Recent advances in fish cutting: From cutting schemes to automatic technologies and internet of things innovations

作者：Qing Li, Huawei Ma, Weiqing Min, Yang Wang, Ran Zhao, Yongjie Zhou, Yuqing Tan, Yongkang Luo, Hui Hong · 发表于：Comprehensive Reviews in Food Science and Food Safety · 年份：2024 · DOI：10.1111/1541-4337.70039 · 被引用次数：11 · 研究领域：Water Quality Monitoring Technologies、Meat and Animal Product Quality、Microplastics and Plastic Pollution

Fish-cutting products are widely loved by consumers due to the unique nutrient composition and flavor in different cuts. However, fish-cutting faces the issue of labor shortage due to the harsh working environment, huge workload, and seasonal work. Hence, some automatic, efficient, and large-scale cutting technologies are needed to overcome these challenges. Accompanied by the development of Industry 4.0, the Internet of Things (IoT), artificial intelligence, big data, and blockchain technologies are progressively applied in the cutting process, which plays pivotal roles in digital production monitoring and product safety enhancement. This review focuses on the main fish-cutting schemes and delves into advanced automatic cutting techniques, showing the latest technological advancements and how they are revolutionizing fish cutting. Additionally, the production monitoring architecture based on IoT in the fish-cutting process is discussed. Fish cutting involves a variety of schemes tailored to the specific characteristics of each fish cut. The cutting process includes deheading and tail removal, filleting, boning, skinning, trimming, and bone inspection. By incorporating sensors, machine vision, deep learning, and advanced cutting tools, these technologies are transforming fish cutting from a manual to an automated process. This transformation has significant practical implications for the industry, offering improved efficiency, consistent product quality, and enhanced safety, ...