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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F23%3A97836" target="_blank" >RIV/60460709:41320/23:97836 - isvavai.cz</a>

Result on the web

<a href="https://spj.science.org/doi/10.34133/plantphenomics.0111" target="_blank" >https://spj.science.org/doi/10.34133/plantphenomics.0111</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.34133/plantphenomics.0111" target="_blank" >10.34133/plantphenomics.0111</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

40102 - Forestry

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Plant Phenomics

ISSN

2643-6515

e-ISSN

2643-6515

Volume of the periodical

5

Issue of the periodical within the volume

0111

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

1-15

UT code for WoS article

001123111800001

EID of the result in the Scopus database

2-s2.0-85180134255