

et al. 2024

AgriEngineering

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“…The recirculating aquaculture system is a breeding model in which the water resources are purified and treated by a circulating water system and then recycled 6 . The key technology of the recirculating aquaculture system is the purification treatment of aquaculture water and the rapid removal of harmful substances 7 …”

Section: Introductionmentioning

confidence: 99%

“…6 The key technology of the recirculating aquaculture system is the purification treatment of aquaculture water and the rapid removal of harmful substances. 7 The advantages of RAS are summarized as follows. First, it uses less water with low water quality, and water resources can be recycled.…”

Section: Introductionmentioning

confidence: 99%

(Retracted) Dynamic scene images-assisted intelligent control method for industrialized feeding through deep vision learning

et al. 2022

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In a recirculating aquaculture system (RAS), feeding is an important factor affecting the growth of breeding objects. The traditional feeding methods relied on manual experience, which resulted in high labor costs and bait waste. To deal with these challenges, this paper proposes a dynamic scene images-assisted intelligent control method for industrial feeding through deep vision learning. First, a feeding video is processed according to the interframe difference method to obtain the image of the feeding state of the fish. Then, a modified VGG16 model is developed to determine the feeding state of the fish, transform it into a binary classification problem, and calculate the feeding frequency of the fish. After that, residual bait detection is deployed by adapting the YOLOv5 model. The results of the feeding frequency and the residual bait detection are then used to develop an intelligent feeding strategy to improve the growth rate of the fish and the conversion rate of the bait. Experimental tests on real-world scene images showed that the accuracy of identifying the feeding state by the modified VGG16 model reaches 92.4%. Through the verification of the medium-size RAS, compared with the traditional feeding method, the intelligent feeding method significantly saves manpower and reduce 15% of bait waste.

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Pisces Bot

A J,

B R

et al. 2024

2024 International Conference on E-Mobility, Power Control and Smart Systems (ICEMPS)

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Evolution of Intelligent Feeding System for Aquaculture: A Review

Cited by 3 publications

References 4 publications

The Influencing Factors Analysis of Aquaculture Mechanization Development in Liaoning, China

The Influencing Factors Analysis of Aquaculture Mechanization Development in Liaoning, China

(Retracted) Dynamic scene images-assisted intelligent control method for industrialized feeding through deep vision learning

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