Description

The goal of this project is to develop a computer-vision based non-intrusive automatic data collection mechanism to collect images and give insights about ecological succession on coral reefs in the Vamizi Island, allowing biologists to analyze data in real-time and infer on animals life story, behavior and population in Mozambican waters.

Rationale

Coral reefs are among the world’s most diverse ecosystems, with more than 800 species of corals providing habitat and shelter for approximately 25% of global marine life, although they cover less than 0.1% of the ocean floor. Coral reefs are also extremely valuable ecosystems providing livelihood for 1 billion people, and generate 2.7 trillions US Dollars from fisheries, coastal protection, tourism and recreation each year worldwide.

Nevertheless, coral reefs are rapidly declining due to various global and local factors such as overfishing, climate change, ocean acidification, pollution and unsustainable coastal development.

In this context, technological resources have been used for monitoring and analysing the state of coral reefs, and to allow biologists to obtain data in real-time to know about animals’ life story, behaviour, population, and survivorship, collecting valuable data that informs sound decision-making and management/conservation efforts.

Different studies show various approaches for collecting data for marine biodiversity conservation purposes, such as using Remotely Operated Vehicles, Autonomous Underwater Vehicles, and fixed underwater video cameras equipped with Video Analytics Services Platforms.

Most of these studies developed deep learning tools for rapid and large-scale automatic collection and annotation of marine data. However, these studies suggested that to improve current solutions, convolutional neural networks have to be optimised and backup power supplies must be improved.

Moreover, some studies also consider applying infrared cameras, which would enable night-time video capture to create a complete picture of the coral ecosystem. In Africa, however, little or no research has focused on these approaches to apply advanced technology to research marine ecology conservation.

Outcomes

In the long-term, resolving this question will help gain insight on the ecological processes around artificial reefs (particularly important in the context of the oil and gas developments occurring in Mozambique and which will warrant the implementation of reef restoration measures).

Further, this system will be helpful to develop many other research projects which require long periods of observation in remote reefs where permanent and nighttime access is limited. Additionally, this project will create capacity in the young mozambican research community regarding the application of Artificial Intelligence technologies to tackle marine conservation issues.

Vision

This project is an opportunity to pioneer the development of new technologies that will ultimately support conservation effort through enhanced data collection and processing.

The vision is to improve data collection capacity by building on top of already existing systems, namely by developing a different mechanism to provide power supply capable of maintaining such systems in coral reefs located more than a few kilometres from shore by using floating solar panels instead.

In the long-run, the project will be replicated for different coral reefs to allow biologists to obtain data in real-time and learn about animals’ life story, behaviour, and population dynamics. In addition, multiple units would be deployed at several locations to allow for more comprehensive research or monitoring reefs from various angles.

Personel

Erwan Sola, PhD ( Project Lead), Investigator in the Marine Ecology Department, Faculty of Natural Science, Lúrio University, Mozambique. Experience in project coordination. Coral biology specialist. Extensive fieldwork experience on coral reefs. He will contribute to concept development, project coordination and ecological data analysis.

Luís Pina, MSc Computer Engineering Department, Faculty of  Engineering, Lúrio University, Mozambique. Luis Pina has his Master degree in Information Technology, with experience in developing classification models. He will contribute to this project through data pre-processing and developing classification models. Also, he will be involved in developing the object detection model.

Tiago Azevedo, PhD Candidate Department of Computer Science and Technology, University of Cambridge, United Kingdom. 4th-year Computer Science PhD student, with experience in developing Deep Learning and Machine Learning models in real-world settings. He will contribute to this project through support in coding the object detection model.

Lourenço Matandire , BSc Mechanical Engineering Department, Faculty of Engineering, Lúrio University, Mozambique. Lourenco Matandire is a Mechatronics Engineering that will be responsible for creating and providing the assessment to the Flexible Underwater Observatory (FUO) and managing its power supply.

Boaventura Manhique, BSc Computer Engineering Department, Faculty of Engineering, Lúrio University, Mozambique. Bonaventure Manhique is a Computer Engineering in Networking with a deep understanding of electronics. He will be responsible for maintaining and managing all means of communication and information sharing between the FUO and the biologists.

Disclaimer

This work has been funded by Artificial Intelligence 4 Development (AI4D) programme as part of the AI4D/IndabaX Innovation Grant programme.