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DOI: 10.1016/j.rse.2019.04.029

High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity

Katherine Meacham-Hensold, Christopher Montes, Jin Wu, Kaiyu Guan, Peng Fu, Elizabeth Ainsworth, Taylor Pederson, Caitlin Moore, Kenny Brown, Christine Raines, and Carl Bernacchi


Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurementsof key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statisticalapproaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the me-chanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may af-fect reflectance spectra causing predictive models to lose power. The objectives of this research were to assessover two separate years, whether a predictive model can represent natural and imposed variation in leaf pho-tosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannualcapabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominantdriver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gasexchange data was used to build predictive models for photosynthetic parameters including maximum car-boxylation rate of Rubisco (Vc,max), maximum electron transport rate (Jmax) and percentage leaf nitrogen ([N]).The model was developed for wild type and genetically modified plants that represent a wide range of photo-synthetic capacities. Results show that hyperspectral reflectance accurately predictedVc,max,Jmaxand [N] for allplants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictiveability relative to gas exchange measurements forVc,max, but not forJmax, and not for genotypes unique to 2017.Building a new model including data collected in 2017 resulted in more robust predictions, with R2increases of17% forVc,max. and 13%Jmax. Plants generally have a positive correlation between leaf nitrogen and photo-synthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lowerVc,max. The PLSR model was able to accurately predict both lowerVc,maxand higher leaf [N] for this genotypesuggesting that the spectral based estimates ofVc,maxand leaf nitrogen [N] are independent. These resultssuggest that the PLSR model can be applied across years, but only to genotypes used to build the model and thatthe actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of theleaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but touse these methods across years and between genotypes at any scale, application of accurately populated physicalbased models based on radiative transfer principles may be required.


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