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Related Experiment Video

Updated: Jan 7, 2026

Author Spotlight: Improving Beef Cattle Nutrition and Production with a Focus on Feed Efficiency and Meat Quality Traits Through Advanced Biochemical and Molecular Assays
07:46

Author Spotlight: Improving Beef Cattle Nutrition and Production with a Focus on Feed Efficiency and Meat Quality Traits Through Advanced Biochemical and Molecular Assays

Published on: July 12, 2024

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Research on an Intelligent Grading Method for Beef Freshness in Complex Backgrounds Based on the DEVA-ConvNeXt Model.

Xiuling Yu1, Yifu Xu1, Chenxiao Qu1

  • 1College of Information and Technology, Jilin Agricultural University, Changchun 130118, China.

Foods (Basel, Switzerland)
|December 30, 2025
PubMed
Summary

A new DEVA-ConvNeXt model improves beef freshness grading using advanced image processing. This method enhances accuracy and speed, making it suitable for real-world applications and equipment design.

Keywords:
background replacementbeef quality evaluationdynamic non-local attention mechanismenhanced deep convolutional modulefeature extraction enhancement

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Food Science

Background:

  • Beef freshness grading faces challenges with data collection, complex backgrounds, and model accuracy.
  • Existing methods struggle with feature extraction in diverse and cluttered environments.

Purpose of the Study:

  • To introduce a novel DEVA-ConvNeXt model for accurate and efficient beef freshness grading.
  • To address limitations in current beef image analysis techniques.

Main Methods:

  • Developed Alpha-Background Generation Shift (ABG-Shift) for rapid dataset generation with complex backgrounds.
  • Integrated Dynamic Non-Local Coordinate Attention (DNLC) and Enhanced Depthwise Convolution (EDW) modules for superior feature extraction.
  • Utilized Varifocal Loss (VFL) to accelerate learning and improve model convergence.

Main Results:

  • DEVA-ConvNeXt significantly outperformed ResNet101 and ShuffleNet V2.
  • Achieved a 6.2% increase in recognition accuracy (94.8%), 5.4% in precision (94.8%), 5.9% in recall (94.6%), and 6.0% in F1 score (94.7%) compared to the ConvNeXt baseline.
  • Demonstrated feasibility for real-world deployment on embedded devices, balancing accuracy and speed.

Conclusions:

  • The DEVA-ConvNeXt model offers a robust solution for beef freshness grading.
  • The proposed techniques provide valuable technical support for developing advanced grading equipment.