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Summary

This study introduces an automated computer vision system for pork quality assessment. The MobileNetV3_Small model achieved 98.59% accuracy in detecting pork parts and freshness, enhancing meat inspection efficiency.

Keywords:
computer visionconvolutional neural networksfood safetymeat quality detectionpork cuts

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Food Science

Background:

  • Traditional manual pork quality inspection is inefficient and lacks accuracy.
  • Automated methods are needed to improve pork quality assessment.

Purpose of the Study:

  • To develop an automated pork quality detection system using computer vision.
  • To evaluate the efficiency and accuracy of different convolutional neural network (CNN) models for pork quality assessment.

Main Methods:

  • High-resolution cameras captured pork images (hind leg, loin, belly) from Jinfen white pigs.
  • Digital image processing expanded datasets; five CNN models (VGGNet, ResNet, DenseNet, MobileNet, EfficientNet) were used for feature recognition.
  • The MobileNetV3_Small model was deployed using the PYQT5 framework for an end-to-end system.

Main Results:

  • The MobileNetV3_Small model achieved 98.59% accuracy, outperforming other tested CNN architectures.
  • Statistical analysis showed no significant performance difference between MobileNetV3_Small and ResNet101, EfficientNetB0, and EfficientNetB1 (p > 0.05).
  • Other models exhibited statistically significant performance differences (p < 0.05).

Conclusions:

  • The developed system provides an efficient and accurate method for automatic pork quality detection.
  • This technology can significantly improve the reliability of pork quality inspection and support pork safety monitoring systems.