Fake Hilsa Fish Detection Using Machine Vision

TL;DR

This paper presents a machine vision-based method for accurately distinguishing genuine Hilsa fish from counterfeit ones, addressing food safety concerns and aiding in authentic fish verification.

Contribution

First to research Hilsa fish identification using deep learning, with a dataset of over 16,000 images and a comparative analysis of models achieving high accuracy.

Findings

DenseNet201 achieved 97.02% accuracy

Deep learning models effectively classify real vs. fake Hilsa

The method can assist in preventing counterfeit fish sales

Abstract

Hilsa is the national fish of Bangladesh. Bangladesh is earning a lot of foreign currency by exporting this fish. Unfortunately, in recent days, some unscrupulous businessmen are selling fake Hilsa fishes to gain profit. The Sardines and Sardinella are the most sold in the market as Hilsa. The government agency of Bangladesh, namely Bangladesh Food Safety Authority said that these fake Hilsa fish contain high levels of cadmium and lead which are detrimental for humans. In this research, we have proposed a method that can readily identify original Hilsa fish and fake Hilsa fish. Based on the research available on online literature, we are the first to do research on identifying original Hilsa fish. We have collected more than 16,000 images of original and counterfeit Hilsa fish. To classify these images, we have used several deep learning-based models. Then, the performance has been compared between them. Among those models, DenseNet201 achieved the highest accuracy of 97.02%.