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AI-driven classification and precision cutting algorithms using machine vision in a customer-operated fish processing

Hossein Azarmdel1,2,3, Seyed Saeid Mohtasebi4, Ali Jafary5

  • 1Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. h.azarmdel@ut.ac.ir.

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Summary

This study introduces an intelligent fish processing system using Artificial Intelligence (AI) to classify fish and determine precise cutting points. This innovation aims to overcome sensory barriers and boost fish consumption by automating processing.

Keywords:
Backlight illuminated segmentationCutting pointsFish classificationMachine visionSupport vector machine (SVM)

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Area of Science:

  • * Agricultural Engineering
  • * Artificial Intelligence
  • * Food Science

Background:

  • * Fish consumption is limited by odor and laborious processing.
  • * Existing fish processing systems require manual intervention for cutting and cleaning.
  • * Sensory barriers, like fish odor, reduce consumer appeal.

Purpose of the Study:

  • * To develop an intelligent fish processing system by integrating Artificial Intelligence (AI).
  • * To classify high-consumption fish species using AI algorithms.
  • * To create automated cutting point determination algorithms for enhanced fish processing.

Main Methods:

  • * Classification of four high-consumption fish classes using AI.
  • * Development of cutting point determination algorithms utilizing a backlighted blue background.
  • * Evaluation of Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers based on Mean Squared Error (MSE) and accuracy.

Main Results:

  • * ANN model achieved high accuracy (99.62% train, 95.06% test) with an optimal 6-23-4 structure.
  • * SVM classifier demonstrated strong performance with 99.69% train and 98.75% test accuracy.
  • * Accurate head and belly cutting points were achieved for Silver Carp (98.36%, 99.49%), Carp (97.85%, 98.07%), and Trout (96.61%, 97.90%).

Conclusions:

  • * AI-driven classification and automated cutting point determination significantly enhance fish processing.
  • * The intelligent system effectively addresses challenges associated with fish odor and manual processing.
  • * The developed algorithms show high accuracy, paving the way for more efficient fish processing systems.