Harnessing Computer Vision and Deep Learning to Monitor Coral Reef Health

Coral reefs represent critical biodiversity hotspots within marine ecosystems, hosting a significant proportion of global marine organisms. The escalating phenomenon of coral bleaching, characterized by the whitening of corals due to rising water temperatures, signifies a severe decline in reef health and poses an existential threat to these vital habitats. Addressing this environmental challenge necessitates the development of rapid and effective monitoring solutions to mitigate bleaching progression and safeguard coral reef survival. This research introduces an innovative system leveraging deep learning and computer vision techniques for comprehensive coral reef health assessment and the early detection of bleaching events. The methodology focuses on coral reefs within the Red Sea region, employing

advanced object detection models, specifically YOLOv8 and YOLOv9. These models were trained and evaluated on an extensive, augmented dataset comprising 10,285 labeled images, meticulously categorized into healthy, bleached, and dead coral states. The proposed system integrates a user-friendly interface for image classification and incorporates an automated notification mechanism to alert relevant authorities upon the identification of bleaching or coral mortality. Performance evaluation indicated that the YOLOv9 model achieved a mean Average Precision (mAP) of 89%, marginally surpassing YOLOv8's 88%, thereby demonstrating the efficacy of this approach in environmental monitoring.

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