Fast and Accurate Meat Freshness Classification Using Depthwise Separable Convolution and SPPF

Ensuring the freshness of meat is vital for food safety, consumer trust, and waste reduction. Traditional chemical and microbiological tests, though reliable, are destructive and time-consuming, limiting their suitability for real-time monitoring. This study introduces DW–SPPFNet, a lightweight deep learning framework designed for rapid, non-destructive classification of meat freshness from RGB images. The model integrates Depthwise Separable Convolution (DW) to minimize redundant computation and Spatial Pyramid Pooling Feature-lite (SPPF-lite) to enhance multi-scale spatial representation. This combination achieves a superior trade-off between accuracy and efficiency, enabling edge-level deployment. Trained and tested on a dataset of 10,372 labeled pork images, DW–SPPFNet achieved 98.31\% test accuracy, 98.32\% macro-F1, and a Cohen’s $\kappa$ of 0.9747, surpassing state-of-the-art lightweight backbones such as MobileNetV4-S and EfficientViT. The model operates at 2.72 ms per image with a minimal computational footprint (0.213 GFLOPs, 1.63M parameters), allowing real-time inference on resource-limited devices.