Chapter 3: Artificial Intelligence Tools for Processing and Quality Detection of Fish and Fisheries Products Check Access

In the seafood industry, quality evaluation of fish and fishery products, including fillets, plays a significant role in determining consumer satisfaction and profitability for the industry. Traditional methods of assessing fish quality are often time-consuming, subjective, and prone to errors. With the advancements in artificial intelligence (AI) technologies, there has been increasing interest in executing AI tools for the quick and unbiased screening of fish fillet quality. This chapter will focus on how to use AI tools such as machine learning algorithms and computer vision techniques for detecting and classifying different quality attributes in fish fillets i.e., freshness, color, texture, presence of defects, and overall appearance. When compared to manual inspection methods, the integration of AI technologies into quality control processes has several benefits which include higher accuracy rate, uniformity, as well as increased efficiency rates. The primary components of AI-driven systems for assessing fish fillet quality are discussed, including data acquisition, feature extraction, model training and validation. The challenges and limitations of implementing AI tools in real-world fish processing environments are also covered, including the need for large annotated datasets, environmental variables, and fish species diversity. Furthermore, this chapter highlights emerging trends and future directions in the field, such as the development of multispectral imaging techniques, deep learning architectures, and the potential for real-time quality monitoring systems. By adopting AI tools, the seafood industry can enhance product quality control, reduce waste, and meet the increasing demands for high-quality fish products worldwide.