Impurity detection of premium green tea based on improved lightweight deep learning model

Tea may be mixed with impurities during picking and processing, which can lower their quality. At present, the sorting of impurities in premium green tea mainly relies on manual labor, which is inefficient. In response to the technical challenges in this industry, this article uses deep learning technology to detect impurities in premium green tea. However, in actual production, computing resources are limited by equipment, and large models are not suitable for deployment. On this basis, a lightweight model for detecting impurities in premium green tea is proposed. We conducted experiments on You Only Look Once version 8 (YOLOv8) models on a self-made dataset and selected the basic model and the teacher model. By replacing the loss function and lightweight convolution, model pruning, and knowledge distillation, the model achieves lightweighting while improving detection performance. The experimental results reveal that the Giga floating point operations (GFLOPs), parameters, precision (P), recall (R), mean average precision (mAP), and frames per second (FPS) of the improved model are 4.2, 791966 B, 0.9379, 0.8959, 0.9484, and 1362.7, respectively. Compared with the original model, the P, R, mAP, and FPS are improved by 0.0216, 0.0320, 0.0261, and 368.0, respectively. The GFLOPs and parameters are reduced by 3.9 (48.15%) and 2214462 B (73.66%), respectively. This study provides technical support for the intelligent sorting of impurities in premium green tea.