Abstract

Abstract: Accurate identification of marine species is critical for effective fishery management, biodiversity conservation, and the aquaculture industry. Traditional methods of fish identification rely on expert knowledge and manual labor, making them time- consuming, expensive, and error-prone. In this research, we explore a machine learning-based approach to automate the classification of nine fish species using image recognition techniques. The fish species under study include Black Sea Sprat, Gilt- Head Bream, Horse Mackerel, Red Sea Bream, Shrimp, Trout, Striped Red Mullet, Sea Bass, and Red Mullet. We collected 1000 images per species, yielding a total of 9000 images, with 750 used for training and 250 reserved for testing. We utilized convolutional neural networks (CNNs) pre-trained on the ImageNet dataset and fine-tuned them to perform fish species classification. Preliminary experiments showed that CNNs, particularly transfer learning models like VGG16 and ResNet50, achieved accuracy rates as high as 98.6% on the test set. This research highlights the potential of deep learning in automating marine species identification, enabling faster, more reliable monitoring.