Stressed Coral Reef Identification Using Deep Learning CNN Techniques

Abstract

Deep learning is a machine learning technique that allows the computer to process things that occur naturally to humans. Today, deep learning techniques are commonly used in computer vision to classify images and videos. As a result, for challenging computer vision problems, deep learning provides state of the art solutions to it. Coral reefs are an essential resource of the earth. A new study finds the planet has lost half of its coral reefs since 1950. It is necessary to restore and prevent damage to coral reefs as they play an important role in maintaining a balance in the marine ecosystem. This proposed work helps to prevent the corals from bleaching and restore them to a healthy condition by identifying the root cause of the threats. In the proposed work, using deep learning CNN techniques, the images are classified into Healthy and Stressed coral reefs. Stressed coral reefs are an intermediate state of coral reef between healthy and bleached coral reefs. The pre-trained models Resnet50 and Inception V3 are used in this study to classify the images. Also, a proposed CNN model is built and tested for the same. The results of Inception V3 and Resnet50 are improved to 70% and 55% by tuning the hypermeters such as dropouts and batch normalisation. Similarly, the proposed model is tuned as required and obtains a maximum of up to 90% accuracy. With large datasets, the optimum amount of neural networks and tuning it as required brings higher accuracy than other methods.