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Updated: Jun 12, 2026

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

An integrated deep learning framework for automated coral health assessment using a custom-annotated database.

Dua Weraikat¹, Febin Antony²

¹Mechanical & Industrial Engineering Department, Rochester Institute of Technology, Dubai, United Arab Emirates.

Marine Pollution Bulletin

|June 10, 2026

Summary

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Automated coral health monitoring is crucial for conservation. This study developed a deep learning framework using HSV preprocessing and YOLOv8-ResNet50 for accurate coral bleaching detection and classification.

Area of Science:

Marine Biology
Computer Science
Environmental Monitoring

Background:

Coral reefs are threatened by climate change, leading to bleaching events.
Scalable and automated tools are needed for effective coral health monitoring.
Existing methods lack the precision and automation required for widespread application.

Purpose of the Study:

To develop and evaluate a deep learning framework for automated coral health assessment.
To compare different image preprocessing techniques for coral image analysis.
To identify optimal deep learning models for object detection and classification of coral bleaching.

Main Methods:

A custom dataset of 2,640 underwater coral images was used.
Four preprocessing strategies (RGB normalization, HSV conversion, histogram equalization, color jittering) were evaluated.

Keywords:

Coral bleaching Coral health monitoring Deep learning ResNet50 Underwater imaging YOLOv8

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An Integrated Micro-Device System for Coral Growth and Monitoring

An Integrated Micro-Device System for Coral Growth and Monitoring

Published on: July 21, 2023

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Last Updated: Jun 12, 2026

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

An Integrated Micro-Device System for Coral Growth and Monitoring

An Integrated Micro-Device System for Coral Growth and Monitoring

Published on: July 21, 2023

Object detection (YOLOv8, YOLOv4) and image classification (ResNet50, VGG16, VGG19, EfficientNet) models were implemented and compared.

Main Results:

HSV preprocessing enhanced detection and classification performance while maintaining image fidelity.
YOLOv8 significantly outperformed YOLOv4 in object detection (mAP@50: 0.986).
ResNet50 achieved the highest classification accuracy (92.51%) and F1-score (0.93).

Conclusions:

The proposed YOLOv8-ResNet50 framework offers a reproducible and scalable solution for automated coral health monitoring.
This framework can aid in marine conservation efforts and environmental monitoring systems.
Effective preprocessing and deep learning models are key to accurate coral health assessment.