RAPID: real-time automated plankton identification dashboard using Edge AI at sea

Pitois, Sophie G.; Blackwell, Robert E.; Close, Hayden; Eftekhari, Noushin; Giering, Sarah L. C. ORCID: https://orcid.org/0000-0002-3090-1876; Masoudi, Mojtaba; Payne, Eric; Ribeiro, Joseph; Scott, James. 2025 RAPID: real-time automated plankton identification dashboard using Edge AI at sea. Frontiers in Marine Science, 11. 10.3389/fmars.2024.1513463

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Text © 2025 Pitois, Blackwell, Close, Eftekhari, Giering, Masoudi, Payne, Ribeiro and Scott. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms fmars-2-1513463.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (8MB) | Preview

Abstract/Summary

We describe RAPID: a Real-time Automated Plankton Identification Dashboard, deployed on the Plankton Imager, a high-speed line-scan camera that is connected to a ship water supply and captures images of particles in a flow-through system. This end-to-end pipeline for zooplankton data uses Edge AI equipped with a classification (ResNet) model that separates the images into three broad classes: Copepods, Non-Copepods zooplankton and Detritus. The results are transmitted and visualised on a terrestrial system in near real time. Over a 7-days survey, the Plankton Imager successfully imaged and saved 128 million particles of the mesozooplankton size range, 17 million of which were successfully processed in real-time via Edge AI. Data loss occurred along the real-time pipeline, mostly due to the processing limitation of the Edge AI system. Nevertheless, we found similar variability in the counts of the three classes in the output of the dashboard (after data loss) with that of the post-survey processing of the entire dataset. This concept offers a rapid and cost-effective method for the monitoring of trends and events at fine temporal and spatial scales, thus making the most of the continuous data collection in real time and allowing for adaptive sampling to be deployed. Given the rapid pace of improvement in AI tools, it is anticipated that it will soon be possible to deploy expanded classifiers on more performant computer processors. The use of imaging and AI tools is still in its infancy, with industrial and scientific applications of the concept presented therein being open-ended. Early results suggest that technological advances in this field have the potential to revolutionise how we monitor our seas.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.3389/fmars.2024.1513463
ISSN:	2296-7745
Additional Keywords:	plankton imager, real time, plankton ecology, Edge AI, Pi-10, plankton classification, machine learning, adaptive sampling
Date made live:	05 Feb 2025 12:59 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538860

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