Towards Sustainable Seas Utilizing VGG19 Empowered Coral Health Evaluation

The application of Deep Convolutional Neural Networks (CNNs) and Transfer Learning to coral reef conservation is examined in this article. In order to achieve this, a collection of 923 images categorising corals as either bleached or healthy is introduced. This article presents an automated photo classification approach based on Convolutional Neural Networks (CNNs) to address the labor-intensive task of manually recognising coral reefs. This is especially significant because of the problems coral bleaching and other critical functions of coral reefs. The method makes use of training, validation, and testing datasets together with the pre-trained CNN model VGG19. Callbacks are used in model monitoring implementation. Batch size, epochs, and input form are among the model's explicitly stated hyperparameters. In machine learning, common assessment metrics include classification report, confusion matrix, and accuracy. The paper demonstrates how the proposed paradigm has benefits in terms of scalability, efficiency, and real-time monitoring. Encouraged results are shown by the 74% accuracy, which shows that CNNs can quickly detect and monitor coral reef health. Making educated judgements and starting conservation projects need this.