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 eq...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1513463/full |
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| author | Sophie G. Pitois Robert E. Blackwell Hayden Close Noushin Eftekhari Sarah L. C. Giering Mojtaba Masoudi Eric Payne Joseph Ribeiro James Scott |
| author_facet | Sophie G. Pitois Robert E. Blackwell Hayden Close Noushin Eftekhari Sarah L. C. Giering Mojtaba Masoudi Eric Payne Joseph Ribeiro James Scott |
| author_sort | Sophie G. Pitois |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-93385ca4f25546fa9464646c528dcc7b |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-93385ca4f25546fa9464646c528dcc7b2025-01-10T10:19:56ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-01-011110.3389/fmars.2024.15134631513463RAPID: real-time automated plankton identification dashboard using Edge AI at seaSophie G. Pitois0Robert E. Blackwell1Hayden Close2Noushin Eftekhari3Sarah L. C. Giering4Mojtaba Masoudi5Eric Payne6Joseph Ribeiro7James Scott8The Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, United KingdomThe Alan Turing Institute, London, United KingdomThe Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, United KingdomThe Alan Turing Institute, London, United KingdomNational Oceanography Centre, Southampton, United KingdomNational Oceanography Centre, Southampton, United KingdomThe Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, United KingdomThe Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, United KingdomThe Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, United KingdomWe 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.https://www.frontiersin.org/articles/10.3389/fmars.2024.1513463/fullplankton imagerreal timeplankton ecologyEdge AIPi-10plankton classification |
| spellingShingle | Sophie G. Pitois Robert E. Blackwell Hayden Close Noushin Eftekhari Sarah L. C. Giering Mojtaba Masoudi Eric Payne Joseph Ribeiro James Scott RAPID: real-time automated plankton identification dashboard using Edge AI at sea Frontiers in Marine Science plankton imager real time plankton ecology Edge AI Pi-10 plankton classification |
| title | RAPID: real-time automated plankton identification dashboard using Edge AI at sea |
| title_full | RAPID: real-time automated plankton identification dashboard using Edge AI at sea |
| title_fullStr | RAPID: real-time automated plankton identification dashboard using Edge AI at sea |
| title_full_unstemmed | RAPID: real-time automated plankton identification dashboard using Edge AI at sea |
| title_short | RAPID: real-time automated plankton identification dashboard using Edge AI at sea |
| title_sort | rapid real time automated plankton identification dashboard using edge ai at sea |
| topic | plankton imager real time plankton ecology Edge AI Pi-10 plankton classification |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1513463/full |
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