克服使用机器学习的小型投影区域对高光谱植物图像进行细分的困难
Eva Neuwirthová1,2, Jiří Chuchlík3, Miroslav Pikl4
1Department of Forest Genetics and Physiology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Czech Republic. eva.neuwirthova@natur.cuni.cz.
Scientific reports
|January 30, 2026
概括
超光谱成像和机器学习现在可以对单个针叶树苗进行细分,以更快,非破坏性的生理评估. 这项技术通过评估苗木的健康状况和起源,帮助森林树生理学研究和树木育种.
科学领域:
- 林业林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 植物生理学 植物生理学
背景情况:
- 使用高光谱成像 (HSI) 的单个树木细分比田间作物少.
- 针叶树的评估对林业至关重要,需要非破坏性方法,如HSI和高通量表型 (HTP) 早期治疗.
- 针树的针头由于空间分辨率和边缘像素比例而带来细分挑战,与宽叶不同.
研究的目的:
- 开发一个高光谱图像处理管道,用于细分和隔离单个苏格兰松 (Pinus sylvestris L.) 苗木.
- 用K-means集群将幼苗分为不同的组,并评估其生理状态.
- 使用机器学习,根据其在水应激期间的生态型起源区分苏格兰松树苗.
主要方法:
- 开发了用于苏格兰松树苗细分的高光谱图像处理管道.
- 运用K-means算法推导出23个超光谱中心体,将它们分为十个不同的组.
- 利用随机森林分类模型,根据水应激和恢复的起源区分幼苗.
主要成果:
- 从超光谱数据中成功分段和分离了单个苏格兰松树苗.
- 衍生和分类的超光谱中心体,识别了十个生物学上不同的群体.
- 随机森林模型在水压力和恢复期间准确地区分了苗木的起源.
结论:
- 超光谱成像与机器学习相结合,提供了一种强大的方法来评估针叶树苗的生理状态.
- 这种方法显示出在森林树生理学研究和树木育种计划中应用的巨大潜力.
- 开发的管道能够进行非破坏性,高通量评估,这对于早期针叶树的管理至关重要.
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