Fish recognition model for fraud prevention using convolutional neural networks

Abstract

Fraud, misidentification, and adulteration of food, whether unintentional or purposeful, are a worldwide and growing concern. Aquaculture and fisheries are recognized as one of the sectors most vulnerable to food fraud. Besides, a series of risks related to health and distrust between consumer and popular market makes this sector develop an effective solution for fraud control. Species identification is an essential aspect to expose commercial fraud. Convolutional neural networks (CNNs) are one of the most powerful tools for image recognition and classification tasks. Thus, the objective of this study is to propose a model of recognition of fish species based on CNNs. After the implementation and comparison of the results of the CNNs, it was found that the Xception architecture achieved better performance with 86% accuracy. It was also possible to build a web application mockup. The proposal is easily applied in other aquaculture areas such as the species recognition of lobsters, shrimp, among other seafood.