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高光谱反射成像和光谱成分分析技术用于揭示植物叶片上不同的颜色模式。

Hyperspectral reflectance imaging and spectral component analysis techniques to reveal distinct color patterns on plant leaves.

作者信息

Krishnamoorthi Shalini, Urano Daisuke

机构信息

Temasek Life Sciences Laboratory, Singapore 117604, Singapore.

Temasek Life Sciences Laboratory, Singapore 117604, Singapore; Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore.

出版信息

STAR Protoc. 2025 Jun 20;6(2):103854. doi: 10.1016/j.xpro.2025.103854. Epub 2025 May 29.

DOI:10.1016/j.xpro.2025.103854
PMID:40445837
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12166450/
Abstract

Leaf color patterns in nature, shaped by genetic and environmental factors, can be analyzed using hyperspectral reflectance imaging. This protocol details step-by-step procedures for hyperspectral image acquisition, correction of uneven lighting, and spectral component analysis to reveal distinct and sometimes previously undetectable features on leaves. We outline how to identify key spectral components and project hyperspectral cubes onto them to highlight specific spectral traits. For complete details of this protocol, please refer to Krishnamoorthi et al..

摘要

自然界中由遗传和环境因素塑造的叶片颜色模式，可以使用高光谱反射成像进行分析。本方案详细介绍了高光谱图像采集、不均匀光照校正和光谱成分分析的分步程序，以揭示叶片上独特的、有时是以前无法检测到的特征。我们概述了如何识别关键光谱成分，并将高光谱立方体投影到这些成分上以突出特定光谱特征。有关本方案的完整详细信息，请参考克里希纳穆尔蒂等人的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/0e09690e2dcc/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/db88be3bf296/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/10f93b980a9d/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/6ffe7aecbde4/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/80bf398997a0/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/3a6a71141c9c/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/517fae76324c/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/0e09690e2dcc/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/db88be3bf296/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/10f93b980a9d/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/6ffe7aecbde4/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/80bf398997a0/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/3a6a71141c9c/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/517fae76324c/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8722/12166450/0e09690e2dcc/gr6.jpg

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Cell Rep. 2024 Jul 23;43(7):114463. doi: 10.1016/j.celrep.2024.114463. Epub 2024 Jul 9.
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Assessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery.评估非常高分辨率多光谱航空图像中城市植被无监督分割的替代方法。
PLoS One. 2020 May 7;15(5):e0230856. doi: 10.1371/journal.pone.0230856. eCollection 2020.
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Linking physiological parameters with visible/near-infrared leaf reflectance in the incubation period of vascular wilt disease.
在维管束萎蔫病潜伏期将生理参数与可见/近红外叶片反射率相联系
Saudi J Biol Sci. 2020 Jan;27(1):88-99. doi: 10.1016/j.sjbs.2019.05.007. Epub 2019 May 31.