Harnessing AI for Enhanced Underwater Biodiversity Monitoring

Enabling effective monitoring of marine species, habitats, and ecosystem health
Underwater image

Harnessing AI for Enhanced Underwater Biodiversity Monitoring

Enabling effective monitoring of marine species, habitats, and ecosystem health
Title: Harnessing AI for Enhanced Underwater Biodiversity Monitoring 
Dates: 2024- 2028 
Project partners: FUGRO, University of Exeter 
University of Plymouth staff: Dr Dena Bazazian (PI), Professor Kerry Howell
 
Harnessing AI for Enhanced Underwater Biodiversity Monitoring explores the application of artificial intelligence and deep learning to improve the monitoring of marine ecosystems using autonomous and robotic sensing platforms. As marine environments face increasing pressure from biodiversity loss and climate change, there is a growing need for efficient, scalable, and accurate methods of collecting and analysing ecological data. 
The project investigates how AI can be used to automate the interpretation of underwater imagery and environmental observations, enabling more effective monitoring of marine species, habitats, and ecosystem health. 
 
 

Project objectives

The project aims to develop advanced AI-driven approaches that enhance the efficiency, autonomy, and endurance of marine robotic and autonomous systems used for biodiversity monitoring. Key objectives include automating the identification and classification of marine species from underwater imagery, reducing the reliance on manual data analysis, improving the decision-making capabilities of autonomous platforms during missions, and increasing the scale and frequency of ecological observations. 
Ultimately, the research seeks to reduce the cost and time associated with marine data collection while generating higher-quality biodiversity information for scientific and environmental management purposes. 

By harnessing the power of artificial intelligence, this project aims to transform marine biodiversity monitoring, enabling autonomous systems to observe, interpret, and respond to the underwater environment with unprecedented efficiency and intelligence.

Dena BazazianDr Dena Bazazian
Lecturer in Robotics and Machine Vision

 
The global challenges of biodiversity loss and climate change have highlighted the urgent need for comprehensive environmental monitoring. Effective conservation and ecosystem management depend on accurate observations of species distributions, population dynamics, and habitat conditions. However, marine biodiversity data remain limited in both spatial and temporal coverage due to the vastness and inaccessibility of underwater environments. While autonomous underwater vehicles, robotic platforms, and advanced sensing technologies have significantly expanded data collection capabilities, the large volumes of imagery generated often require extensive human interpretation, creating bottlenecks in analysis and limiting the full potential of these systems. 
This project addresses these challenges by integrating artificial intelligence and deep learning into marine biodiversity monitoring workflows. AI algorithms can automatically process, analyse, and interpret underwater imagery, enabling rapid detection, classification, and quantification of marine organisms and habitats. 
By embedding intelligent decision-making capabilities within autonomous and robotic platforms, the system can adapt monitoring strategies in real time, improving mission efficiency and data quality. 
The resulting approach reduces the burden of manual image analysis, increases monitoring coverage and frequency, and supports more informed conservation, resource management, and climate resilience efforts in marine ecosystems. 

Centre for Decarbonisation and Offshore Renewable Energy 

In response to climate change imperatives, we are bringing together a critical mass of leading research and expertise from across the University of Plymouth. Through co-creation and collaboration with partners from business, government and key communities from across the globe, the Centre aims to be a beacon for the University’s whole-system transdisciplinary approach to solutions-oriented research, accelerating sustainable developments in decarbonisation and renewable energy.
Centre for Decarbonisation and Offshore Renewable Energy