Taxonomic Identification of Sri Lankan Freshwater Fish based on Advanced Feature Extraction Techniques

Abstract

Sri Lanka is a tropical composed of different kinds of animals living in different environments. Among these, various kinds of fish species can be identified around Sri Lankan rivers and basins. Freshwater fish species vary between marine and brackish forms. Some human activities destroy their environment and, as a result, Sri Lankan freshwater fish species are at risk. Consequently, the implementation of the freshwater fish classification system has become very important to remedy this situation. Research indicates that Malpulutta Kretseri, Belontia Signata, and Puntis Tittaya are the freshwater fish species selected for classification. The main objective is to extract features more precisely and accurately while optimizing each feature extraction technique to the optimum level. Initially, four algorithms were used, and checked the results were. Then, two better-performing algorithms namely, SIFT and ORB were sorted out and carried out further tests. These two algorithms used corners, blobs, and edges to extract features. Furthermore, the test was done by segmenting as Body, Head, and Fins, and the results were improved significantly. For implementing the system 1000 data training images and 180 data testing images and data validation images were used. ORB algorithm gives 96.7% accuracy and SIFT algorithm gives 85% accuracy. The segmentation method adapted to the characteristics results in a precision of 82%. According to the research, the ORB algorithm-based feature extraction is the more sophisticated technique.