Intelligent identification method of origin for Alismatis Rhizoma based on image and machine learning

TL;DR

This study develops a fast and accurate method using images and machine learning to identify the origin of Alismatis Rhizoma, a natural medicine.

Contribution

The novel contribution is combining image-based features with a Random Forest model to achieve high accuracy in identifying AR origins.

Findings

The S + T-RF model achieved 99.17% accuracy in identifying two species of AR.

The same model achieved 96.67% accuracy in identifying four geographic origins of AR.

Image processing with machine learning offers a quick and effective solution for AR origin identification.

Abstract

Alismatis Rhizoma (AR) is widely utilized as a natural medicine across many Asian countries. However, in China, due to its complex origins, AR quality varies, which can affect clinical efficacy. Therefore, there is a need for a method that is both fast and objective to determine the source of AR. In this study, a total of 400 samples of two species and four geographic origins from AR were imaged and processed. From these images, 17 features were extracted, including three shape (S), two color (C), and 12 texture features (T), resulting in a total of 6800 data points. Four commonly used classification models Random Forest (RF), Extreme Learning Machine (ELM), Back Propagation (BP) neural network, and Support Vector Machines (SVM) were tested to find the optimal combination of AR fusion features and classification models. The S + T-RF combinations achieved the best results, with 99.17% accuracy in two species identification and 96.67% accuracy in four geographic origin identification on test sets. These results suggest that image processing combined with the RF model can quickly and effectively identify the complex origins of AR and can provide a reference for the origins identification of other natural medicines.

The online version contains supplementary material available at 10.1038/s41598-025-98458-2.

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