A deep vision sensing‐based fuzzy control scheme for smart feeding in the industrial recirculating aquaculture systems. Issue 2 (18th January 2023)

• Record Type: Journal Article
• Authors: Zhou, Yueming; Zhang, Qin; Zhang, Huiyan; Yang, Junchao; Guo, Zhiwei; Bulugu, Isack; Shen, Yu
• Is Part Of: Electronics letters. Volume 59:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-18
• Subjects: fuzzy control -- fuzzy neural nets -- intelligent control; Electronics -- Periodicals; 621.381
• DOI: 10.1049/ell2.12727
• Languages: English
• ISSNs: 0013-5194

Abstract

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. Abstract : 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.