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Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning
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  • Published: 30 January 2026

Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning

  • Eva Neuwirthová1,2,
  • Jiří Chuchlík1,
  • Miroslav Pikl3,
  • Zuzana Lhotáková2,
  • Ivan Kashkan4,5,
  • Klára Panzarová4,
  • Jan Stejskal1,
  • Jana Albrechtová2,
  • Milan Lstibůrek1 &
  • …
  • Jaroslav Čepl1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Computational biology and bioinformatics
  • Ecology
  • Plant sciences

Abstract

Segmentation of hyperspectral image data is a well-established technique in remote sensing. While it is commonly applied to individual field crops, its use for individual trees is less prevalent. Conifers are crucial in forestry, and assessing physiological status, or genetic diversity is required for effective early-age treatment in nurseries and hyperspectral imaging (HSI) combined with high-throughput phenotyping (HTP) offers faster and non-destructive evaluation. NDVI-based thresholding is sufficient for detection of leaves with large projection areas, but needles of conifers present challenges due to spatial resolution constraints and increased proportion of border pixels. This study monitored the offspring of three locally adapted Scots pine (Pinus sylvestris L.) populations, representing distinct upland and lowland ecotypes. This study presents a hyperspectral image processing pipeline for segmenting and isolating individual Scots pine seedlings. Using a K-means algorithm, 23 hyperspectral centroids were successfully derived and subsequently classified into ten biologically distinct groups. Random forest classification model effectively differentiated Scots pine seedlings based on origin during water stress and recovery periods. This study highlights the potential of hyperspectral imaging and machine learning in evaluating the physiological state of conifer seedlings, demonstrating promising applications in forest tree physiology research and tree breeding.

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Data availability

Demonstration sample data and their accompanying descriptions are available in the Zenodo repository (https://doi.org/10.5281/zenodo.17167809).The scripts developed for this study are available on GitHub at: https://github.com/JCepl/Pine-hyperspectral-image-segmentaionComplete experimental data and other materials will be made available to third-party academic researchers upon reasonable request.

Abbreviations

DAT:

Days after transplantation

SRWC:

Soil relative water content

HSI:

Hyperspectral imaging

VI:

Vegetation index

PCA:

Principal component analysis

LDA:

Linear discriminant analysis

RF:

Random forest

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Acknowledgements

We thank to Dr. Jan Kaňák, Arboretum Sofronka, Pilsen for providing plant materials and consultation throughout the cultivation process of pine trees. We thank to Jakub Dvořák, Daniel Provazník and Jiří Chuchlík for help with field sampling; Anna Pilská and Monika Pilátová for DNA extraction (all from CULS); and Miroslav Barták (CUNI) for field sampling, sample processing and graphical help. We also appreciate the time and effort that the Associate Editor and both reviewers dedicated to reviewing our manuscript, and we are grateful for the constructive and inspirative feedback provided. We acknowledge that feedback, which greatly contributed to improving the content of our manuscript and enhancing its quality.

Funding

This research was funded mainly by the Ministry of Education, Youth and Sports of the Czech Republic, scheme INTER-EXCELLENCE, INTER-ACTION, grant number LTAUSA19113, titled “Genetic variability of hyper-spectral reflectance in Scots pine ecotypes for selection of drought-resistant individuals.” This work was partially supported by the Ministry of Education, Youth and Sports of the Czech Republic with the European Regional Development Fund-Project “SINGING PLANT” (no. CZ.02.1.01/0.0/0.0/16_026/0008446). The final phase of manuscript writing was supported for JA and ZL by the Národní agentura pro zemědělský výzkum (NAZV) grant no. QL24010275.

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Authors and Affiliations

  1. Department of Forest Genetics and Physiology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Czech Republic

    Eva Neuwirthová, Jiří Chuchlík, Jan Stejskal, Milan Lstibůrek & Jaroslav Čepl

  2. Department of Experimental Plant Biology, Faculty of Science, Charles University, Prague, Czech Republic

    Eva Neuwirthová, Zuzana Lhotáková & Jana Albrechtová

  3. Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic

    Miroslav Pikl

  4. Photon Systems Instruments, (PSI, s.r.o.), Drásov, Czech Republic

    Ivan Kashkan & Klára Panzarová

  5. Laboratory of Hormonal Regulations in Plants, Institute of Experimental Botany, Czech Academy of Sciences, Prague, Czech Republic

    Ivan Kashkan

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  1. Eva Neuwirthová
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  2. Jiří Chuchlík
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Contributions

Conceptualisation ML, JA; Methodology JČ, MP, EN, JCH, IK, KP, JS; Software JČ, MP, EN; Validation JČ, EN; Writing - Original Draft EN, JČ, MP, IK, KP, ZL; Writing – Review & Editing EN, JČ, MP, IK, JCH, ZL, JS, JA, ML.

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Correspondence to Eva Neuwirthová.

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Neuwirthová, E., Chuchlík, J., Pikl, M. et al. Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning. Sci Rep (2026). https://doi.org/10.1038/s41598-025-31952-9

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  • Received: 15 September 2025

  • Accepted: 05 December 2025

  • Published: 30 January 2026

  • DOI: https://doi.org/10.1038/s41598-025-31952-9

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Keywords

  • Conifers
  • Phenotyping
  • Needle segmentation
  • Controlled environment
  • Water stress
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