Deep Learning Models for Coral Bleaching Classification in Multi-Condition Underwater Image Datasets
This work addresses efficient coral reef monitoring for marine conservation, but it is incremental as it applies existing methods to a new dataset.
This study tackled the problem of classifying coral bleaching in underwater images by benchmarking three deep learning models on a diverse global dataset, with the CNN model achieving the highest accuracy of 88%.
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.