RAPID: Real-time Automated Plankton Identification Dashboard using Edge AI at sea

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 costeffective 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.

Keywords: Plankton Imager, real time, Plankton ecology, Edge AI, Pi-10, Plankton classification, machine learning, Adaptive sampling

Received: 18 Oct 2024; Accepted: 18 Dec 2024.

Copyright: © 2024 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) or licensor 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.

* Correspondence:
Sophie G. Pitois, Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Lowestoft, United Kingdom
Noushin Eftekhari, The Alan Turing Institute, London, NW1 2DB, England, United Kingdom
Mojtaba Masoudi, National Oceanography Centre, University of Southampton, Southampton, SO14 3ZH, England, United Kingdom
Eric Payne, Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Lowestoft, United Kingdom
Joseph Ribeiro, Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Lowestoft, United Kingdom
James Scott, Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Lowestoft, United Kingdom