Harnessing Computer Vision and Deep Learning to Monitor Coral Reef Health

Coral reefs have emerged as the most biodiverse and important entities in the marine ecosystem, as they house 25% of all marine organisms. As water temperatures rise in some sea areas, coral reef colors gradually turn white. This phenomenon, known as coral bleaching, signifies the deterioration of coral reef health and poses a significant threat to their survival. There is an urgent need for rapid and effective solutions to mitigate these threats, limit the spread of bleaching, and protect coral reefs. This study proposes a novel system that utilizes deep learning and computer vision to assess coral reef health and detect early signs of bleaching. Focusing on coral reefs in the Red Sea, the YOLOv8 and YOLOv9 object detection models were used on an augmented dataset of 10,285 labeled images representing healthy, bleached, and dead corals. The system includes a user-friendly interface for image classification and automatic notification of relevant authorities upon detection of bleaching or death. The evaluation results showed that YOLOv9 achieved a slightly higher mean Average Precision (mAP) of 89% compared to YOLOv8 (88%), demonstrating the effectiveness and potential of the system for real-time coral reef monitoring. This research offers a practical, automated solution for early detection, reducing human effort and achieving faster results, ultimately saving coral reefs from irreversible damage.