High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel

BackgroundHyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS–NIR–SWIR, 400–2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS–NIR–SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with a VIS–NIR–SWIR spectroradiometer at tasseling. Two multivariate modeling approaches, partial least squares regression (PLSR) and support vector regression (SVR), were employed to estimate the leaf properties from hyperspectral data. Several common vegetation indices (VIs: GNDVI, RENDVI, and NDWI), which were calculated from hyperspectral data, were also assessed to estimate these leaf properties.ResultsSome VIs were able to estimate CHL and N (R2 > 0.68), but failed to estimate the other four leaf properties. Models developed with PLSR and SVR exhibited comparable performance to each other, and provided improved accuracy relative to VI models. CHL were estimated most successfully, with R2 (coefficient of determination) > 0.94 and ratio of performance to deviation (RPD) > 4.0. N was also predicted satisfactorily (R2 > 0.85 and RPD > 2.6). LWC, SLA and K were predicted moderately well, with R2 ranging from 0.54 to 0.70 and RPD from 1.5 to 1.8. The lowest prediction accuracy was for P, with R2 < 0.5 and RPD < 1.4.ConclusionThis study showed that VIS–NIR–SWIR reflectance spectroscopy is a promising tool for low-cost, nondestructive, and high-throughput analysis of a number of leaf physiological and biochemical properties. Full-spectrum based modeling approaches (PLSR and SVR) led to more accurate prediction models compared to VI-based methods. We called for the construction of a leaf VIS–NIR–SWIR spectral library that would greatly benefit the plant phenotyping community for the research of plant leaf traits.

[1]  T. Fearn.  Assessing Calibrations: SEP, RPD, RER and R2 , 2002 .

[2]  A. M. Edwards,et al.  Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time , 2015, Journal of experimental botany.

[3]  J. Dungan,et al.  Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies , 2001 .

[4]  John Doebley,et al.  Maize association population: a high-resolution platform for quantitative trait locus dissection. , 2005, The Plant journal : for cell and molecular biology.

[5]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[6]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[7]  Pierre Roumet,et al.  Assessing leaf nitrogen content and leaf mass per unit area of wheat in the field throughout plant cycle with a portable spectrometer , 2013 .

[8]  Avi C. Knecht,et al.  Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice1[OPEN] , 2015, Plant Physiology.

[9]  G. A. Blackburn,et al.  Hyperspectral remote sensing of plant pigments. , 2006, Journal of experimental botany.

[10]  H. Jones,et al.  Remote Sensing of Vegetation: Principles, Techniques, and Applications , 2010 .

[11]  A. Leakey,et al.  High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance1[OPEN] , 2016, Plant Physiology.

[12]  Yufeng Ge,et al.  Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging , 2016, Comput. Electron. Agric..

[13]  Ulrich Schurr,et al.  Future scenarios for plant phenotyping. , 2013, Annual review of plant biology.

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  Gustavo A. Lobos,et al.  Spectral Knowledge (SK-UTALCA): Software for Exploratory Analysis of High-Resolution Spectral Reflectance Data on Plant Breeding , 2017, Front. Plant Sci..

[16]  D. M. Klaus,et al.  The assessment of leaf water content using leaf reflectance ratios in the visible, near‐, and short‐wave‐infrared , 2008 .

[17]  M. Tester,et al.  Phenomics--technologies to relieve the phenotyping bottleneck. , 2011, Trends in plant science.

[18]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[19]  J. Araus,et al.  Field high-throughput phenotyping: the new crop breeding frontier. , 2014, Trends in plant science.

[20]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[21]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[22]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics, ProbabilityTheory Group (Formerly: E1071), TU Wien , 2015 .

[23]  Ron Wehrens,et al.  The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .

[24]  Yufeng Ge,et al.  High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging , 2017, Front. Plant Sci..

[25]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[26]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[27]  Josep Peñuelas,et al.  Visible and near-infrared reflectance techniques for diagnosing plant physiological status , 1998 .

[28]  Josep Peñuelas,et al.  The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis , 2011 .

[29]  David Heckmann,et al.  Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance Spectra. , 2017, Molecular plant.

[30]  D. Basak,et al.  Support Vector Regression , 2008 .

[31]  Llorenç Cabrera-Bosquet,et al.  NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions , 2011 .

[32]  I. Helland.  Partial Least Squares Regression , 2006 .

[33]  Shawn P Serbin,et al.  Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat , 2017, Journal of experimental botany.

[34]  Anatoly A. Gitelson,et al.  Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[35]  Eyal Ben-Dor,et al.  Application in Analysis of Soils , 2015 .

[36]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[37]  Malia A. Gehan,et al.  A Versatile Phenotyping System and Analytics Platform Reveals Diverse Temporal Responses to Water Availability in Setaria. , 2015, Molecular plant.