Developing a convolutional neural network to classify phytoplankton images collected with an Imaging FlowCytobot along the West Antarctic Peninsula

Schuyler C. Nardelli, Patrick C. Gray, Oscar Schofield

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

High-resolution optical imaging systems are quickly becoming universal tools to characterize and quantify microbial diversity in marine ecosystems. Automated detection systems such as convolutional neural networks (CNN) are often developed to identify the immense number of images collected. The goal of our study was to develop a CNN to classify phytoplankton images collected with an Imaging FlowCytobot for the Palmer Antarctica Long-Term Ecological Research project. A medium complexity CNN was developed using a subset of manually-identified images, resulting in an overall accuracy, recall, and f1-score of 93.8%, 93.7%, and 93.7%, respectively. The f1-score dropped to 46.5% when tested on a new random subset of 10, 269 images, likely due to highly imbalanced class distributions, high intraclass variance, and interclass morphological similarities of cells in naturally occurring phytoplankton assemblages. Our model was then used to predict taxonomic classifications of phytoplankton at Palmer Station, Antarctica over 2017-2018 and 2018-2019 summer field seasons. The CNN was generally able to capture important seasonal dynamics such as the shift from large centric diatoms to small pennate diatoms in both seasons, which is thought to be driven by increases in glacial meltwater from January to March. Moving forward, we hope to further increase the accuracy of our model to better characterize coastal phytoplankton communities threatened by rapidly changing environmental conditions.

Original languageEnglish (US)
Title of host publicationOCEANS 2021
Subtitle of host publicationSan Diego - Porto
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780692935590
DOIs
StatePublished - 2021
Externally publishedYes
EventOCEANS 2021: San Diego - Porto - San Diego, United States
Duration: Sep 20 2021Sep 23 2021

Publication series

NameOceans Conference Record (IEEE)
Volume2021-September
ISSN (Print)0197-7385

Conference

ConferenceOCEANS 2021: San Diego - Porto
Country/TerritoryUnited States
CitySan Diego
Period9/20/219/23/21

All Science Journal Classification (ASJC) codes

  • Oceanography

Keywords

  • Machine learning
  • Neural network
  • Phytoplankton
  • Polar science

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