Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan Klasifikasi Citra

TL;DR

This paper develops a CNN-based classification model using ResNet architecture to distinguish healthy from bleached corals in images, aiding reef health monitoring and conservation efforts.

Contribution

It introduces a specialized dataset and demonstrates that training ResNet from scratch yields better performance than pretrained models for coral health classification.

Findings

ResNet trained from scratch outperforms pretrained models in accuracy and precision.

A dataset of 923 coral images was used for training and evaluation.

The model can assist in environmental monitoring and coral reef conservation.

Abstract

The rich biodiversity of coral reefs in Indonesian waters represents a valuable asset that must be preserved. Rapid climate change and uncontrolled human activities have caused significant degradation of coral reef ecosystems, including coral bleaching, which is a critical indicator of declining reef health. Therefore, this study aims to develop an accurate classification model to distinguish between healthy corals and bleached corals. This research utilizes a specialized dataset consisting of 923 images collected from Flickr using the Flickr API. The dataset comprises two distinct classes: healthy corals (438 images) and bleached corals (485 images). All images were resized so that the maximum width or height does not exceed 300 pixels, ensuring consistent image dimensions across the dataset. The proposed approach employs machine learning techniques, particularly convolutional neural networks (CNNs), to identify and differentiate visual patterns associated with healthy and bleached corals. The dataset can be used to train and evaluate various classification models in order to achieve optimal performance. Using the ResNet architecture, the results indicate that a ResNet model trained from scratch outperforms pretrained models in terms of both precision and accuracy. The successful development of an accurate classification model provides substantial benefits for researchers and marine biologists by enabling a deeper understanding of coral reef health. Furthermore, these models can be applied to monitor environmental changes in coral reef ecosystems, thereby contributing meaningfully to conservation and restoration efforts that are vital to sustaining marine life.