Predicting photosynthetic capacity in tobacco using shortwave infrared spectral reflectance.

Abstract

Plateauing yield and stressful environmental conditions necessitate selecting crops for superior physiological traits with untapped potential to enhance crop performance. Plant productivity is often limited by carbon fixation rates that could be improved by increasing maximum photosynthetic carboxylation capacity (Vcmax). However, Vcmax measurements using gas exchange and biochemical assays are slow and laborious, prohibiting selection in breeding programs. Rapid hyperspectral reflectance measurements show potential for predicting Vcmax using regression models. While several hyperspectral models have been developed, contributions from different spectral regions to predictions of Vcmax have not been clearly identified or linked to biochemical variation contributing to Vcmax. In this study, hyperspectral reflectance data from 350-2500 nm were used to build partial least squares regression models predicting in vivo and in vitro Vcmax. Wild-type and transgenic tobacco plants with antisense reductions in Rubisco content were used to alter Vcmax independent from chlorophyll, carbon, and nitrogen content. Different spectral regions were used to independently build partial least squares regression models and identify key regions linked to Vcmax and other leaf traits. The greatest Vcmax prediction accuracy used a portion of the shortwave infrared region from 2070 nm to 2470 nm, where the inclusion of fewer spectral regions resulted in more accurate models.

Plant phenotyping relevance

短波赤外ハイパースペクトル反射からVcmaxという植物生理形質を予測する回帰モデルを開発・比較しており、形質取得法とモデル精度評価が研究の中心である。

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