Making the genotypic variation visible: hyperspectral phenotyping in Scots pine seedlings

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 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 two non-destructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350-2500 nm range for phenotyping on 1788 individual Scots pine seedlings belonging to lowland and upland ecotypes of three 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 percent accuracy in predicting three 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.