Many diverse living forms found in aquatic habitats are critical to ecological balance and scientific advancement. The classification and identification of aquatic life, including fish, marine animals, and other species, is critical to understanding and protecting these ecosystems. This project uses machine learning to classify aquatic life automatically. The system collects underwater data, preprocesses it to improve quality, and then employs models trained on an annotated dataset of aquatic species. These models can identify and classify underwater animals with high accuracy, and they support both real-time and batch processing. A novel application of Convolutional Neural Networks (CNNs), a type of deep learning, is used to automate the classification of aquatic life from images and videos in this study. It is impossible to overestimate the importance of aquatic ecosystems in maintaining biodiversity and ecological balance. The various species that live in these environments are vital to the food chain, nutrient cycling, and ecosystem stability. As human activities such as pollution, overfishing, and habitat destruction continue to have an impact on aquatic ecosystems, there is a growing need to understand, monitor, and protect these environments. Traditionally, aquatic species classification has been labor-intensive, relying on manual observation and taxonomic expertise. However, the scalability of this approach is limited, particularly when dealing with vast and diverse aquatic ecosystems. This study addresses this issue by using machine learning, specifically Convolutional Neural Networks, to automate the species classification process.

Convolutional Neural Networks Machine Learning Aquatic life Classification