Practical guidance on automated sorting of underwater images in plankton ecology research

Abstract

In situ plankton imaging complements classical sampling approaches by obtaining observations of plankton composition and traits at finer spatial and temporal resolutions. These imaging techniques can provide valuable ecological insight and are also notorious for generating a massive volume of images that require classification to generate quantitative data. Automating image segmentation and classification can accelerate data extraction; however, the high diversity and uneven distribution of plankton taxa, variation in image characteristics obtained from different imagers, and limited availability of human classification expertise present challenges to development of user-friendly and universally accepted image processing and classification tools. Differences in desired taxonomic or trait resolution, classifier performance, and spatial variability in community composition often necessitate the development of tailored automated classifiers for specific cases. This customization typically requires expertise in computer vision and machine learning that many ecologists do not acquire through traditional training. In this paper, we review the plankton imaging and classification workflow and present two case studies for a plankton ecology audience. We emphasize Convolutional Neural Network (CNN) classifiers and demonstrate strategies to address common challenges in image classification using semiautomated classification and unsupervised learning approaches. The overarching aim is to provide practical guidance for ecologists and encourage broader adoption of in situ plankton imaging in ecological research.