AI in marine sciences: An openaccess integrated environment for automated classifi cation of phytoplankton images

Aquatic ecosystems are vital in regulating climate and providing resources, but face threats from global
change and local stressors. Understanding their dynamics is crucial for sustainable use and conservation.
Coordinated by the EGI Foundation, which leverages a Federation of over 200 Data Centres all over
Europe and delivered over 82M Cloud CPU/hours to more than 95,000 users, the iMagine project offers
aquatic science researchers (marine and freshwaters) the iMagine AI Platform, a suite of AI-powered
image analysis tools for researchers in aquatic sciences, facilitating a better understanding of scientific
phenomena and applying AI and ML for processing image data. The platform supports the entire
machine learning cycle, from model development to deployment, leveraging data from underwater
platforms, webcams, microscopes, drones, and satellites and utilising distributed resources across
Europe. A serverless architecture and DevOps approach enable easy sharing and deployment of AI
models. Four providers within the pan-European EGI federation power the platform, offering substantial
computational resources for image processing.
Eight use cases in iMagine focus on image analytics services, which will be available to external
researchers through Virtual Access. FlowCam, powered by VLIZ, is one of them.
Phytoplankton, the single-cell algae at the basis of marine food webs and an essential indicator of
ecosystem health, is continuously being monitored at several stations in the Belgian Part of the North
Sea under the LifeWatch research infrastructure. To process monitoring samples in a fast and
automated manner, we make use of automated imaging techniques like FlowCam. By aligning particles
in the sample in a continuous fluid stream and capturing each particle in a picture as it passes a camera,
this device can produce an image library of a sample in under 30 minutes. While FlowCam has
significantly sped up time spent in the lab, it delivers about 350,000 images yearly and calls for an
automated approach to handle high data loads. To speed up the taxonomist’s job of manually labelling
all these images, we built semi-automated data pipelines and implemented machine-learning
algorithms, specifically Convolutional Neural Networks, to classify the images. Over the years, this
combination of automated imaging and machine learning has helped us build a set of over 2,2 million
annotated FlowCam images and trained classifiers fine-tuned by taxonomists correcting wrong model
predictions. This dataset and the trained classifiers have proven to greatly benefit our marine
monitoring, and we wanted to share this asset with other researchers.

Under iMagine, we aim to publish the open access image set and classifiers and build a user-friendly
module where users can both predict FlowCam images using pretrained models and train classifiers on
their own image input. The iMagine platform hosting this module offers an integrated environment with
all source code and a graphical user interface for users with less coding experience. Computing
resources for the services are also available to the user through the platform. The FlowCam module
further provides tools for post-hoc analysis of model performance and code for image transformation
and augmentation to deal with different image resolutions and class imbalances in training sets. More
information on the project and the FlowCam service can be found at https://www.imagine-ai.eu. In the
next coming years, we hope to facilitate many marine researchers in the application of automated
classification of phytoplankton imaging data. We actively encourage researchers and monitoring
programs to make use of the FlowCam service and the iMagine platform to contribute to more efficient
biomonitoring. The outcomes of the FlowCam use case could boost the awareness of the Belgian open
science community about the healthiness of the national marine ecosystem through the project
findings, which could in turn be beneficial to researchers in marine science all over Europe.