DEVELOPING AND ASSESSING A DIVERSE PLANKTON IMAGERY TRAINING SET FOR MACHINE-LEARNING PLANKTON CLASSIFICATION IN THE NORTH PACIFIC SUBTROPICAL REGION

Abstract

ABSTRACT The Imaging FlowCytobot (IFCB) has a continually growing role in oceanographic research, particularly in the exploration of microbial life within the North Pacific Subtropical Gyre (NPSG). However, the vast amount of data generated by the IFCB poses a challenge for manual sorting and taxonomic classification. This study addresses this challenge by developing a Convolutional Neural Network (CNN) training set to efficiently categorize IFCB images into taxonomic groups. Specifically focusing on the diatom Hemiaulus and ciliate phylum Ciliphora during a research cruise within the NPSG in the summer of 2021, the study aims to quantify the CNN's performance compared to manual annotations of IFCB images taken on this cruise, providing insights into the CNN’s accuracy and precision over time. Statistical analyses of the CNN’s machine learning-based classifications indicate a high accuracy in the automated identification of Hemiaulus and Ciliophora. Analysis of biovolume and particle number concentration reveals trends in taxonomic abundance over the course of the cruise. Despite morphological changes of Hemiaulus as it loses structure over time, the CNN demonstrates an overall improvement in accuracy as the cruise progresses, particularly for Hemiaulus. This study highlights the development of a robust training set of roughly 76,000 images, allowing the CNN to accurately classify images collected within the NPSG.

Keywords: Taxonomic sorting, machine learning, zooplankton, Imaging FlowCytoBot (IFCB), North Pacific Subtropical Gyre (NPSG), ocean microbiology.