ontacts, Shalini Krishnamoorthi ( kshalini@tll.org.sg ) and Dr. Daisuke Urano ( daisuke@tll.org.sg ). Materials availability No new experimental materials were utilized in this protocol. Data and code availability The code and sample hyperspectral images used in this protocol are available in Supplementary Information, GitHub ( https://github.com/dr-daisuke-urano/Plant-Hyperspectral ), and Figshare ( https://figshare.com/s/612dd829187a318b7744 ). The repository corresponding to the version at the time of publication has been archived on Zenodo ( https://doi.org/10.5281/zenodo.15354496 ). Acknowledgments This study was supported by the Agency for Science, Technology and Research (A∗STAR), Sin
Open resource ↗Plant-Hyperspectral · dr-daisuke-urano/Plant-Hyperspectral · lines:398-433Unverified paper record
Hyperspectral reflectance imaging and spectral component analysis techniques to reveal distinct color patterns on plant leaves.
STAR protocols · 29 May 2025 · 10.1016/j.xpro.2025.103854
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. 1 .
Plant phenotyping relevance
葉の色・スペクトル形質を抽出するハイパースペクトル画像取得、補正、成分分析の手順を扱うプロトコルであり、植物フェノタイピング手法が中心です。
abstractThis 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.
abstractWe outline how to identify key spectral components and project hyperspectral cubes onto them to highlight specific spectral traits.
Code and data availability
The protocol's authors publicly release their analysis code (Python script, Jupyter notebook, conda environment) and sample hyperspectral images of ornamental plants via GitHub, Figshare, and a Zenodo-archived repository version. These are paper-specific phenotyping assets (hyperspectral leaf images and spectral unmix/
and sample hyperspectral images used in this protocol are available in Supplementary Information, GitHub ( https://github.com/dr-daisuke-urano/Plant-Hyperspectral ), and Figshare ( https://figshare.com/s/612dd829187a318b7744 ). The repository corresponding to the version at the time of publication has been archived on Zenodo ( https://doi.org/10.5281/zenodo.15354496 ). Acknowledgments This study was supported by the Agency for Science, Technology and Research (A∗STAR), Singapore, under the industry alignment fund pre-positioning program: High Performance Precision Agriculture system (A19E4a0101), and by the Singapore-MIT Alliance for Research & Technology, National Research Foundation: Dis
Open resource ↗Zenodo · 10.5281/zenodo.15354496 · lines:398-433ano ( daisuke@tll.org.sg ). Materials availability No new experimental materials were utilized in this protocol. Data and code availability The code and sample hyperspectral images used in this protocol are available in Supplementary Information, GitHub ( https://github.com/dr-daisuke-urano/Plant-Hyperspectral ), and Figshare ( https://figshare.com/s/612dd829187a318b7744 ). The repository corresponding to the version at the time of publication has been archived on Zenodo ( https://doi.org/10.5281/zenodo.15354496 ). Acknowledgments This study was supported by the Agency for Science, Technology and Research (A∗STAR), Singapore, under the industry alignment fund pre-positioning program: High Pe
Open resource ↗Figshare · lines:398-433This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.