Marine Animal Classification Using Deep Learning and Convolutional Neural Networks (CNN)

The development of artificial intelligence greatly helps in this challenging task of classifying different species of marine animals based on their visual characteristics. Using Convolutional Neural Networks (CNN), this study developed an accurate and efficient method to classify marine animals. The project adopted a systematic approach, from data collection and preprocessing to building a CNN architecture, training the model, and deploying it for performance evaluation and real-time forecasting. The task at hand is to collect a variety of photos of marine animals and prepare them by compressing and flattening the pixel values. Convolutional, pooling, and fully connected layers contribute to the architecture of the CNN model. The dataset is used to train the model, and the hyperparameters are tuned for the best results. The completion of this research highlights the achievement of its goals, including the creation of a reliable CNN-based model for classifying marine fauna. The trained model has excellent accuracy when identifying and classifying different species of marine animals and has an accuracy of 87.99%. The performance of this model has been further improved and will help our proposed models and educational programs to increase its capabilities. Overall, this experiment demonstrates the effectiveness of CNNs in marine animal classification and emphasizes the potential for further development in the area. This research helps us understand and conserve marine ecosystems and supports a number of scientific, conservation, and educational initiatives by precisely identifying and classifying marine animal species.