A deep vision sensing‐based fuzzy control scheme for smart feeding in the industrial recirculating aquaculture systems

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 issu...

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Bibliographic Details
Published in:Electronics letters 2023-01, Vol.59 (2), p.n/a
Main Authors: Zhou, Yueming, Zhang, Qin, Zhang, Huiyan, Yang, Junchao, Guo, Zhiwei, Bulugu, Isack, Shen, Yu
Format: Article
Language:English
Subjects:
Algorithms
Aquaculture
Control systems
Decision making
Fuzzy control
Fuzzy logic
fuzzy neural nets
Fuzzy systems
intelligent control
Neural networks
Object recognition
Vision
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Summary: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. This paper propose a deep vision sensing‐based fuzzy control scheme for smart feeding in IRAS. Firstly, a deep learning‐based object detection model is introduced to capture two aspects features as the decision factors. Then, a fuzzy neural network model is formulated to calculate control decision strategies via fuzzy inference.
ISSN:0013-5194
1350-911X
DOI:10.1049/ell2.12727