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Plankton classification with high-throughput submersible holographic microscopy and transfer learning

BMC Ecology and Evolution 2021 29 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 40 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Liam MacNeil, Liam MacNeil, Sergey Missan, Sergey Missan, Junliang Luo, Thomas Trappenberg, Julie LaRoche Julie LaRoche

Summary

Researchers used underwater holographic microscopes and transfer learning — an AI technique that applies knowledge from one task to another — to automatically classify diverse plankton species from images, including rare forms. The system shows promise for large-scale, automated ocean monitoring without needing constant human analysis.

These results show compelling baselines for classifying holographic plankton images, both rare and plentiful, including several dinoflagellate and diatom groups. These results also support a broader potential for deployable holographic microscopes to sample diverse microbial eukaryotic communities, and its use for high-throughput plankton monitoring.

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