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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1442968
This article is part of the Research Topic Harnessing Machine Learning to Decode Plant-Microbiome Dynamics for Sustainable Agriculture View all 12 articles

A Segmentation-Combination Data Augmentation Strategy and Dual Attention Mechanism for Accurate Chinese Herbal Medicine Microscopic Identification

Provisionally accepted
XIAOYING ZHU XIAOYING ZHU Guangyao Pang Guangyao Pang *Xi He Xi He *Yue Chen Yue Chen *Zhenming Yu Zhenming Yu *
  • Wuzhou University, Wuzhou, China

The final, formatted version of the article will be published soon.

    Chinese Herbal Medicine (CHM), with its deep-rooted history and increasing global recognition, faces significant obstacles in widespread application due to the limitations inherent in traditional microscopic identification methods. The challenges in automating Chinese herbal medicine microscopic identification are multifaceted, encompassing the scarcity of publicly accessible datasets, imbalanced class distributions, small and unevenly distributed features, and the frequent occurrence of incomplete or blurred cell structures in microscopic images. To overcome these obstacles, this paper introduces a novel deep learning-based approach for automated Chinese Herbal Medicine Microscopic Identification (CHMMI). Our CHMMI employs a segmentation-combination data augmentation strategy to expand and balance the dataset, capturing comprehensive feature sets. A shallow-deep dual attention module enhances the model's focus on relevant features across different layers, enabling effective processing of small, uneven, incomplete, and blurred features. Multi-scale inference integrates features at different scales to improve object detection and identification accuracy. The proposed CHMMI approach achieved an Average Precision (AP) of 0.841, a mean Average Precision at IoU=.50 (mAP@.5) of 0.887, a mean Average Precision at IoU from .50 to .95 (mAP@.5:.95) of 0.551, and a Matthews Correlation Coefficient of 0.898, demonstrating superior performance compared to state-of-theart methods like YOLOv5, SSD, Faster R-CNN, and ResNet. These results highlight CHMMI's potential for practical application in automating CHM microscopic identification, addressing the limitations of traditional methods, and supporting the modernization and growth of the CHM industry.

    Keywords: Chinese herbal medicine, deep learning, attention mechanism, cell recognition, Data augmentation

    Received: 03 Jun 2024; Accepted: 04 Nov 2024.

    Copyright: © 2024 ZHU, Pang, He, Chen and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Guangyao Pang, Wuzhou University, Wuzhou, China
    Xi He, Wuzhou University, Wuzhou, China
    Yue Chen, Wuzhou University, Wuzhou, China
    Zhenming Yu, Wuzhou University, Wuzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.