Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging

Abstract

Photosynthesis is currently measured using time-laborious and/or destructive methods which slows research and breeding efforts to identify crop germplasm with higher photosynthetic capacities. We present a plot-level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunlit leaf pixels collected from a plot (~2 m×2 m) in <1 min. Using field-grown Nicotiana tabacum with genetically altered photosynthetic pathways over two growing seasons (2017 and 2018), we built predictive models for eight photosynthetic parameters and pigment traits. Using partial least squares regression (PLSR) analysis of plot-level sunlit vegetative reflectance pixels from a single visible near infra-red (VNIR) (400-900 nm) hyperspectral camera, we predict maximum carboxylation rate of Rubisco (Vc,max, R2=0.79) maximum electron transport rate in given conditions (J1800, R2=0.59), maximal light-saturated photosynthesis (Pmax, R2=0.54), chlorophyll content (R2=0.87), the Chl a/b ratio (R2=0.63), carbon content (R2=0.47), and nitrogen content (R2=0.49). Model predictions did not improve when using two cameras spanning 400-1800 nm, suggesting a robust, widely applicable and more 'cost-effective' pipeline requiring only a single VNIR camera. The analysis pipeline and methods can be used in any cropping system with modified species-specific PLSR analysis to offer a high-throughput field phenotyping screening for germplasm with improved photosynthetic performance in field trials.

Plant phenotyping relevance

近接ハイパースペクトル画像とPLSRにより、圃場プロットから光合成パラメータや色素形質を高速推定するスクリーニング手法を開発しており、表現型取得・抽出法が研究の中心である。

Code and data availability

The article describes a plot-level hyperspectral phenotyping pipeline and PLSR models, but the supplied blocks contain no public phenotype/trait dataset, hyperspectral images, author analysis code repository, or trained model deposit. The only URLs present are the ORCID of an author, the CC BY license, and a generic提及的

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