Automated Detection of Coral Reef Health Indicators Using YOLO-Based Deep Learning

Coral reefs constitute critical marine ecosystems that support biodiversity, provide coastal protection, and contribute substantially to global economic activity. These ecosystems face increasing threats from climate change, unsustainable anthropogenic activities, and pollution, necessitating timely and accurate health assessments for effective conservation management. This study presents an innovative approach utilizing the You Only Look Once (YOLO) algorithm, a state-ofthe-art deep learning object detection framework, to develop an automated real-time system for detecting coral reef health indicators and diseases with significant applications in marine biology [18]. The YOLO algorithm's capacity for rapid and precise image processing makes it particularly well-suited for identifying underwater coral pathologies, including bleaching events, tissue loss, and chromatic aberrations in photographic and video data. The proposed system leverages a meticulously curated and annotated dataset comprising images of both healthy coral formations and disease indicators, ensuring robust algorithmic performance [19]. The YOLO framework has been specifically optimized to address challenges inherent to underwater environments, including variable illumination, reduced contrast, and frequent visual obstructions. Comprehensive testing demonstrates that the system achieves notable precision and recall metrics in distinguishing between healthy and diseased coral specimens. This capability enables continuous real-time reef monitoring, providing a robust analytical tool for comprehensive ecosystem assessment. Consequently, marine biologists and conservation practitioners can implement rapid response measures to protect and restore these vulnerable ecosystems. This research underscores the transformative potential of artificial intelligence—specifically YOLO-based approaches for advancing coral reef monitoring and conservation initiatives. Future research directions include dataset expansion, detection accuracy enhancement, and integration with autonomous underwater vehicles (AUVs) to facilitate large-scale coral reef health evaluations.