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Information fusion in aquaculture: a state-of the art review

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      Abstract

      Efficient fish feeding is currently one of biggest challenges in aquaculture to enhance the production of fish quality and quantity. In this review, an information fusion approach was used to integrate multi-sensor and computer vision techniques to make fish feeding more efficient and accurate. Information fusion is a well-known technology that has been used in different fields of artificial intelligence, robotics, image processing, computer vision, sensors and wireless sensor networks. Information fusion in aquaculture is a growing field of research that is used to enhance the performance of an industrialized ecosystem. This review study surveys different fish feeding systems using multi-sensor data fusion, computer vision technology, and different food intake models. In addition, different fish behavior monitoring techniques are discussed, and the parameters of water, pH, dissolved oxygen, turbidity, temperature etc., necessary for the fish feeding process, are examined. Moreover, the different waste management and fish disease diagnosis techniques using different technologies, expert systems and modeling are also reviewed.

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      Multi-sensor optimal information fusion Kalman filter

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        Information measure for performance of image fusion

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          Solids management and removal for intensive land-based aquaculture production systems

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            Author and article information

            Affiliations
            [1 ]. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
            [2 ]. China-EU Center for Information and Communication Technologies in Agriculture, China Agricultural University, Beijing 100083, China
            [3 ]. Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China
            [4 ]. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China
            [5 ]. Beijing Engineering Center for Advanced Sensors in Agriculture, Beijing 100083, China
            [6 ]. Department of Materials Science and Engineering, College of Engineering, Peking University, Beijing 100871, China
            Author notes
            dliangl@cau.edu.cn
            Contributors
            Journal
            Front. Agr. Sci. Eng.
            FASE
            CN10-1204/S
            Frontiers of Agricultural Science and Engineering
            Higher Education Press (4 Huixin Dongjie, Chaoyang District, Beijing 100029, China )
            2095-7505
            2095-977X
            2016
            : 3
            : 3
            : 206-221
            10.15302/J-FASE-2016111
            Categories
            REVIEW

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