中华中医药学刊2026，Vol.44Issue(5)：23-26,后插12-后插15,8.DOI:10.13193/j.issn.1673-7717.2026.05.004
基于深度学习的滇黄精"辨状论质"研究
Evaluation of"Quality Evaluation Through Morphological Identification"of Dianhuangjing(Polygonatum kingianum)Based on Deep Learning
摘要
Abstract
Objective To explore new identification methods and technical means for evaluating the"quality evaluation through morphological identification"of Dianhuangjing(Polygonatum kingianum).Methods The appearance characteristics of fresh and dried samples were collected,and permanent sections were prepared.The Densenet-121 network model was trained to automati-cally identify Dianhuangjing(Polygonatum kingianum)slices.Microscopic features were observed and collected under a micro-scope,and the corresponding image sets were established to train the YOLOv7 model for automatically recognizing and counting the number of vascular bundles and needle crystals in Dianhuangjing(Polygonatum kingianum)microscopic slices.Results The Densenet-121 network model achieved an average recognition rate of 90.97％for Dianhuangjing(Polygonatum kingianum)slices.Compared to manual counting,the YOLOv7 model achieved an average accuracy of 98.14％for identifying vascular bun-dles in Dianhuangjing(Polygonatum kingianum)slices,and an average accuracy of 53.2％for identifying calcium oxalate needle crystals.Conclusion The Densenet-121 network model can achieve relatively high and stable automatic identification between Dianhuangjing(Polygonatum kingianum)and adulterants.The YOLOv7 model can effectively count the number of microvascular bundles and needle crystals in Dianhuangjing(Polygonatum kingianum)slices.Taking Dianhuangjing(Polygonatum kingianum)as an example,a new research approach and technical method for rapid and accurate AI identification of traditional Chinese me-dicinal materials was constructed.关键词
滇黄精/性状特征/Densenet-121/YOLOv7Key words
Dianhuangjing(Polygonatum kingianum)/characteristics of traits/Densenet-121/YOLOv7分类
医药卫生引用本文复制引用
徐翔,徐雅静,刘晓兰,俞捷,李学芳,李静平..基于深度学习的滇黄精"辨状论质"研究[J].中华中医药学刊,2026,44(5):23-26,后插12-后插15,8.基金项目
国家自然科学基金项目(81960740) （81960740）
