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Related Experiment Video

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Porkolor: A deep learning framework for pork color classification.

Yuxian Pang1, Chuchu Chen1, Yuedong Yang1

  • 1Sun Yat-sen University, No. 132 Waihuandong Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.

Meat Science
|December 18, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning model for objective pork color assessment, improving food safety and quality evaluation. The model achieves high accuracy using standardized data and advanced image processing techniques.

Keywords:
Patch-based trainingPork colorResNetSegment anything model

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Area of Science:

  • Food Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Pork color is vital for assessing safety and freshness, but human evaluation is subjective and inefficient.
  • Existing computer vision and deep learning methods lack standardized data and robust preprocessing, limiting performance.
  • Background noise in images negatively impacts the accuracy of automated pork color analysis.

Purpose of the Study:

  • To develop a standardized method and dataset for pork image collection.
  • To propose a novel deep learning model for accurate pork color prediction.
  • To enhance the objectivity and reliability of pork quality assessment.

Main Methods:

  • Designed a standardized pork image collection device and curated a dataset of 1707 images.
  • Developed a two-module deep learning framework: image preprocessing and color classification.
  • Utilized the Segment Anything Model (SAM) for background noise removal and ResNet-101 for classification with patch-based training.

Main Results:

  • Achieved 91.50% classification accuracy on the custom dataset.
  • Attained 89.00% accuracy on an external validation dataset.
  • Demonstrated improved model stability and accuracy through effective image preprocessing.

Conclusions:

  • The proposed deep learning model offers a reliable and objective method for pork color analysis.
  • Standardized data collection and advanced preprocessing significantly enhance model performance.
  • The Porkolor online application provides a practical tool for real-world application.