南印度的药用植物:为物种识别提供全面数据集
Muthukumar Arunachalam1, T Gopu1, K Uma2
1Department of Electronics Communication and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Srivilliputhur, Tamil Nadu 626126, India.
Data in brief
|June 18, 2025
概括
这项研究介绍了南印度药用植物数据集 (SIMPD),这是一个用于AI的高质量图像集合. SIMPD有助于自动识别药用植物,支持保护和研究.
科学领域:
- 植物学 植物学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 药用植物的准确识别和分类对于传统医学和人工智能应用至关重要.
- 目前标准化,高质量的数据集的局限性阻碍了自动化物种识别.
- 将民族植物学知识与计算方法相结合,对于进步至关重要.
研究的目的:
- 为了介绍SIMPD版本1,南印度药用植物的精选数据集.
- 通过详细的分类学数据,促进精确的物种识别和生物多样性分析.
- 支持人工智能驱动的植物分类,物体检测和细分方面的研究.
主要方法:
- 在现实条件下获取各种药用植物物种的高分辨率图像.
- 将详细的分类学分类和元数据整合到数据集中.
- 考虑照明,姿势和环境因素的变化,以获得强度.
主要成果:
- 开发SIMPD第1版本,这是人工智能应用程序的全面数据集.
- 数据集旨在增强用于植物识别的机器学习模型的稳定性.
- 促进人工智能驱动的药用植物分类和分析研究.
结论:
- SIMPD版本1解决了在药用植物研究中对标准化数据集的需求.
- 该数据集支持自动物种识别和生物多样性分析方面的进展.
- 这项工作促进了传统知识与现代计算方法的整合,用于保护和研究.
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