ArticleScientific reports2025
AI-driven classification and precision cutting algorithms using machine vision in a customer-operated fish processing system.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A Multi-Objective Optimization Cutting Method for Irregularly Shaped Fish Fillets.Foods (Basel, Switzerland) · 2026Article
- Hand Detection in Hazardous Zones of Frozen Tuna Cutting Machines Based on an Infrared Thermopile Sensor.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning-Enhanced UV Fluorescence for Automated Detection of Foreign Bodies in Tilapia Fillets.Foods (Basel, Switzerland) · 2026Article
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5 authors.
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Abstract
Despite the high nutritional value of fish, it is often under-consumed due to its characteristic odor and laborious cleaning process. This sensory barrier significantly diminishes the appeal of fish, particularly in regions or cultures where individual exhibit heightened sensitivity to fish odor. Fish processing systems have been developed to facilitate cutting and cleaning steps in aquatic supply centers and factories. In this study, to upgrade a fish processing system to an intelligent machine, four high-consumption fish classes were classified using Artificial Intelligence (AI), and the corresponding cutting point determination algorithms were developed using a multipurpose backlighted pure blue background for each class. As the classification algorithms developed, the best results were selected based on the least total MSE value. The best ANN structure was determined as 6-23-4 with 99.62%, 96.72%, and 95.06% with corresponding MSE values of 9.51 × 10
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