用于药用植物物种分类和识别的深度学习:系统性审查
Adibaru Kiflie Mulugeta1, Durga Prasad Sharma2, Abebe Haile Mesfin1
1Department of Computer Science and Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, Ethiopia.
Frontiers in plant science
|January 22, 2024
概括
深度学习有助于识别药用植物,这对生物多样性至关重要. 本综述强调卷积神经网络 (CNN) 和转移学习,但指出需要公共数据集.
科学领域:
- 植物学和计算机科学
- 专注于生物多样性保护和技术应用.
背景情况:
- 药用植物的分类对于保护至关重要,但专家识别具有挑战性.
- 深度学习为识别药用植物物种提供了自动化解决方案.
研究的目的:
- 系统地审查医用植物分类和识别的深度学习研究.
- 确定这一领域的趋势,方法和研究缺口.
主要方法:
- 进行了2018年1月至2022年12月期间发表的研究的系统审查.
- 选了1644项研究,根据PRISMA指南选择了31项进行深入分析.
主要成果:
- 印度领先于研究 (29%),大多数研究使用私人数据集和植物叶子器官 (96.7%).
- 转移学习 (83.8%) 和卷积神经网络 (CNN,64.5%) 是主要的技术.
- 一个重要的研究缺口是缺乏全球可用的,国家特定药用植物的公共数据集.
结论:
- 深度学习,特别是转移学习的CNN,对药用植物的识别有很大的希望.
- 解决公开,多样化的数据集的需求对于推进该领域和确保可信度至关重要.
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