Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat

Silva-Perez, Viridiana; Molero, Gemma; Serbin, Shawn P.; Condon, Anthony G.; Reynolds, Matthew P.; Furbank, Robert T.; Evans, John R.

doi:10.1093/jxb/erx421

Title: Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat

Journal Article · 22 December 2017 · Journal of Experimental Botany

DOI: https://doi.org/10.1093/jxb/erx421 · OSTI ID:1439796

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^[3]; Condon, Anthony G. ^[1];

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CSIRO Agriculture, Canberra, ACT (Australia); Australian National Univ., Canberra, ACT (Australia). ARC Centre of Excellence for Translational Photosynthesis. Research School of Biology
International Maize and Wheat Improvement Centre (CIMMYT), Mexico City (Mexico)
Brookhaven National Lab. (BNL), Upton, NY (United States). Environmental and Climate Sciences Dept.
Australian National Univ., Canberra, ACT (Australia). ARC Centre of Excellence for Translational Photosynthesis. Research School of Biology

Improving photosynthesis to raise wheat yield potential has emerged as a major target for wheat physiologists. Photosynthesis-related traits, such as nitrogen per unit leaf area (N_area) and leaf dry mass per area (LMA), require laborious, destructive, laboratory-based methods, while physiological traits underpinning photosynthetic capacity, such as maximum Rubisco activity normalized to 25 °C (V_cmax25) and electron transport rate (J), require time-consuming gas exchange measurements. The aim of this study was to assess whether hyperspectral reflectance (350–2500 nm) can be used to rapidly estimate these traits on intact wheat leaves. Predictive models were constructed using gas exchange and hyperspectral reflectance data from 76 genotypes grown in glasshouses with different nitrogen levels and/or in the field under yield potential conditions. Models were developed using half of the observed data with the remainder used for validation, yielding correlation coefficients (R² values) of 0.62 for V_cmax25, 0.7 for J, 0.81 for SPAD, 0.89 for LMA, and 0.93 for N_area, with bias <0.7%. The models were tested on elite lines and landraces that had not been used to create the models. The bias varied between -2.3% and -5.5% while relative error of prediction was similar for SPAD but slightly greater for LMA and N_area.

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Research Organization:: Australian National Univ., Canberra, ACT (Australia); Brookhaven National Lab. (BNL), Upton, NY (United States); CSIRO Agriculture, Canberra, ACT (Australia); International Maize and Wheat Improvement Centre (CIMMYT), Mexico City (Mexico)

Sponsoring Organization:: Australian Research Council (ARC); Grains Research & Development Corporation (Australia); National Council of Science and Technology (CONACYT) (Mexico); Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA) (Mexico); USDOE

Grant/Contract Number:: SC0012704

OSTI ID:: 1439796

Report Number(s):: BNL--114530-2017-JAAM

Journal Information:: Journal of Experimental Botany, Journal Name: Journal of Experimental Botany Journal Issue: 3 Vol. 69; ISSN 0022-0957

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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Seasonal patterns of foliar reflectance in relation to photosynthetic capacity and color index in two co-occurring tree species, Quercus rubra and Betula papyrifera Dillen, Sophie Y.; de Beeck, Maarten Op; Hufkens, Koen Agricultural and Forest Meteorology, Vol. 160 https://doi.org/10.1016/j.agrformet.2012.03.001	journal	July 2012
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Raising yield potential of wheat. II. Increasing photosynthetic capacity and efficiency Parry, M. A. J.; Reynolds, M.; Salvucci, M. E. Journal of Experimental Botany, Vol. 62, Issue 2 https://doi.org/10.1093/jxb/erq304	journal	October 2010
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High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance Yendrek, Craig R.; Tomaz, Tiago; Montes, Christopher M. Plant Physiology, Vol. 173, Issue 1 https://doi.org/10.1104/pp.16.01447	journal	November 2016
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NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions Cabrera-Bosquet, L.; Molero, G.; Stellacci, A. Cereal Research Communications, Vol. 39, Issue 1 https://doi.org/10.1556/CRC.39.2011.1.15	journal	March 2011
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Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration Yao, Xia; Huang, Yu; Shang, Guiyan Remote Sensing, Vol. 7, Issue 11 https://doi.org/10.3390/rs71114939	journal	November 2015
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Spectroscopy can predict key leaf traits associated with source–sink balance and carbon–nitrogen status Ely, Kim S.; Burnett, Angela C.; Lieberman-Cribbin, Wil Journal of Experimental Botany, Vol. 70, Issue 6 https://doi.org/10.1093/jxb/erz061	journal	February 2019
Genetic variation for photosynthetic capacity and efficiency in spring wheat Silva-Pérez, Viridiana; De Faveri, Joanne; Molero, Gemma Journal of Experimental Botany, Vol. 71, Issue 7 https://doi.org/10.1093/jxb/erz439	journal	September 2019
Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops Furbank, Robert T.; Jimenez‐Berni, Jose A.; George‐Jaeggli, Barbara New Phytologist, Vol. 223, Issue 4 https://doi.org/10.1111/nph.15817	journal	April 2019
Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests Wu, Jin; Rogers, Alistair; Albert, Loren P. New Phytologist, Vol. 224, Issue 2 https://doi.org/10.1111/nph.16029	journal	June 2019
From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance Serbin, Shawn P.; Wu, Jin; Ely, Kim S. New Phytologist, Vol. 224, Issue 4 https://doi.org/10.1111/nph.16123	journal	September 2019
Predicting dark respiration rates of wheat leaves from hyperspectral reflectance Coast, Onoriode; Shah, Shahen; Ivakov, Alexander Plant, Cell & Environment, Vol. 42, Issue 7 https://doi.org/10.1111/pce.13544	journal	March 2019
The “one‐point method” for estimating maximum carboxylation capacity of photosynthesis: A cautionary tale Burnett, Angela C.; Davidson, Kenneth J.; Serbin, Shawn P. Plant, Cell & Environment, Vol. 42, Issue 8 https://doi.org/10.1111/pce.13574	journal	June 2019
Hyperspectral imaging combined with machine learning as a tool to obtain high‐throughput plant salt‐stress phenotyping Feng, Xuping; Zhan, Yihua; Wang, Qi The Plant Journal, Vol. 101, Issue 6 https://doi.org/10.1111/tpj.14597	journal	December 2019
Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data Vergara‐Diaz, Omar; Vatter, Thomas; Kefauver, Shawn Carlisle The Plant Journal, Vol. 102, Issue 3 https://doi.org/10.1111/tpj.14636	journal	January 2020
High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel Ge, Yufeng; Atefi, Abbas; Zhang, Huichun Plant Methods, Vol. 15, Issue 1 https://doi.org/10.1186/s13007-019-0450-8	journal	June 2019
Spectral characterization of wheat functional trait responses to Hessian fly: Mechanisms for trait-based resistance Campos-Medina, Veronica A.; Cotrozzi, Lorenzo; Stuart, Jeffrey J. PLOS ONE, Vol. 14, Issue 8 https://doi.org/10.1371/journal.pone.0219431	journal	August 2019
Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms Fu, Peng; Meacham-Hensold, Katherine; Guan, Kaiyu Frontiers in Plant Science, Vol. 10 https://doi.org/10.3389/fpls.2019.00730	journal	June 2019
Advancing Bromegrass Breeding Through Imaging Phenotyping and Genomic Selection: A Review Biswas, Dilip K.; Coulman, Bruce; Biligetu, Bill Frontiers in Plant Science, Vol. 10 https://doi.org/10.3389/fpls.2019.01673	journal	January 2020
Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions Lobos, Gustavo; Escobar-Opazo, Alejandro; Estrada, Félix Remote Sensing, Vol. 11, Issue 3 https://doi.org/10.3390/rs11030329	journal	February 2019
Evaluation of Hyperspectral Reflectance Parameters to Assess the Leaf Water Content in Soybean Kovar, Marek; Brestic, Marian; Sytar, Oksana Water, Vol. 11, Issue 3 https://doi.org/10.3390/w11030443	journal	March 2019
Nondestructive Phenomic Tools for the Prediction of Heat and Drought Tolerance at Anthesis in Brassica Species Chen, Sheng; Guo, Yiming; Sirault, Xavier Plant Phenomics, Vol. 2019 https://doi.org/10.34133/2019/3264872	journal	May 2019
Multiple-nutrient limitation of upland rainfed rice in ferralsols: a greenhouse nutrient-omission trial Raminoarison, Manoa; Razafimbelo, Tantely; Rakotoson, Tovohery Taylor & Francis https://doi.org/10.6084/m9.figshare.10008035	text	January 2019
Multiple-nutrient limitation of upland rainfed rice in ferralsols: a greenhouse nutrient-omission trial Raminoarison, Manoa; Razafimbelo, Tantely; Rakotoson, Tovohery Taylor & Francis https://doi.org/10.6084/m9.figshare.10008035.v1	text	January 2019

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59 BASIC BIOLOGICAL SCIENCES
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