Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan Klasifikasi Citra

• URL: http://arxiv.org/abs/2308.04337v1
• Date: Tue, 8 Aug 2023 15:30:08 GMT
• Title: Pengembangan Model untuk Mendeteksi Kerusakan pada Terumbu Karang dengan Klasifikasi Citra
• Authors: Fadhil Muhammad, Alif Bintang Elfandra, Iqbal Pahlevi Amin, Alfan Farizki Wicaksono
• Abstract summary: This study utilizes a specialized dataset consisting of 923 images collected from Flickr using the Flickr API. The method employed in this research involves the use of machine learning models, particularly convolutional neural networks (CNN) It was found that a from-scratch ResNet model can outperform pretrained models in terms of precision and accuracy.
• Score: 3.254879465902239
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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