Fish Species Classification Using Deep Learning and Appearance-Based Feature Extraction

Abstract

As aquatic ecosystems face increasing threats and challenges, the accurate and efficient classification of fish species has become a critical task for fisheries management, biodiversity conservation, and environmental monitoring. This research explores the application of deep learning techniques in conjunction with appearance-based feature extraction for the automated and precise identification of fish species from images. The study employs a comprehensive dataset of underwater fish images, encompassing various species, sizes, and environmental conditions. Convolutional Neural Networks (CNNs) are used for feature extraction and classification, allowing for the capture of intricate visual characteristics that distinguish different fish species. Moreover, appearance-based features, such as colour patterns, fin shapes, and other morphological attributes, are integrated into the model to enhance classification accuracy. Additionally, the study introduces Improved Sequential Forward Selection (ISFS) as a method to select relevant features for classification, further enhancing the efficiency and effectiveness of the model. The research evaluates the performance of the proposed methodology against existing classification methods, emphasizing the advantages of deep learning and appearance-based feature extraction in terms of classification accuracy, robustness, and efficiency. In addition, the study explores the potential for real-world applications in fisheries management, species monitoring, and ecological research. The findings of this research demonstrate that the combination of deep learning and appearance-based feature extraction provides a promising approach for fish species classification, offering a valuable tool for conservation efforts and ecological studies. This research contributes to the growing body of knowledge in the field of computer vision and biodiversity conservation and opens up new possibilities for automated fish species identification in aquatic environments.