Katherine Meacham-Hensold, Peng Fu, Jin Wu, Shawn Serbin, Christopher M Montes, Elizabeth Ainsworth, Kaiyu Guan, Evan Dracup, Taylor Pederson, Steven Driever, and Carl Bernacchi

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 (~2mx2m) in less than one minute. 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 VNIR (400-900nm) hyperspectral camera, we predict maximum carboxylation rate of Rubisco (V_{c,max ,} R² =0.79) maximum electron transport rate in given conditions (J_{1800 ,} R² = 0.59), maximal light saturated photosynthesis (P_{max ,} R² = 0.54), chlorophyll content (Chl, R² = 0.87), the ratio of chlorophyll a to b (Chl a:b, R² = 0.63), carbon content (C, R² = 0.47) and nitrogen content (N, R² = 0.49). Model predictions did not improve when using two cameras spanning 400-1800nm 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.