Limnology and Oceanography Bulletin Volume 32 Number 3 August 2023 89‐123

Abstract

Digital imaging technologies are increasingly used to study life in the ocean. To deal with the large volume of image data collected over space and time, scientists employ various machine learning and deep learning algorithms to perform automated image classification. Training of classifiers requires a large number of expertly curated sets of images, a time-consuming process that requires taxonomic knowledge and understanding of the local ecosystem. The creation of these labeled training sets is the critical bottleneck for building skillful automated classifiers. Here, we discuss how we overcame this barrier by leveraging taxonomic knowledge from a group of specialists in a workshop setting and suggest best practices for effectively organizing image annotation efforts. In our experience, this 2 day workshop proved very insightful and facilitated classification of over 4 years of plankton images obtained at Scripps Pier (La Jolla, CA), focusing on diatoms and dinoflagellates. We highlight the importance of facilitating a dialog between taxonomists and engineers to better integrate ecological goals with computational constraints, and encourage continuous involvement of taxonomic experts for successful implementation of automated classifiers.