Research on artificial intelligence detection method of few-shot oriented fine-grained black beans of different specifications and their confused and counterfeit products
SHI Jia
KANG Shuai
WANG Zi-jun
ZUO Tian-tian
LIU Tu
LIU Yue-shuai
LUO Xiao
CHENG Xian-long
LIN Yong-qiang
WEI Feng
YU Jian-dong
LU Guang-ming
Abstract：Objective:To establish an artificial intelligence image recognition and data analysis method to efficiently and accurately distinguish different specifications of black beans,confused wild soybeans,and common counterfeit black kidney beans,thus to achieve intelligent upgrading of traditional Chinese medicine trait identification.Methods:Digital image samples of black bean traits were collected to create a dataset.Deep learning methods were used to learn the dataset to obtain the ability to distinguish various types of black beans and their counterfeits.Sample expansion methods and metric learning methods were used to improve model performance and obtain better discriminative ability.Results:The method showed accuracy of 99.8%,recall of 100.0%,mean average precision(mAP)of 99.9%,and mAP@0.5:0.95 of 99.8%on the black bean dataset.The trained model could effectively distinguish various types of black beans and their counterfeits,and visually display their detection results.Conclusion:This study takes black beans and related varieties as an example,providing references for the intelligent upgrading of traditional Chinese medicine trait identification,aiming to improve the efficiency and accuracy of the identification,perfect the quality standard system,empower the digital transformation of the entire industry chain,and provide technical support for regulation and market circulation of traditional Chinese medicine.
Keywords：Sojae Semen Nigrumcharacteristic identificationartificial intelligencecounterfeit goodsdeep learning
Publication Date：2026-02-28
Online Publishing Date：2026-08-14(First online date of this platform, not the publication date of the document)
Pages：9( 387-395 )