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

TL;DR

This paper develops and compares deep learning models for classifying coral bleaching in diverse underwater images, achieving high accuracy and advancing automated coral reef monitoring.

Contribution

It introduces a novel machine learning system for coral bleaching classification using a diverse global dataset and benchmarks three state-of-the-art models.

Findings

CNN achieved 88% accuracy, outperforming others

Provides insights into model performance across environmental conditions

Enhances autonomous coral reef monitoring capabilities

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.