The abundant biodiversity of coral reefs in Indonesian waters is a valuable asset that needs to be preserved. Rapid climate change and uncontrolled human activities have led to the degradation of coral reef ecosystems, including coral bleaching, which is a critical indicator of coral health conditions. Therefore, this research aims to develop an accurate classification model to distinguish between healthy corals and corals experiencing bleaching. This study 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). These images have been resized to a maximum of 300 pixels in width or height, whichever is larger, to maintain consistent sizes across the dataset. The method employed in this research involves the use of machine learning models, particularly convolutional neural networks (CNN), to recognize and differentiate visual patterns associated with healthy and bleached corals. In this context, the dataset can be used to train and test various classification models to achieve optimal results. By leveraging the ResNet model, it was found that a from-scratch ResNet model can outperform pretrained models in terms of precision and accuracy. The success in developing accurate classification models will greatly benefit researchers and marine biologists in gaining a better understanding of coral reef health. These models can also be employed to monitor changes in the coral reef environment, thereby making a significant contribution to conservation and ecosystem restoration efforts that have far-reaching impacts on life.

翻译：印度尼西亚海域珊瑚礁丰富的生物多样性是需要保护的宝贵资产。快速的气候变化和不受控制的人类活动导致珊瑚礁生态系统退化，其中珊瑚白化是珊瑚健康状况的关键指标。因此，本研究旨在开发一个准确的分类模型，以区分健康珊瑚和正在白化的珊瑚。本研究使用了通过Flickr API收集的923张图像专用数据集，包含两个不同类别：健康珊瑚（438张图像）和白化珊瑚（485张图像）。这些图像已调整至宽度或高度（取较大值）最大300像素，以保持数据集尺寸一致。研究方法采用机器学习模型，特别是卷积神经网络（CNN），来识别和区分健康与白化珊瑚的视觉模式。在此背景下，该数据集可用于训练和测试各种分类模型以达到最优结果。通过利用ResNet模型，研究发现从头训练的ResNet模型在精度和准确率上可超越预训练模型。成功开发准确的分类模型将极大帮助研究人员和海洋生物学家更深入地了解珊瑚礁健康状况。这些模型还可用于监测珊瑚礁环境变化，从而为保护和生态系统恢复工作做出重要贡献，这些工作对生命产生深远影响。