Research Article | Open Access

Volume 2023 |Article ID 0111 | https://doi.org/10.34133/plantphenomics.0111

Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings

Jan Stejskal ,¹ Jaroslav Čepl,¹ Eva Neuwirthová,^1,3 Olusegun Olaitan Akinyemi,^1,2 Jiří Chuchlík,¹ Daniel Provazník,¹ Markku Keinänen,^2,4 Petya Campbell,^5,6 Jana Albrechtová,³ Milan Lstibůrek,¹ and Zuzana Lhotáková³

¹Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
²Department of Environmental and Biological Sciences, University of Eastern Finland, Joensuu, Finland
³Department of Experimental Plant Biology, Charles University, Prague, Czech Republic
⁴Center for Photonic Sciences, University of Eastern Finland, Joensuu, Finland
⁵Department of Geography and Environmental Sciences, University of Maryland Baltimore County, Baltimore, MD, USA
⁶Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

Abstract

Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant’s physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings’ hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.