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IJAT Vol.19 No.4 pp. 642-650
doi: 10.20965/ijat.2025.p0642
(2025)

Research Paper:

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Automated Detection of Harmful Red Tide Phytoplankton Using Deep Learning-Based Object Detection Models

Tomoka Kawano*1,†, Masahiro Migita*1 ORCID Icon, Kaito Kamimura*2 ORCID Icon, Atsushi Urabe*3 ORCID Icon, Haruo Yamaguchi*4 ORCID Icon, Setsuko Sakamoto*5, Yuji Tomaru*5 ORCID Icon, and Masashi Toda*1 ORCID Icon

*1Kumamoto University
2-40-1 Kurokami Chuo-ku, Kumamoto, , Japan

†Corresponding author

*2Kochi Prefectural Fisheries Experimental Station
Susaki, Japan

*3Fisheries Management Division, Fisheries Promotion Department, Kochi Prefectural Government
Kochi, Japan

*4Faculty of Agriculture and Marine Sciences, Kochi University
Nankoku, Japan

*5Fisheries Technology Institute, Japan Fisheries Research and Education Agency
Hatsukaichi, Japan

Received:
December 1, 2024
Accepted:
February 12, 2025
Published:
July 5, 2025
Creative Commons License
Keywords:
red tide detection, deep learning, object detection
Abstract

Red tides are phenomena caused by the abnormal proliferation of marine phytoplankton, leading to mass fish mortality and severe economic damage to fisheries. Currently, the detection and quantification of harmful phytoplankton rely primarily on manual inspection using optical microscopes. This process is time-consuming, labor-intensive, and requires specialized expertise in species identification. In this study, we propose an automated detection system using deep learning-based object detection methods to classify various marine phytoplankton species from microscopic images and identify harmful red tide-related species. Our approach aims to enhance early detection capabilities, reduce the burden on researchers, and improve the accuracy of harmful phytoplankton monitoring.

Cite this article as:
T. Kawano, M. Migita, K. Kamimura, A. Urabe, H. Yamaguchi, S. Sakamoto, Y. Tomaru, and M. Toda, “Automated Detection of Harmful Red Tide Phytoplankton Using Deep Learning-Based Object Detection Models,” Int. J. Automation Technol., Vol.19 No.4, pp. 642-650, 2025.
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Last updated on Jul. 28, 2025