Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Mar. Sci.

Sec. Aquatic Microbiology

Automated Image Classification Workflow for Phytoplankton Monitoring

Provisionally accepted
Wout DecropWout Decrop1*Rune LagaisseRune Lagaisse2Jonas MortelmansJonas Mortelmans1Carlota MuñizCarlota Muñiz1Ignacio HerediaIgnacio Heredia3Amanda arrayo CalatravaAmanda arrayo Calatrava4Klaas DeneudtKlaas Deneudt1
  • 1Flanders Marine Institute, Ostend, Belgium
  • 2Universiteit Gent, Ghent, Belgium
  • 3Instituto de Fisica de Cantabria, Santander, Spain
  • 4Universitat Politecnica de Valencia, Valencia, Spain

The final, formatted version of the article will be published soon.

Select one of your emails

You have multiple emails registered with Frontiers:

Notify me on publication

Please enter your email address:

If you already have an account, please login

You don't have a Frontiers account ? You can register here

Phytoplankton are fundamental components of marine ecosystems and play a critical role in global biogeochemical cycles. Efficient monitoring of marine phytoplankton is crucial for assessing ecosystem health, forecasting harmful algal blooms and sustainable marine management. The integration of high-throughput imaging sensors like FlowCam technology with artificial intelligence (AI) for image recognition has revolutionized phytoplankton monitoring, enabling rapid and accurate class identification. This study introduces an automated image classification workflow designed to improve speed, accuracy and scalability of phytoplankton identification. By leveraging convolutional neural networks (CNNs), the system enhances performance while reducing reliance on traditional, labor-manual identification methods.

Keywords: Convolutional Neural Network, Phytoplankton, image classification, FlowCAM, BPNS, Biodiversity monitoring

Received: 05 Sep 2025; Accepted: 24 Nov 2025.

Copyright: © 2025 Decrop, Lagaisse, Mortelmans, Muñiz, Heredia, Calatrava and Deneudt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Wout Decrop

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material