A study on detection and classification methods for red tide phytoplankton using deep learning
Red tides are phenomena caused by the abnormal proliferation of marine plankton, leading to massive fish deaths and significant damage to the fishing industry. Currently, detection and quantification of plankton responsible for red tides are performed primarily through manual inspection using optical microscopy, which requires considerable time, effort, and expertise in species identification. This study explored the use of object detection methods to classify various marine plankton species from microscopy images and attempted to automate the detection of red tide phytoplankton.
Authors
- Setsuko Sakamoto
- Haruo Yamaguchi (ORCID: https://orcid.org/0000-0002-6620-3033)
- K. Kamimura (ORCID: https://orcid.org/0000-0003-4807-5020)
- Atsushi Urabe (ORCID: https://orcid.org/0000-0002-0885-2879)
- Yuji Tomaru (ORCID: https://orcid.org/0000-0002-8164-6991)
- Tomoka Kawano
- Masashi Toda
- Masahiro Migita
Institutions
- Yamanashi Prefectural Fisheries Technology Center (JP)
- Kochi Prefectural Education Center (JP)
- Tochigi Prefectural Fisheries Experimental Station (JP)
- Japan Fisheries Research and Education Agency (JP)
- Kōchi University (JP)
- Kumamoto University (JP)
Publication Details
- Published
- 2025-02-05
- DOI
- https://doi.org/10.1117/12.3058042
- Citations
- 1
- Primary Topic
- Water Quality Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.00