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

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.