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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F23%3A10475602" target="_blank" >RIV/00216208:11310/23:10475602 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=xcX9Sk.JUQ" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=xcX9Sk.JUQ</a>

DOI - Digital Object Identifier

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

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant&apos;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&apos; 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant&apos;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&apos; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10611 - Plant sciences, botany

Projekt

<a href="/cs/project/LTAUSA19113" target="_blank" >LTAUSA19113: Využití genetické variability hyperspektrální odrazivosti ekotypů borovice lesní pro selekci jedinců odolných vůči suchu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Plant Phenomics

ISSN

2097-0374

e-ISSN

2643-6515

Svazek periodika

5

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

CN - Čínská lidová republika

Počet stran výsledku

16

Strana od-do

0111

Kód UT WoS článku

001123111800001

EID výsledku v databázi Scopus

2-s2.0-85180134255