• Title/Summary/Keyword: Intelligent Fish Feed Model

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Fish Activity State based an Intelligent Automatic Fish Feeding Model Using Fuzzy Inference (퍼지추론을 이용한 어류 활동상태 기반의 지능형 자동급이 모델)

  • Choi, Han Suk;Choi, Jeong Hyeon;Kim, Yeong-ju;Shin, Younghak
    • The Journal of the Korea Contents Association
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    • v.20 no.10
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    • pp.167-176
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    • 2020
  • The automated fish feed system currently used in Korea supplies a certain amounts of feed to water tanks at a certain time. This automated system can reduce the labor cost of managing aqua farms, but it is very difficult to control intelligently and appropriately the amount of expensive feed that is critical to aqua farm productivity. In this paper, we propose the FIIFF Inference Model( Fuzzy Inference-based Intelligent Fish Feeding Model) that can solves the problems of these existing automatic fish feeding devices and maximizes the efficiency of feed supply while properly maintaining the growth rate of fish in aqua farms. The proposed FIIFF inference model has the advantage of being able to control feed amounts appropriately since it computes the amount of feed using the current water environments and fish activity state of the aqua farms. The result of the feed amount yield experiment with the proposed FIIFF Inference Model represents the effect of saving 14.8% over the eight months of actual feed amount in the aqua farm.