Fish Quality Assessment Using Hyperspectral Imaging and Computer Vision: A Review

The global food industry prioritizes the quality and safety of fish and seafood products due to their perishable nature and the increasing consumer demand for nutritious, high-quality protein sources. Traditional quality assessment methods—such as sensory evaluation, chemical analysis, physical testing, and microbiological testing—form the foundation of current practices but face notable limitations, including subjectivity, destructiveness, and labor-intensive procedures that delay results. Motivated by the need for faster, more reliable, and non-destructive quality control systems, this review investigates how emerging technologies can address these limitations. Specifically, it aims to answer the following review questions: (1) What are the limitations of traditional fish quality assessment methods? (2) How can hyperspectral imaging (HSI) and computer vision improve the accuracy and efficiency of quality assessment? (3) What roles do machine learning and deep learning techniques play in enhancing these technologies? This review explores the integration of HSI and computer vision as cutting-edge, non-invasive technologies enabling real-time, comprehensive evaluation of key fish quality attributes such as freshness, safety, nutritional content, and species verification. The fusion of HSI and computer vision with advanced learning algorithms enhances precision in quality control, reduces food waste, and supports compliance with modern standards. Finally, the review underscores the need for continued research to drive sustainable innovation and strengthen consumer confidence.