通过使用U-Net细结构识别辅助的高光谱成像来改善Radix glycyrrhizae (甘露) 的地理来源分类
Hui Zhang1, YiXia Pan1, Yuan Chen1
1College of Pharmaceutical Science, Zhejiang University of Technology, No. 18, Chaowang Road, Hangzhou 310014, China. zh889@zjut.edu.cn.
The Analyst
|February 12, 2024
概括
这项研究引入了一个深度学习U-Net模型,用于对甘 (Radix glycyrrhizae) 的高光谱图像进行细分. 与传统方法相比,U-Net模型显著提高了地理来源分类的准确性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 药理学 药理学是指药理学的学科.
背景情况:
- 甘 (Radix glycyrrhizae) 在中国传统医学,食品和化品中至关重要.
- 地理来源对甘露的质量产生重大影响,需要准确的分类.
- 甘原产地分类的现有方法需要改进.
研究的目的:
- 开发一种深度学习技术,用于对甘组织的高光谱图像进行细分.
- 提高Radix glycyrrhizae.地理原产地分类的准确性.
- 将U-Net模型的性能与传统的细分方法进行比较.
主要方法:
- 用U-Net深度学习模型对甘的高光谱图像进行了细分,以识别叶膜,叶膜和骨.
- 该U-Net模型是在标记的组织分区上训练的.
- 机器学习分类器 (LDA,SVM,PLS-DA) 应用于原产地分类的细分数据.
- 与值和圆圈细分方法进行比较.
主要成果:
- 与值方法 (99.17%的培训,91.15%的验证,92.50%的测试) 相比,U-Net细分方法显著提高了原产地分类准确性 (99.06%的培训,94.72%的验证,96.07%的测试).
- 环形方法在分类准确度上显示出有限的改进.
- U-Net有效地利用固有的组织结构进行分类.
结论:
- 基于深度学习的超光谱图像的U-Net细分增强了Radix glycyrrhizae起源分类的准确性.
- 这种技术为传统中医药的智能质量控制提供了巨大的潜力.
- 这项研究强调了在超光谱成像中组织结构识别对于植物起源确定的重要性.
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