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Research on Beef Marbling Grading Algorithm Based on Improved YOLOv8x

文献类型： 外文期刊

作者： Liu, Jun 1 ; Wang, Lian 1 ; Xu, Huafu 2 ; Pi, Jie 3 ; Wang, Daoying 4 ;

作者机构： 1.Nanjing Agr Univ, Coll Artificial Intelligence, Nanjing 210031, Peoples R China

2.Guangxi Zhuang Autonomous Reg Informat Ctr, Guangxi Key Lab Digital Infrastruct, Nanning 530201, Peoples R China

3.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing 210014, Peoples R China

4.Jiangsu Acad Agr Sci, Inst Agr Prod Proc, Nanjing 210014, Peoples R China

关键词： beef marbling; deep learning; YOLOv8x; ROI algorithm; machine vision; grading

期刊名称：FOODS （ 影响因子：5.1； 五年影响因子：5.6 ）

ISSN：

年卷期： 2025 年 14 卷 10 期

页码：

收录情况： SCI

摘要： Marbling is a crucial indicator that significantly impacts beef quality grading. Currently, Chinese beef processing enterprises rely on professional graders who visually assess marbling using national standard atlases. However, this manual evaluation method is highly subjective and time consuming. This study proposes a beef marbling grading algorithm based on an enhanced YOLOv8x model to address these challenges. The model integrates a convolutional neural network (CNN) augmented with an improved attention mechanism and loss function, along with a Region-of-Interest (ROI) preprocessing algorithm to automate the marbling grading process. A dataset comprising 1300 beef sample images was collected and split into training and test sets at an 8:2 ratio. Comparative experiments were conducted with other deep learning models as well as ablation tests to validate the proposed model's effectiveness. The experimental results demonstrate that the improved YOLOv8x achieves a validation accuracy of 99.93%, a practical grading accuracy of 97.82%, and a detection time of less than 0.5 s per image. The proposed algorithm enhances grading efficiency and contributes to intelligent agricultural practices and livestock product quality assessment.

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