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

Plant Phenomics 2023年第4期5卷 790-804页

作者： Jan Stejskal Jaroslav Čepl Eva Neuwirthová Olusegun Olaitan Akinyemi Jiří Chuchlík Daniel Provazník Markku Keinänen Petya Campbell jana albrechtová Milan Lstiburek Zuzana Lhotáková Department of Genetics and Physiology of Forest Trees Faculty of Forestry and Wood SciencesCzech University of Life Sciences PraguePragueCzech Republic Department of Environmental and Biological Sciences University of Eastern FinlandJoensuuFinland Department of Experimental Plant Biology Charles UniversityPragueCzech Republic Center for Photonic Sciences University of Eastern FinlandJoensuuFinland Department of Geography and Environmental Sciences University of Maryland Baltimore CountyBaltimoreMDUSA Biospheric Sciences Laboratory NASA Goddard Space Flight CenterGreenbeltMDUSA

Hyperspectral reflectance contains valuable information about leaf functional traits,which can indicate a plant's physiological ***,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 *** 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 ***-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 *** 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 *** latter method was highly susceptible to changes in incoming *** spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral ***,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 *** conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.

关键词： Scots forest reflectance

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