A Deep Learning Approach for Marine Animal Classification: Enhancing Taxonomic Identification and Conservation Efforts

Classification of marine animals is essential for studying marine ecosystems, keeping track of biodiversity, and developing efficient conservation plans. The success of deep learning methods in picture classification tasks has increased dramatically in recent years. In this research, we propose a systematic investigation of the use of several deep learning models for the categorization of marine organisms; these models include Inception, ResNetV, ResNet50, VGG16, InceptionV3, Xception, DenseNet, MobileNet, and NASNet, among others. Models originally built on ImageNet were then fine-tuned using transfer learning before being applied to our marine animal dataset for training. Training and validation accuracy were used to rank the models, and the models' overall performance was compared to find the best one. Inception outperformed the other models in tests by a wide margin, with a training accuracy of 95.85% and a validation accuracy of 96.75. Inception's greater performance in marine animal categorization may be attributed to its deeper design and its ability to grasp complex spatial connections. When deep learning models are used to correctly categorize marine species, it may greatly improve taxonomic identification, aid conservation efforts, and make ecological study much easier. Additionally, the results highlight the significance of constructing domain-specific deep learning models for fields like marine biology, where distinct obstacles exist.