Journal of the Korean Society of Visualization (한국가시화정보학회지)

The Korean Society of Visualization (한국가시화정보학회)
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Development of microfluidic green algae cell counter based on deep learning

딥러닝 기반 녹조 세포 계수 미세 유체 기기 개발

  • Cho, Seongsu (School of Mechanical Engineering, Sungkyunkwan University) ;
  • Shin, Seonghun (School of Mechanical Engineering, Sungkyunkwan University) ;
  • Sim, Jaemin (School of Mechanical Engineering, Sungkyunkwan University) ;
  • Lee, Jinkee (School of Mechanical Engineering, Sungkyunkwan University)
  • Received : 2021.08.03
  • Accepted : 2021.08.22
  • Published : 2021.08.31
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Abstract

River and stream are the important water supply source in our lives. Eutrophication causes excessive green algae growth including microcystis, which makes harmful to ecosystem and human health. Therefore, the water purification process to remove green algae is essential. In Korea, green algae alarm system exists depending on the concentration of green algae cells in river or stream. To maintain the growth amount under control, green algae monitoring system is being used. However, the unmanned, small and automatic monitoring system would be preferable. In this study, we developed the 3D printed device to measure the concentration of green algae cell using microfluidic droplet generator and deep learning. Deep learning network was trained by using transfer learning through pre-trained deep learning network. This newly developed microfluidic cell counter has sufficient accuracy to be possibly applicable to green algae alarm system.

Keywords

Acknowledgement

본 결과물은 환경부의 재원으로 한국환경산업기술원의 생태모방 기반 환경오염관리 기술개발사업의 지원을 받아 연구되었습니다.(2019002790003)

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