通过基于机器学习的表型化方法揭示了双倍体和多倍体Arabidopsis的季节性色素波动PlantServation
Reiko Akiyama1, Takao Goto2, Toshiaki Tameshige3,4
1Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Nature communications
|September 22, 2023
概括
使用图像分析的新方法PlantServation,可以对植物叶子颜色和素含量进行非破坏性,长期的现场监测. 这种方法揭示了环境因素和遗传学如何影响植物在自然环境中的反应.
科学领域:
- 植物生物学 植物生物学
- 生态生态学 生态生态学
- 生物信息学是一种生物信息学.
背景情况:
- 长期监测叶色素对于了解植物对环境反应至关重要.
- 传统的破坏性方法限制了现场,持续的观察.
- 生态研究需要自动化,非破坏性技术.
研究的目的:
- 开发和验证PlantServation,一种用于自动化,现场叶子颜色分析的新方法.
- 为了研究环境和基因型对Arabidopsis.com中的安托西亚宁含量的影响.
- 评估PlantServation对研究植物在自然环境中的适应性的有用性.
主要方法:
- 开发了PlantServation:强大的图像获取硬件和深度学习软件,用于自动检测植物和提取叶子颜色.
- 在不同的现场条件下处理了超过400万张阿拉比多普西斯个体的图像.
- 分析将叶子颜色衍生的抗生素含量与环境数据 (辐射,温度,降水) 和基因型相关联.
主要成果:
- 植物服务成功提取了叶子颜色和估计的安托素含量>400万图像在不同的现场条件.
- 环境因素,包括过去的辐射,寒冷和降水,显著影响了安托西亚宁水平.
- 合成的多重复合体Arabidopsis kamchatica展示了特征集成,反映了自然的多重复合体反应并支持了进化假设.
结论:
- 植物服务提供了一种实用,非破坏性的方法,用于长期的实地植物颜料监测.
- 这项研究证实了环境变量对抗生素含量的重大影响.
- 这些发现证实了特征遗传和结合在全聚体中的假设,这种假设是由植物服务系统为"自然"研究提供便利.
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