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Title: 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 photosyntheticmore » performance in field trials.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [2];  [9]; ORCiD logo [6];  [10]
  1. Department of Plant Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
  2. Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
  3. Environmental & Climate Science Department, Brookhaven National Laboratory, Upton, New York, USA, School of Biological Sciences, University of Hong Kong, Pokfulam, Hong Kong
  4. Environmental & Climate Science Department, Brookhaven National Laboratory, Upton, New York, USA
  5. Department of Plant Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
  6. Department of Plant Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA, USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
  7. Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA, National Center of Supercomputing Applications, University of Illinois at Urbana-Champaign, Champaign, IL, USA
  8. USDA ARS Global Change and Photosynthesis Research Unit, Urbana, IL, USA
  9. Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA, Center for Crop Systems Analysis, Wageningen University, The Netherlands
  10. University of Essex, UK
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); Bill and Melinda Gates Foundation; USDA
OSTI Identifier:
1608496
Alternate Identifier(s):
OSTI ID: 1595697
Report Number(s):
BNL-213589-2020-JAAM
Journal ID: ISSN 0022-0957
Grant/Contract Number:  
SC0012704; OPP1060461
Resource Type:
Published Article
Journal Name:
Journal of Experimental Botany
Additional Journal Information:
Journal Name: Journal of Experimental Botany Journal Volume: 71 Journal Issue: 7; Journal ID: ISSN 0022-0957
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Field phenotyping; food security; hyperspectral imaging; photosynthesis; proximal sensing; spectral reflectance

Citation Formats

Meacham-Hensold, Katherine, Fu, Peng, Wu, Jin, Serbin, Shawn, Montes, Christopher M., Ainsworth, Elizabeth, Guan, Kaiyu, Dracup, Evan, Pederson, Taylor, Driever, Steven, Bernacchi, Carl, and Lawson, ed., Tracy. Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging. United Kingdom: N. p., 2020. Web. doi:10.1093/jxb/eraa068.
Meacham-Hensold, Katherine, Fu, Peng, Wu, Jin, Serbin, Shawn, Montes, Christopher M., Ainsworth, Elizabeth, Guan, Kaiyu, Dracup, Evan, Pederson, Taylor, Driever, Steven, Bernacchi, Carl, & Lawson, ed., Tracy. Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging. United Kingdom. doi:https://doi.org/10.1093/jxb/eraa068
Meacham-Hensold, Katherine, Fu, Peng, Wu, Jin, Serbin, Shawn, Montes, Christopher M., Ainsworth, Elizabeth, Guan, Kaiyu, Dracup, Evan, Pederson, Taylor, Driever, Steven, Bernacchi, Carl, and Lawson, ed., Tracy. Mon . "Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging". United Kingdom. doi:https://doi.org/10.1093/jxb/eraa068.
@article{osti_1608496,
title = {Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging},
author = {Meacham-Hensold, Katherine and Fu, Peng and Wu, Jin and Serbin, Shawn and Montes, Christopher M. and Ainsworth, Elizabeth and Guan, Kaiyu and Dracup, Evan and Pederson, Taylor and Driever, Steven and Bernacchi, Carl and Lawson, ed., Tracy},
abstractNote = {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.},
doi = {10.1093/jxb/eraa068},
journal = {Journal of Experimental Botany},
number = 7,
volume = 71,
place = {United Kingdom},
year = {2020},
month = {2}
}

Journal Article:
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DOI: https://doi.org/10.1093/jxb/eraa068

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