使用无人机多光谱图像多时间地在斜松中进行现象选择.
Yanjie Li1,2,3,4, Xinyu Yang5, Long Tong6
1State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China.
Frontiers in plant science
|September 6, 2023
概括
基因组选择对树木来说是一个挑战. 这项研究使用无人机的多谱图像和植被指数来预测树木的生长,提供了一种新的无序基因改进方法.
科学领域:
- 林业林业 林业 林业 林业
- 植物遗传学 植物遗传学
- 遥感 遥感 遥感 遥感
背景情况:
- 基因组选择 (GS) 对植物育种有效,但对于具有复杂基因组的长寿树来说是不切实际的.
- 传统的树木育种方法由于长代时间而缓慢.
研究的目的:
- 使用无人机多光谱成像开发树木遗传改进的预测方法.
- 评估使用时间多谱数据和植被指数 (VIs) 来评估斜松的遗传变异的可行性.
- 创建一种独立于测序或血统的表型选择 (PS) 方法.
主要方法:
- 利用时间序列无人机多光谱图像来分析斜松生长特征的遗传变异.
- 开发了一个包含多谱数据加上植被指数 (MV) 的预测模型.
- 评估了单独使用MV方法以及与血统数据相结合的预测能力.
主要成果:
- 时间因素显著影响了树木生长特征,在大多数月份观察到的高遗传相关性.
- 多光谱+VI (MV) 方法显示出有前途且可靠的预测能力,用于选择最佳的斜松家族.
- 7月份实现了最高的预测值 (0.520.56),表明季节性重要性.
结论:
- 无人机多光谱成像为针叶树的表型选择提供了一个可行的,高通量替代方案.
- 这种方法可能会缩短生长时间,并提高树木育种中的遗传细分性,绕过对测序或谱系信息的需求.
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