Classification of Different Fermentation Stages of Black Tea Using a Lightweight CNN Optimized by Knowledge Distillation

In red tea production, fermentation is critical for flavor. However, manual determination of its stages is inaccurate and inefficient, often spoiling flavor and lowering product value. To solve this, this study combines CNN and knowledge distillation to build a lightweight classification model, AT-ShuffleNet, for accurate, efficient stage identification in real processing. It collected images of Fuding and Tieguanyin tea at different fermentation stages. ResNet (teacher model) and ShuffleNet v2-0.5 (student model) were used for distillation. Focal and Poly Losses optimized both models to tap distillation potential. STD, MGD, SPKD, ATD, and KD methods were tested at various ratios to find the optimal strategy, forming AT-ShuffleNet. The lightweight model performed well: P (89.11%), R (90.16%), Kappa (89.29%), ACC (91.2%), F1 (89.53%). It addresses manual limitations, enabling accurate classification and reducing deployment issues in unstructured environments. For industrial validation, it was deployed on edge devices and integrated into a self-developed WeChat mini-program.