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.

翻译：印度尼西亚水域珊瑚礁丰富的生物多样性是必须保护的宝贵资产。快速的气候变化和不受控制的人类活动已导致珊瑚礁生态系统严重退化，其中珊瑚白化是礁体健康状况下降的关键指标。因此，本研究旨在开发一种精确的分类模型，以区分健康珊瑚与白化珊瑚。本研究利用通过Flickr API从Flickr收集的923张图像组成的专用数据集。该数据集包含两个不同的类别：健康珊瑚（438张图像）和白化珊瑚（485张图像）。所有图像均经过尺寸调整，确保其最大宽度或高度不超过300像素，以保证整个数据集的图像尺寸一致性。所提出的方法采用机器学习技术，特别是卷积神经网络（CNN），以识别和区分与健康珊瑚和白化珊瑚相关的视觉模式。该数据集可用于训练和评估各种分类模型，以实现最佳性能。使用ResNet架构的结果表明，从头开始训练的ResNet模型在精确度和准确度方面均优于预训练模型。准确分类模型的成功开发为研究人员和海洋生物学家提供了重要帮助，使其能够更深入地理解珊瑚礁健康状况。此外，这些模型可应用于监测珊瑚礁生态系统的环境变化，从而为对维持海洋生物至关重要的保护和恢复工作做出有意义的贡献。