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Automated image classification workflow for phytoplankton monitoring
Decrop, W.; Lagaisse, R.; Mortelmans, J.; Muñiz, C.; Heredia, I.; Calatrava, A.; Deneudt, K. (2025). Automated image classification workflow for phytoplankton monitoring. Front. Mar. Sci. 12: 1699781. https://dx.doi.org/10.3389/fmars.2025.1699781
In: Frontiers in Marine Science. Frontiers Media: Lausanne. ISSN 2296-7745; e-ISSN 2296-7745
Related to:
Decrop, W.; Lagaisse, R.; Mortelmans, J.; Muñiz, C.; Deneudt, K. (2025). Design and implementation of an automated Image classification workflow for phytoplankton monitoring, in: Praet, N. et al. Particles in Europe 2025: Conference program and proceedings, 17 - 19 September 2025, Ostend, Belgium. pp. 62-70, more
Peer reviewed article  
Available in  Authors 
Author keywords
    convolutional neural network; phytoplankton; image classification; FlowCAM; bpns; biodiversity monitoring; marine; features
Project Top | Authors 
  • Flemish contribution to LifeWatch.eu
Authors  Top 
  • Decrop, W.
  • Lagaisse, R.
  • Mortelmans, J.
  • Muñiz, C.
  • Heredia, I.
  • Calatrava, A.
  • Deneudt, K.
Abstract
    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.
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