EDIBLE FISH IDENTIFICATION BASED ON MACHINE LEARNING

Abstract

Automated fish identification system has a beneficial role in various fields. Fish species can usually be identified based on visual observation and human experiences. False appreciation can cause food poisoning. The proposed system aims to efficiently and effectively identify edible fish from poisonous ones based on three machine learning (ML) techniques. A total of 300 fish images are used, collected from 20 species with differences in shapes, sizes, and colors. Hybrid features were extracted and then fed to three types of ML techniques: k-nearest neighbor (K-NN), support vector machine (SVM), and neural networks (NN). The 300 fish images are divided into two: 70% for training and 30% for testing. The accuracy rates for the presented system were 91.1%, 92.2%, and 94.4% for KNN, SVM, and NNs, respectively. The proposed system is evaluated using four terms: precision, sensitivity, F1-score, and accuracy. Results show that the proposed approach achieved higher accuracy compared with other recent pertinent studies.