Flowcam phytoplankton identification

Setting up operational environment at iMagine platform, with the AI pipeline for processing Flowcam images, and storing resulting data

Connecting the MongoDB database to the iMagine platform for ingesting and extracting data

Refining the AI tools for taxonomic identification of phytoplankton. For instance, the current image classification model does not include sampling metadata and environmental data as context input to the CNN

Enhancing the FAIRness of data output in accordance with Darwin Core standards and relevant vocabularies

Developing guidance and training material for uptake of the Flowcam processing service

Reaching out to users for uptake and provide support and training

A long term (>4y) high quality phytoplankton image dataset is available in a NoSQL

The global description of the abundance and diversity of phytoplankton communities yields an indication of the health of marine ecosystems and their response to anthropic stressors. As such, the image derived phytoplankton community characteristics are used within three common OSPAR indicators for the Good Environmental Status assessment for pelagic habitats under Descriptor 1 (Biodiversity).

The provision of the Flowcam processing pipeline in iMagine will result in more users and more image providers, contributing to more phytoplankton information and more efficient biomonitoring.