VGG19's Role in Safeguarding Coral Reefs using a CNN-Based Monitoring Model

The use of Transfer Learning and Deep Convolutional Neural Networks (CNNs) to the preservation of coral reefs is the subject of this investigation. To do this, it introduces a dataset of 923 images that label corals as healthy or bleached. This research presents a Convolutional Neural Network (CNN) based automated photo classification model with the goal of automating the laborious task of manually recognising coral reefs. Given the critical role that coral reefs play and the challenges they encounter, including coral bleaching, this is of utmost importance. This method makes use of training, validation, and test datasets, as well as the VGG19 pre-trained CNN model. Callbacks are used to implement model monitoring. Model hyperparameters like batch size, epochs, and input shape are defined directly. The classification report, confusion matrix, and accuracy are common metrics utilised for evaluating machine learning. In terms of efficiency, scalability, and real-time monitoring, the study emphasises the benefits of the proposed paradigm. Positive results, such as a 74% accuracy rate, show that CNNs can quickly detect coral reefs and monitor their health. In order to make educated judgements and launch conservation efforts, this is crucial.