Study on identification method for Artemisia argyi floss

Abstract

Mugwort floss, valued in traditional Chinese medicine, varies in therapeutic properties and market price based on origin and production year. Traditional identification methods, due to their destructiveness and low accuracy, often confuse mugwort floss with A.stolonifera and cause a testing waste. Hyperspectral Imaging, a non-contact technique, offers potential for rapid identification of such medicinal materials. In this paper, we explore hyperspectral data to differentiate mugwort and A.stolonifera using deep learning and neural networks. Using a massive hyperspectral dataset from mugwort and wormwood from two regions across four years, we analyzed performance using metrics like Accuracy, Specificity, and F1 Score. The self-attention-based Backpropagation Neural Network model showed the most promising results for accurate classification. This approach has potential future applications in various fields using Hyperspectral data