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

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.