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The occurrence of red tides causes mass mortality of farmed fish and leads to significant damage to the fishing industry, making early detection crucial. Currently, the detection and counting of phytoplankton, which are responsible for red tides, are mainly conducted through visual inspection using microscopes. However, this process requires time, effort, and knowledge of species identification. Therefore, in this study, we attempted to detect and count red tide plankton from microscope images of phytoplankton using the object detection method DINO.

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Detection of Red Tide Plankton in Automatic Microscope System Imaging Using Deep Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Tomoka Kawano,
  • Masahiro Migita,
  • Tadashi Nakano,
  • Akira Yamaguchi,
  • Koki Yuasa,
  • Masashi Toda

摘要

The occurrence of red tides causes mass mortality of farmed fish and leads to significant damage to the fishing industry, making early detection crucial. Currently, the detection and counting of phytoplankton, which are responsible for red tides, are mainly conducted through visual inspection using microscopes. However, this process requires time, effort, and knowledge of species identification. Therefore, in this study, we attempted to detect and count red tide plankton from microscope images of phytoplankton using the object detection method DINO.