Automated in situ Plankton Imaging and Classification System (APICS)
Lead Research Organisation:
Plymouth Marine Laboratory
Department Name: Plymouth Marine Lab
Abstract
Marine plankton are defined as organisms with zero or small swimming velocities, which move passively with ocean currents. The definition covers a taxonomically and morphologically diverse group of organisms that span many phyla and tens of thousands of species. In size alone, plankton span many orders of magnitude, ranging from sub-micron scale unicellular life forms up to large jelly fish that can measure up to a meter in diameter.
Plankton sustain all other forms of multicellular life in the ocean. Photosynthetic members account for approximately 50 % of global primary production. The energy captured by photosynthetic plankton is passed up the food chain through predatory interactions, where it supports the growth of commercially important species of fish and shellfish. Plankton also regulate Earth's climate, through their role in the global carbon cycle.
Key to understanding the ecology of plankton and the functional role they play in marine ecosystems, is the ability to follow how plankton abundances and community composition change in time. This usually requires that organisms be visualised and classified. Historically, such measurements have been made aboard research vessels in often remote locations. The measurements are expensive and time consuming to make. The potential benefits of automating the process have been appreciated for years. However, it has taken time for suitable, commercially available instruments to become available.
In this project, we will configure an Automated, in situ Plankton Imaging and Classification System (APICS) for studying plankton dynamics at a long-term time series site, Station L4 in the Western English Channel. Images will be automatically classified using machine learning software and made publicly available. The system will enable a 100-fold increase in the frequency at which plankton data is collected. Furthermore, through automation, the system will dramatically reduce operating costs compared to current, manual ship-based sampling and classification procedures.
In a world first, APICS will automatically acquire image data in situ for plankton spanning 3-4 orders of magnitude in size, ranging from 10 microns up to 20 mm. Embedded within the Western Channel Observatory (WCO), where other physical and chemical variables are routinely measured, it will provide a unique system for studying high-frequency temporal changes in the marine environment.
Over the past 30 years, data from the WCO have shown marked changes in plankton abundances, with reductions in the number of diatoms and large copepods, and an increase in the number of meroplankton. APICS will enable such trends to be studied in fine detail, leading to improved understanding of plankton community dynamics, and the relationship between plankton and marine ecosystems as a whole.
Plankton sustain all other forms of multicellular life in the ocean. Photosynthetic members account for approximately 50 % of global primary production. The energy captured by photosynthetic plankton is passed up the food chain through predatory interactions, where it supports the growth of commercially important species of fish and shellfish. Plankton also regulate Earth's climate, through their role in the global carbon cycle.
Key to understanding the ecology of plankton and the functional role they play in marine ecosystems, is the ability to follow how plankton abundances and community composition change in time. This usually requires that organisms be visualised and classified. Historically, such measurements have been made aboard research vessels in often remote locations. The measurements are expensive and time consuming to make. The potential benefits of automating the process have been appreciated for years. However, it has taken time for suitable, commercially available instruments to become available.
In this project, we will configure an Automated, in situ Plankton Imaging and Classification System (APICS) for studying plankton dynamics at a long-term time series site, Station L4 in the Western English Channel. Images will be automatically classified using machine learning software and made publicly available. The system will enable a 100-fold increase in the frequency at which plankton data is collected. Furthermore, through automation, the system will dramatically reduce operating costs compared to current, manual ship-based sampling and classification procedures.
In a world first, APICS will automatically acquire image data in situ for plankton spanning 3-4 orders of magnitude in size, ranging from 10 microns up to 20 mm. Embedded within the Western Channel Observatory (WCO), where other physical and chemical variables are routinely measured, it will provide a unique system for studying high-frequency temporal changes in the marine environment.
Over the past 30 years, data from the WCO have shown marked changes in plankton abundances, with reductions in the number of diatoms and large copepods, and an increase in the number of meroplankton. APICS will enable such trends to be studied in fine detail, leading to improved understanding of plankton community dynamics, and the relationship between plankton and marine ecosystems as a whole.
Organisations
- Plymouth Marine Laboratory (Lead Research Organisation)
- Joint Nature Conservancy Council (Collaboration)
- Woods Hole Oceanographic Institution (Collaboration)
- Norwegian Institute of Marine Research (Collaboration)
- Centre for Environment, Fisheries and Aquaculture Science (Collaboration)
- The Finnish Environment Institute (Collaboration)
- Scottish Association for Marine Science (Collaboration)
- University of Glasgow (Collaboration)
- Hewlett Packard Enterprise (HPE) (Collaboration)
Publications
Clark J
(2025)
The Western Channel Observatory Automated Plankton Imaging and Classification System
in Oceanography
| Description | This award has allowed us to design, procure and test the world's first dual-camera plankton imaging system for remote autonomous deployments at sea. The system will form part of the Western Channel Observatory, which is operated by Plymouth Marine Laboratory and Marine Biological Association. It will transform our ability to monitor changes in plankton abundances within the Western English Channel by enabling us to sample the surface plankton population every hour, as compared with weekly monitoring that is currently undertaken at the same location using research vessels. The system will yield new insights into the community and population dynamics of plankton, which ultimately support all other forms of life in the ocean. |
| Exploitation Route | This funding has enabled the team to design and procure an automated plankton imaging system which is capable of collecting 100-fold more data than past ship-based sampling procedures. We anticipate the data being used to gain a new insight into controls on plankton community dynamics and composition, and the response of planktonic organisms to environmental change. |
| Sectors | Education Environment |
| URL | https://www.pml.ac.uk/science/projects/APICS |
| Description | The equipment was included in a documentary made by CGTN Europe. |
| First Year Of Impact | 2024 |
| Sector | Education |
| Impact Types | Cultural |
| Description | DEAL - Decentralised Learning for automated image analysis and biodiversity monitoring |
| Amount | £745,324 (GBP) |
| Funding ID | APP17631 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2024 |
| End | 04/2027 |
| Title | Automated plankton imaging and classification system (APICS) |
| Description | In this project, we have designed and procured an automated plankton imaging and classification system that consists of: - Imaging FlowCytobot (IFCB; McLane Research Laboratories, Inc.) for automatically imaging and classifying plankton that range in size from <10 µm to 150 µm, - Plankton Imager (Pi-10; Plankton Analytics Ltd.), for automatically imaging and classifying plankton from 100 µm to 20 mm in size, - Ancillary equipment, including a new bespoke buoy from which the two cameras will be deployed. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2023 |
| Provided To Others? | No |
| Impact | We are at the very early stages of collecting new data and testing the new instrument. |
| URL | https://www.pml.ac.uk/science/projects/APICS |
| Description | Collaboration between Plankton Imaging groups |
| Organisation | Centre For Environment, Fisheries And Aquaculture Science |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Sharing of marine image data for automatic classification. |
| Collaborator Contribution | Shared data and helped define technical requirements of the application. |
| Impact | The partnership involves sharing expertise in machine learning and marine taxonomy. |
| Start Year | 2024 |
| Description | Collaboration between Plankton Imaging groups |
| Organisation | Hewlett Packard Enterprise (HPE) |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Sharing of marine image data for automatic classification. |
| Collaborator Contribution | Shared data and helped define technical requirements of the application. |
| Impact | The partnership involves sharing expertise in machine learning and marine taxonomy. |
| Start Year | 2024 |
| Description | Collaboration between Plankton Imaging groups |
| Organisation | Joint Nature Conservancy Council |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Sharing of marine image data for automatic classification. |
| Collaborator Contribution | Shared data and helped define technical requirements of the application. |
| Impact | The partnership involves sharing expertise in machine learning and marine taxonomy. |
| Start Year | 2024 |
| Description | Collaboration between Plankton Imaging groups |
| Organisation | Norwegian Institute of Marine Research |
| Country | Norway |
| Sector | Academic/University |
| PI Contribution | Sharing of marine image data for automatic classification. |
| Collaborator Contribution | Shared data and helped define technical requirements of the application. |
| Impact | The partnership involves sharing expertise in machine learning and marine taxonomy. |
| Start Year | 2024 |
| Description | Collaboration between Plankton Imaging groups |
| Organisation | The Finnish Environment Institute |
| Country | Finland |
| Sector | Academic/University |
| PI Contribution | Sharing of marine image data for automatic classification. |
| Collaborator Contribution | Shared data and helped define technical requirements of the application. |
| Impact | The partnership involves sharing expertise in machine learning and marine taxonomy. |
| Start Year | 2024 |
| Description | Collaboration between Plankton Imaging groups |
| Organisation | University of Glasgow |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Sharing of marine image data for automatic classification. |
| Collaborator Contribution | Shared data and helped define technical requirements of the application. |
| Impact | The partnership involves sharing expertise in machine learning and marine taxonomy. |
| Start Year | 2024 |
| Description | Collaboration between Plankton Imaging groups |
| Organisation | Woods Hole Oceanographic Institution |
| Country | United States |
| Sector | Charity/Non Profit |
| PI Contribution | Sharing of marine image data for automatic classification. |
| Collaborator Contribution | Shared data and helped define technical requirements of the application. |
| Impact | The partnership involves sharing expertise in machine learning and marine taxonomy. |
| Start Year | 2024 |
| Description | Member of the European IFCB Working Group |
| Organisation | Scottish Association For Marine Science |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Intellectual input and knowledge exchange |
| Collaborator Contribution | Knowledge exchange and access to data |
| Impact | N/A |
| Start Year | 2023 |
| Description | Member of the European IFCB Working Group |
| Organisation | Woods Hole Oceanographic Institution |
| Country | United States |
| Sector | Charity/Non Profit |
| PI Contribution | Intellectual input and knowledge exchange |
| Collaborator Contribution | Knowledge exchange and access to data |
| Impact | N/A |
| Start Year | 2023 |
| Description | Member of the Marine and Freshwater Image Classification Working Group |
| Organisation | Scottish Association For Marine Science |
| Department | SAMS Research Services Ltd (SRSL) |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Intellectual input and plankton expertise relating to the APICS Imaging FlowCytobot (IFCB) training dataset. |
| Collaborator Contribution | Expertise and guidance in developing the APICS IFCB classifier and data pipeline. |
| Impact | Collaboration of plankton scientists and data experts to develop AI models for classification of automated plankton imagery. |
| Start Year | 2025 |
| Description | APICS presentation at Marine Research Plymouth Research Dialogue Event #2 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Presentation to advertise the APICS system to local researchers in Plymouth, in the spirit of starting collaberative conversations. |
| Year(s) Of Engagement Activity | 2022 |
| Description | APICS presentation at the European Imaging FlowCytobot (IFCB) workshop meetings |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Presentation to showcase APICS plans and discuss progress with our IFCB operation and results |
| Year(s) Of Engagement Activity | 2023,2024 |
| Description | Exeter University Centre for Doctoral Training (CDT) Grand Challenge |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | PML submitted a challenge within hackathon event called Environmental intelligence: Data science & AI for sustainable futures grand challenge. The event was hosted by Exeter University's Centre for Doctoral Training from Monday 19th to Friday 23rd June. PML's challenge was titles "What's the carbon footprint of all these data? Decision support tools and methodologies that allow scientists to maximise the benefits of big data while minimising their carbon footprint". The event format allowed PML to pitch our challenge to around 60 students with the aim of convincing them to form a team and investigate our challenge (The students could choose from approx. 12 projects from multiple environmental charities and government agencies). 6 students were interested in supporting the APICS themed challenge and picked the team name "Plank-tonnes of Data". The team consisted of approximately 3 students studying Exeter University's MSc in Sustainability and 3 PhD students researching artificial intelligence in environmental science. The team reviewed the challenge text and worked on three main lines of effort: - Developing and testing machine learning (ML) algorithms that will filter and compress IFCB data to minimize storage requirements while limiting its impact on future analysis. - Designing a weighting criteria that allows the value of each IFCB output to be assessed based on the uniqueness of the data contained (providing a weighting as to its future value within the data set). - Providing tools that allow future sensors and applications to learn the lessons from this study and optimize future data pipelines. The main achievements of the team included: 1- The team used the draft PML APICS data pipeline to form a framework around which development efforts could be structured. They identified that the biggest opportunities to reduce the power consumption are in acquiring the data in the correct format and identifying valuable data. 2- The team identified a file format that reduced compressed file size by over 75% without losing information. The team also identified a tiered data structure that allows metadata and valuable data to be placed in high performance storage while data of low value is stored in un-powered storage (a cloud-based glacier). A tool was developed to automatically convert data. 3- Motivated by a recent paper on class imbalance, the team developed an innovative toolset that allowed scientists to filter the files and identify valuable data. A front end allows scientists to select a data set and cluster the images with a machine learning algorithm to identify the uniqueness of each image. Images that are identified as rare and valuable are automatically saved in high performance storage. Images that are non-valuable are saved to a low energy back up. The developed tool is available here --> https://planktonnes.streamlit.app 4- To allow the developments to be quantified and monitored, the team developed a high level dashboard in ReactJS that, built around CarbonCode, allows the carbon footprint of a data pipeline to be monitored. The developed tool is available here --> https://verdant-boba-456952.netlify.app/ The team presented their concepts and toolsets in the report out session. After the judges deliberation, it was revealed that the team won the main prize in the Grand Challenge, with judges praising the multi-disciplinary approach and the development of tools that could achieve quick results in the real world. |
| Year(s) Of Engagement Activity | 2023 |
| URL | http://www.exeter.ac.uk/research/eicdt/events/grandchallenge2023 |
| Description | Interview for ITN on the costs and benefits of AI |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | We were interviewed by ITN for a piece there were doing on the energy demands of AI, and its benefits. For the benefits, they discussed the use of AI in classifying marine image data - including that from our APICS cameras. |
| Year(s) Of Engagement Activity | 2025 |
| URL | https://www.itv.com/news/2025-02-10/can-renewable-energy-keep-up-with-the-increasing-power-demands-o... |
| Description | Interview for documentary on plankton |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | CGTN Europe filmed us for a documentary on plankton. |
| Year(s) Of Engagement Activity | 2025 |
| URL | https://newseu.cgtn.com/news/2025-02-22/RAZOR-What-has-plankton-ever-done-for-us--1B6jKimUOcM/p.html |
| Description | Marine imaging workshop |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Approximately 25 people attended a workshop to discuss end user requirements for a new plankton classifier we are creating. |
| Year(s) Of Engagement Activity | 2025 |
| Description | Ocean Decade meeting in Barcelona, 2024 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Policymakers/politicians |
| Results and Impact | Participated in an expert panel on marine plastic pollution at the Ocean Decade meeting. Contributed to a presentation on next generation ocean technology. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://oceandecade.org/events/2024-ocean-decade-conference/ |
| Description | Promotional film using AI at Plymouth Marine Laboratory |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Short film showcasing PML's Artificial Intelligence expertise which includes plankton research using the APICS Imaging FlowCytobot (IFCB) |
| Year(s) Of Engagement Activity | 2023 |
| Description | Promotional material for APICS |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | We have developed a website and various social media pieces to advertise the work within APICS. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.pml.ac.uk/science/projects/APICS |
| Description | Royal Society Summer Science Exhibition |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | I stood on the stand called Deep heat: how a warming ocean is challenging life on earth. I spent two days talking to students, the general public, scientists, people from the media and government about ocean science. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://royalsociety.org/science-events-and-lectures/2024/summer-science-exhibition/all-exhibits/ |
