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Leveraging FlowCam for AI-Driven Plankton Classification

Explore how high-throughput imaging with FlowCam supports scalable, AI-powered plankton identification.

As artificial intelligence (AI) tools become more accessible, aquatic researchers are turning to machine learning to automate the identification and classification of plankton. But successful AI models require more than just algorithms—they rely on well-structured, high-quality training data.

This white paper, Leveraging FlowCam for AI-Driven Plankton Classification, provides a practical guide to building strong data foundations for AI workflows in aquatic science. Drawing on real-world case studies and lessons from the field, it outlines how FlowCam-generated image libraries are being used to train robust classification models for a range of applications—from harmful algal bloom (HAB) detection to lab culture monitoring and marine community analysis.

What’s Inside

  • The imaging principles that make FlowCam data well-suited for AI model development

  • Key differences between FlowCam’s internal classifiers and external AI tools

  • Practical guidance for structuring, annotating, and validating training datasets

  • Why expert taxonomic input remains essential in AI workflows

  • Case studies highlighting successful AI applications using FlowCam images

Why FlowCam?

FlowCam captures thousands of images per minute, producing high-resolution visual data across diverse particle sizes and sample types. Combined with VisualSpreadsheet® software, these images can be efficiently sorted, exported, and integrated into external machine learning pipelines. This makes FlowCam an effective platform for researchers building AI tools that need to reflect the true complexity of plankton communities.

Get the white paper and start building your AI workflow on a solid data foundation.

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