IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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An Implementation of Feeding Time Detection System for Smart Fish Farm Using Deep Neural Network

심층신경망을 이용한 스마트 양식장용 사료 공급 시점 감지 시스템 구현

  • Received : 2021.06.21
  • Accepted : 2023.01.05
  • Published : 2023.02.28
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Abstract

In traditional fish farming way, the workers have to observe all of the pools every time and every day to feed at the right timing. This method causes tremendous stress on workers and wastes time. To solve this problem, we implemented an automatic detection system for feeding time using deep neural network. The detection system consists of two steps: classification of the presence or absence of feed and checking DO (Dissolved Oxygen) of the pool. For the classification, the pretrained ResNet18 model and transfer learning with custom dataset are used. DO is obtained from the DO sensor in the pool through HTTP in real time. For better accuracy, the next step, checking DO proceeds when the result of the classification is absence of feed several times in a row. DO is checked if it is higher than a DO reference value that is set by the workers. These actions are performed automatically in the UI programs developed with LabVIEW.

Keywords

Acknowledgement

이 논문은 부경대학교 자율창의학술연구비 (2021년)에 의하여 연구되었음.

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