通过深度学习和树的几何形态识别物种 (Quercus spp. ): 优点和缺点: 优点和缺点:
1School of Ecology and Nature Conservation Beijing Forestry University Beijing China.
Ecology and evolution
|February 15, 2024
概括
几何形态测量和深度学习使用叶子形状准确识别树物种. 深度学习提供了效率,而几何形态学有助于识别混合物种个体,推进植物分类.
科学领域:
- 植物学 植物学
- 计算生物学 计算生物学
- 遗传学 是一个遗传学.
背景情况:
- 叶子形态对于植物物种识别至关重要,但树的复杂变异带来了挑战.
- 由于叶子形状的多样性显著,准确的树物种识别在历史上是很困难的.
- 歧视技术的进步为研究树叶形态提供了新的方法.
研究的目的:
- 为了比较几何形态测量方法 (GMM) 和深度学习的准确性和效率,用于识别两个密切相关的树叶树物种.
- 使用简单序列重复 (nSSR) 基因分析进行初步物种识别 (先验),以指导形态分析.
- 评估这些方法在划分树种类和添加剂中的有用性.
主要方法:
- 使用贝叶斯聚类分析对538棵亚洲树叶树的nSSR数据进行物种识别.
- 使用GMM对538棵树的2328片叶子进行叶子形状变化的分析,重点关注13个地标.
- 使用Xception深度学习架构对2221张叶子图像进行训练和分类.
主要成果:
- 在基因数据的指导下,GMM和深度学习都成功识别了这两种树物种.
- 深度学习显示出卓越的成本效益,特别是在时间方面.
- 转基因基因菌被证明是有效的表征混合物个体的叶形状.
结论:
- 该研究证实了GMM和用于树物种识别的深度学习的高分类准确性.
- 深度学习为植物分类提供了一种节省时间的方法,而GMM则提供了详细的形态洞察.
- 这些综合方法为植物形态分析和分类研究提供了强大的工具.
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