Deep Dive: Enhancing Coral Reef Conservation through ResNet50 pre-trained enabled CNN Monitoring

The present study aims to examine the application of Deep Convolutional Neural Networks (CNNs) and Transfer Learning techniques within the domain of coral reef conservation. This is achieved by the utilization of a dataset including 923 pictures that categorize corals into two distinct classifications: healthy or bleached. The objective of the research is to develop an automated photo classification model using Convolutional Neural Networks (CNNs) to address the labour-intensive task of manually identifying coral reefs. This is of utmost significance given the vital role of coral reefs and the challenges they encounter, such as coral bleaching. This study employs the ResNet50 pre-trained convolutional neural network (CNN) model and incorporates training, validation, and testing datasets. Callbacks are utilized to implement model monitoring. The explicit definition of the hyperparameters of the model includes batch size, epochs, and input shape. Machine learning commonly employs evaluation measures such as accuracy, confusion matrix, and classification report. The research emphasizes the benefits of the proposed paradigm in relation to effectiveness, expandability, and instantaneous surveillance. The attained accuracy rate of 74% showcases promising results, underscoring the efficacy of Convolutional Neural Networks (CNNs) in immediately detecting and monitoring the health of coral reefs. The ability to make informed judgments and engage in conservation projects is of utmost importance.