About This Project

We hypothesize that near real-time monitoring using PTZ CCTV cameras and the YOLOv10 AI algorithm can effectively monitor coral reef restoration, reduce fieldwork, and enable rapid response to disturbances. Our primary focus is to determine if this technology can accurately assess reef health and growth and provide early warnings to stakeholders. We will also evaluate the effectiveness of UV light in monitoring coral spawning and the ability of AI models to track fish and classify coral cover.

Ask the Scientists

What is the context of this research?

This research focuses on a coral reef of significant ecological importance in West Lombok, particularly in the Coral Triangle region. We chose this reef because it represents a typical restoration site facing threats such as bleaching and storm damage. The monitoring methods, including live underwater cameras and the YOLOv10 algorithm, were chosen for their ability to provide accurate, real-time data while minimizing resource-intensive fieldwork. This area is critical as a habitat for marine species and supports the local economy through tourism and fishing. Previous studies by NGOs have highlighted the critical state of the reef and the urgent need for innovative restoration and monitoring techniques.

What is the significance of this project?

Our innovation addresses effective coral reef monitoring by providing continuous, real-time data, eliminating traditional, labor-intensive methods that cause delayed responses to disturbances. Using PTZ CCTV cameras with self-cleaning underwater housings ensures uninterrupted monitoring. Solar-powered systems enhance sustainability, while UV lights and data logger buoys enable night-time observations and record key parameters like pH, dissolved oxygen, temperature, and salinity 24/7. Stakeholders, including policymakers and conservationists, can access this live stream, enhancing coral reef restoration assessment and monitoring.

What are the goals of the project?

Our project aims to develop a machine learning model to monitor coral reefs and deploy live underwater cameras to obtain real-time footage from two separate reefs. The machine learning model, using YOLOv10, will classify coral from photogrammetric data. The PTZ CCTV cameras, equipped with self-cleaning housings and UV lights for nighttime monitoring, will provide continuous, high-quality footage. Solar power and data logger buoys will ensure sustainable operation by recording vital parameters such as pH, dissolved oxygen, temperature and salinity. This setup will allow stakeholders, including policymakers and conservationists, to access live data for timely intervention and better decision-making, increasing the efficiency and effectiveness of coral reef restoration efforts.