Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study

Abstract

Fish is regarded as an important protein source in human nutrition due to its high concentration of omega-3 fatty acids In traditional global cuisine, fish holds a prominent position, with seafood restaurants, fish markets, and eateries serving as popular venues for fish consumption. However, it is imperative to preserve fish freshness as improper storage can lead to rapid spoilage, posing risks of potential foodborne illnesses. To address this concern, artificial intelligence techniques have been utilized to evaluate fish freshness, introducing a deep learning and machine learning approach. Leveraging a dataset of 4476 fish images, this study conducted feature extraction using three transfer learning models (MobileNetV2, Xception, VGG16) and applied four machine learning algorithms (SVM, LR, ANN, RF) for classification. The synergy of Xception and MobileNetV2 with SVM and LR algorithms achieved a 100% success rate, highlighting the effectiveness of machine learning in preventing foodborne illness and preserving the taste and quality of fish products, especially in mass production facilities.

Keywords

• machine learning
• transfer learning
• feature extraction
• fish freshness