基于LiDAR数据和超光谱图像数据的多指数模糊的综合评估模型,其中包含了基于LiDAR数据和超光谱图像数据的子盐耐受性的信息
Jiaxin Zhang1,2,3, Aiwu Zhang1,2,3, Zixuan Liu1,2,3
1Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing, China.
Frontiers in plant science
|September 4, 2023
概括
这项研究引入了一种新的非破坏性方法,使用LiDAR和高光谱成像来评估的盐分耐受性. 开发的模型准确地识别了对盐敏感和耐盐品种,提高了大规模育种效率.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 遥感技术 遥感技术 遥感技术
背景情况:
- 传统的子盐耐受性评估是耗时和劳动密集的,阻碍了大规模的育种工作.
- 开发快速,非破坏性的方法对于有效选耐盐草生殖质是至关重要的.
研究的目的:
- 提出一种非接触式,非破坏性的多指数模糊综合性评估模型,用于评估的盐分耐受性.
- 整合光检测和测距 (LiDAR) 和高光谱图像 (HSI) 数据,以进行更全面的评估.
主要方法:
- 从LiDAR数据中提取结构特征和从HSI数据中提取光谱特征,以形成计算类型特征 (CPT).
- 开发了基于CPT的多指数模糊综合评估模型与信息 (FCE-E).
- 将FCE-E模型与传统方法和主要组件分析 (PCA) 进行比较.
主要成果:
- 与传统的表型特征相比,FCE-E模型发现了更多的盐敏感信息,显示了66.67%的一致性.
- FCE-E模型的结果与PCA非常一致,证实了其在评估子盐耐受性方面的准确性.
- 通过使用FCE-E方法,成功选了三种耐盐和两种易盐的草品种.
结论:
- 多指数FCE-E模型提供了一种新的,非接触的,非破坏性的方法来评估的盐分耐受性.
- 这种方法提高了培养计划中耐盐生殖质查的效率和准确性.
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