Published Paper Details:

Underwater species exploration using deep learning technology is designed to classify underwater species from images and retrieve key details such as their biological name, extinction status, and geographic origin. The system employs advanced convolutional neural networks (CNN) for accurate image classification. A well-organized dataset of underwater species is used to train, validate, and test the model, ensuring robustness. The project integrates a backend system with a REST API to connect the model and a database storing species information. A user-friendly frontend allows seamless image upload from system or image link and instant results display. Deployed on cloud platforms, the system ensures scalability and real-time accessibility. This project has significant applications in marine biology, conservation, and environmental monitoring. By automating species identification, it aids in studying biodiversity, tracking invasive species, and supporting ecosystem preservation efforts. The innovative approach minimizes manual effort, provides accurate results, and contributes to sustainable marine research.