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

Xiuling Yu, Yifu Xu, Chenxiao Qu, Senyue Guo, Shuo Jiang, Linqiang Chen, Yang Zhou

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In one paragraph

Article in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

7 authors.

Xiuling YuCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.

Yifu XuCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.

Chenxiao QuCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.

Senyue GuoCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.

Shuo JiangCollege of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China.

Linqiang ChenCollege of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.

Yang ZhouCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, China.ORCID 0000-0002-7239-6556

Funding

the Natural Fund Program of Jilin Provincial Science and Technology Department 20230101284JC

6 · The paper itself

Abstract

This paper presents a novel DEVA-ConvNeXt model to address challenges in beef freshness grading, including data collection difficulties, complex backgrounds, and model accuracy issues. The Alpha-Background Generation Shift (ABG-Shift) technology enables rapid generation of beef image datasets with complex backgrounds. By incorporating the Dynamic Non-Local Coordinate Attention (DNLC) and Enhanced Depthwise Convolution (EDW) modules, the model enhances feature extraction in complex environments. Additionally, Varifocal Loss (VFL) accelerates key feature learning, reducing training time and improving convergence speed. Experimental results show that DEVA-ConvNeXt outperforms models like ResNet101 and ShuffleNet V2 in terms of overall performance. Compared to the baseline model ConvNeXt, it achieves significant improvements in recognition Accuracy (94.8%, a 6.2% increase), Precision (94.8%, a 5.4% increase), Recall (94.6%, a 5.9% increase), and F1 score (94.7%, a 6.0% increase). Furthermore, real-world deployment and testing on embedded devices confirm the feasibility of this method in terms of accuracy and speed, providing valuable technical support for beef freshness grading and equipment design.

Indexed as

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

Identifiers

PMID41464885

DOI10.3390/foods14244178

PMCPMC12732159

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.