Project Lead: Ali Chase, Washington Research Foundation Postdoctoral Fellow, Applied Physics Laboratory 

eScience Liaison: Valentina Staneva

Thanks to recent advances in instrumentation, we can now observe phytoplankton – the single-celled autotrophs that form the base of the marine food web – using automated, high-throughput microscopy. Millions of phytoplankton images have been collected from oceans and seas across the globe, using an instrument called the Imaging FlowCytobot (IFCB), which is deployed onboard oceanographic research vessels and captures thousands of individual particle images every hour. Use of novel plankton imagery data to address a wide range of oceanographic and marine ecosystem questions is currently limited by the time required to analyze and categorize images. Processing these images for use in oceanographic research is time consuming, and the quantity of data necessitates the use of automated processes to classify images. Thus, the need for open-source, efficient, and effective classification tools is high. The primary objective of the UTOPIA incubator project is to develop machine learning methods for IFCB image data classification, and to produce an open-source, user-friendly tool that allows for broad application within the oceanographic research community.