Abstract
In industrial recirculating aquaculture systems (IRAS), the autonomous decision control of feeding strategies remains a practical concern. Conventionally, control schemes were established from data-driven view, which fails to comprehensively perceive activity status of fishes. To deal with this issue, a deep vision sensing-based fuzzy control scheme is proposed for smart feeding in IRAS. In the first stage, a deep learning-based object detection model is introduced to capture two aspects features as the decision factors: residual bait and eating frequency. In the second stage, a fuzzy neural network model is formulated to calculate control decision strategies via fuzzy inference. And experiments on real-world visual scenes are conducted to verify the proposal.
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