Deep Learning Models for Coral Bleaching Classification in Multi-Condition Underwater Image Datasets

Julio Jerison E. Macrohon; Gordon Hung

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arXiv · 2511.00021

Deep Learning Models for Coral Bleaching Classification in Multi-Condition Underwater Image Datasets

Julio Jerison E. Macrohon·Gordon Hung

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Abstract

Coral reefs support numerous marine organisms and are an important source of coastal protection from storms and floods, representing a major part of marine ecosystems. However coral reefs face increasing threats from pollution, ocean acidification, and sea temperature anomalies, making efficient protection and monitoring heavily urgent. Therefore, this study presents a novel machine-learning-based coral bleaching classification system based on a diverse global dataset with samples of healthy and bleached corals under varying environmental conditions, including deep seas, marshes, and coastal zones. We benchmarked and compared three state-of-the-art models: Residual Neural Network (ResNet), Vision Transformer (ViT), and Convolutional Neural Network (CNN). After comprehensive hyperparameter tuning, the CNN model achieved the highest accuracy of 88%, outperforming existing benchmarks. Our findings offer important insights into autonomous coral monitoring and present a comprehensive analysis of the most widely used computer vision models.

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BibTeX RIS

Julio Jerison E. Macrohon, Gordon Hung. 2025-10-24. Deep Learning Models for Coral Bleaching Classification in Multi-Condition Underwater Image Datasets. https://arxiv.org/abs/2511.00021

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