Factory recirculating aquaculture system(RAS)is facing in a stage of continuous research and technological in-novation.Intelligent aquaculture is an important direction for the future development of aquaculture.Howeve...Factory recirculating aquaculture system(RAS)is facing in a stage of continuous research and technological in-novation.Intelligent aquaculture is an important direction for the future development of aquaculture.However,the RAS nowdays still has poor self-learning and optimal decision-making capabilities,which leads to high aqua-culture cost and low running efficiency.In this paper,a precise aeration strategy based on deep learning is de-signed for improving the healthy growth of breeding objects.Firstly,the situation perception driven by computer vision is used to detect the hypoxia behavior.Then combined with the biological energy model,it is constructed to calculate the breeding objects oxygen consumption.Finally,the optimal adaptive aeration strategy is generated according to hypoxia behavior judgement and biological energy model.Experimental results show that the energy consumption of proposed precise aeration strategy decreased by 26.3%compared with the man-ual control and 12.8%compared with the threshold control.Meanwhile,stable water quality conditions acceler-ated breeding objects growth,and the breeding cycle with the average weight of 400 g was shortened from 5 to 6 months to 3–4 months.展开更多
基金supported in part by the Chongqing Municipal Education Commission projects under grant KJCX20-20035,KJQN202200829 and KJQN202300844Chongqing Science and Technology Commission projects under grant CSTB2022BSXM-JCX0117supported in part by Chongqing Technology and Business University projects under GRANT No.(2156004,212017,yjscxx2023-211-69).
文摘Factory recirculating aquaculture system(RAS)is facing in a stage of continuous research and technological in-novation.Intelligent aquaculture is an important direction for the future development of aquaculture.However,the RAS nowdays still has poor self-learning and optimal decision-making capabilities,which leads to high aqua-culture cost and low running efficiency.In this paper,a precise aeration strategy based on deep learning is de-signed for improving the healthy growth of breeding objects.Firstly,the situation perception driven by computer vision is used to detect the hypoxia behavior.Then combined with the biological energy model,it is constructed to calculate the breeding objects oxygen consumption.Finally,the optimal adaptive aeration strategy is generated according to hypoxia behavior judgement and biological energy model.Experimental results show that the energy consumption of proposed precise aeration strategy decreased by 26.3%compared with the man-ual control and 12.8%compared with the threshold control.Meanwhile,stable water quality conditions acceler-ated breeding objects growth,and the breeding cycle with the average weight of 400 g was shortened from 5 to 6 months to 3–4 months.
