Ginseng Quality Identification Based on Multi-Scale Feature Extraction and Knowledge Distillation

Abstract

As demand for the precious medicinal herb ginseng continues to grow, its importance is becoming ever more prominent. Traditional manual methods are inefficient and inconsistent. Thus, improving the accuracy and efficiency of ginseng quality testing is the central objective of this study. We collected ginseng samples and expanded the dataset through augmentation, which added noise, varied lighting, and surface defects such as red rust and insect damage, to reflect real-world conditions. Because ginseng has intricate textures, irregular shapes, and unstable lighting, we built LLT-YOLO on the YOLOv11 framework, adding a DCA module, depth-wise separable convolutions, an efficient multi-scale attention mechanism, and knowledge distillation to boost accuracy on small devices. Tests showed a precision of 90.5%, a recall of 92.3%, an mAP50 of 95.1%, and an mAP50–95 of 77.4%, gains of 3%, 2.2%, 7.8%, and 0.5% over YOLOv11 with fewer parameters and smaller size, confirming LLT-YOLO as a practical tool for appearance-based ginseng grading that can be extended to other crops. The results indicate that LLT-YOLO offers a practical tool for appearance-based ginseng quality assessment and can be extended to other crops in future work.