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Evaluation of“Quality Evaluation Through Morphological Identification”of Dianhuangjing (Polygonatum kingianum) Based on Deep Learning
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Xiang XU, Yajing XU, Xiaolan LIU, Jie YU, Xuefang LI, Jingping LI
Chinese Archives of Traditional Chinese Medicine | 2026, 44(5) : 23 - 26
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Chinese Archives of Traditional Chinese Medicine | 2026, 44(5): 23-26
• Digital Traditional Chinese Medicine •
Evaluation of“Quality Evaluation Through Morphological Identification”of Dianhuangjing (Polygonatum kingianum) Based on Deep Learning
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Xiang XU, Yajing XU, Xiaolan LIU, Jie YU, Xuefang LI, Jingping LI
Affiliations
  • School of Pharmaceutical Science, Yunnan University of Chinese Medicine, Kunming 650500, Yunnan, China
Published: 2026-05-10 doi: 10.13193/j.issn.1673-7717.2026.05.004
Outline
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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 automatically identify Dianhuangjing (Polygonatum kingianum) slices. Microscopic features were observed and collected under a microscope, 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 bundles 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 medicinal materials was constructed.

Dianhuangjing (Polygonatum kingianum)  /  characteristics of traits  /  Densenet-121  /  YOLOv7
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. 中华中医药学刊, 2026 , 44 (5) : 23 -26 . DOI: 10.13193/j.issn.1673-7717.2026.05.004
Xiang XU, Yajing XU, Xiaolan LIU, Jie YU, Xuefang LI, Jingping LI. Evaluation of“Quality Evaluation Through Morphological Identification”of Dianhuangjing (Polygonatum kingianum) Based on Deep Learning[J]. Chinese Archives of Traditional Chinese Medicine, 2026 , 44 (5) : 23 -26 . DOI: 10.13193/j.issn.1673-7717.2026.05.004
Year 2026 volume 44 Issue 5
93
16
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doi: 10.13193/j.issn.1673-7717.2026.05.004
  • Online Date:2026-10-14
  • Published:2026-05-10
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    School of Pharmaceutical Science, Yunnan University of Chinese Medicine, Kunming 650500, Yunnan, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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