Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices

Leaf diseases, such as powdery mildew and leaf rust, frequently infect barley plants and severely affect the economic value of malting barley. Early detection of barley diseases would facilitate the timely application of fungicides. In a field experiment, we investigated the performance of fluorescence and reflectance indices on (1) detecting barley disease risks when no fungicide is applied and (2) estimating leaf chlorophyll concentration (LCC). Leaf fluorescence and canopy reflectance were weekly measured by a portable fluorescence sensor and spectroradiometer, respectively. Results showed that vegetation indices recorded at canopy level performed well for the early detection of slightly-diseased plants. The combined reflectance index, MCARI/TCARI, yielded the best discrimination between healthy and diseased plants across seven barley varieties. The blue to far-red fluorescence ratio (BFRR_UV) and OSAVI were the best fluorescence and reflectance indices for estimating LCC, respectively, yielding R 2 of 0.72 and 0.79. Partial

[1]  J. LaFountain Inc. , 2013, American Art.

[2]  Hartmut K. Lichtenthaler,et al.  Leaf chlorophyll fluorescence corrected for re-absorption by means of absorption and reflectance measurements , 1998 .

[3]  Pablo J. Zarco-Tejada,et al.  Chlorophyll fluorescence effects on vegetation apparent reflectance: II. laboratory and airborne canopy-level measurements with hyperspectral data. , 2000 .

[4]  John R. Miller,et al.  Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. , 2002, Journal of environmental quality.

[5]  Hong Zheng,et al.  Automatic sorting of Chinese jujube (Zizyphus jujuba Mill. cv. ‘hongxing’) using chlorophyll fluorescence and support vector machine , 2010 .

[6]  Jungho Im,et al.  ISPRS Journal of Photogrammetry and Remote Sensing , 2022 .

[7]  Masahiko Nagai,et al.  Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy , 2012, Remote. Sens..

[8]  Hartmut K. Lichtenthaler,et al.  Light Adaptation and Senescence of the Photosynthetic Apparatus. Changes in Pigment Composition, Chlorophyll Fluorescence Parameters and Photosynthetic Activity , 2004 .

[9]  Hartmut K. Lichtenthaler,et al.  The Chlorophyll Fluorescence Ratio F735/F700 as an Accurate Measure of the Chlorophyll Content in Plants , 1999 .

[10]  S. Durbha,et al.  Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer , 2007 .

[11]  Z. Cerovic,et al.  Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.) , 2005 .

[12]  Roman Rosipal,et al.  Overview and Recent Advances in Partial Least Squares , 2005, SLSFS.

[13]  Gwendal Latouche,et al.  Non-Destructive Optical Monitoring of Grape Maturation by Proximal Sensing , 2010, Sensors.

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

[15]  C. Buschmann Variability and application of the chlorophyll fluorescence emission ratio red/far-red of leaves , 2007, Photosynthesis Research.

[16]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[17]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[18]  S. Wold,et al.  Nonlinear PLS modeling , 1989 .

[19]  G. Noga,et al.  Quantum yield of non-regulated energy dissipation in PSII (Y(NO)) for early detection of leaf rust (Puccinia triticina) infection in susceptible and resistant wheat (Triticum aestivum L.) cultivars , 2010, Precision Agriculture.

[20]  Hartmut K. Lichtenthaler,et al.  Light-induced and Age-dependent Development ofChloroplasts in Etiolated Barley Leaves as Visualized by Determination of Photosynthetic Pigments, C02 Assimilation Rates and Different Kinds of Chlorophyll Fluorescence Ratios , 1996 .

[21]  Nicolas Tremblay,et al.  Sensing crop nitrogen status with fluorescence indicators. A review , 2011, Agronomy for Sustainable Development.

[22]  A-Xing Zhu,et al.  Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[23]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[24]  Ismael Moya,et al.  Dual-excitation FLIDAR for the estimation of epidermal UV absorption in leaves and canopies , 2001 .

[25]  L. Plümer,et al.  Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines , 2011 .

[26]  R. Poppi,et al.  Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. , 2006, Analytica chimica acta.

[27]  I. Filella,et al.  Reflectance assessment of mite effects on apple trees , 1995 .

[28]  Hilko van der Voet,et al.  Comparing the predictive accuracy of models using a simple randomization test , 1994 .

[29]  Clement Atzberger,et al.  Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat , 2010 .

[30]  Nicolas Tremblay,et al.  A first comparison of Multiplex ® for the assessment of corn nitrogen status , 2012 .

[31]  Hartmut K. Lichtenthaler,et al.  Principles and characteristics of multi-colour fluorescence imaging of plants , 1998 .

[32]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[33]  Deyong Sun,et al.  A novel support vector regression model to estimate the phycocyanin concentration in turbid inland waters from hyperspectral reflectance , 2011, Hydrobiologia.

[34]  H. Lichtenthaler Vegetation stress : an introduction to the stress concept in plants , 1996 .

[35]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[36]  Hartmut K. Lichtenthaler,et al.  Fluorescence imaging as a diagnostic tool for plant stress , 1997 .

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

[38]  Giovanni Agati,et al.  Nondestructive evaluation of anthocyanins in olive (Olea europaea) fruits by in situ chlorophyll fluorescence spectroscopy. , 2005, Journal of agricultural and food chemistry.

[39]  Hongjian Lin,et al.  Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy. , 2008, Journal of agricultural and food chemistry.

[40]  Svante Wold,et al.  Personal memories of the early PLS development , 2001 .

[41]  Francine Heisel,et al.  Detection of Nutrient Deficiencies of Maize by Laser Induced Fluorescence Imaging , 1996 .

[42]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[43]  Ben Somers,et al.  Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves , 2009, Remote. Sens..

[44]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

[45]  J. McMurtrey,et al.  Laser-induced fluorescence of green plants. 2: LIF caused by nutrient deficiencies in corn. , 1984, Applied optics.

[46]  L. Buydens,et al.  Comparing support vector machines to PLS for spectral regression applications , 2004 .

[47]  Z. Cerovic,et al.  Fluorescence-based versus reflectance proximal sensing of nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses , 2013 .

[48]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[49]  Hartmut K. Lichtenthaler,et al.  The Role of Chlorophyll Fluorescence in The Detection of Stress Conditions in Plants , 1988 .

[50]  Francine Heisel,et al.  Fluorescence Imaging of Water and Temperature Stress in Plant Leaves , 1996 .

[51]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[52]  P. M. Hansena,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[53]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[54]  M. Sowinska,et al.  Multicolour Fluorescence Imaging of Sugar Beet Leaves with Different Nitrogen Status by Flash Lamp UV-Excitation , 2000, Photosynthetica.

[55]  Pol Coppin,et al.  Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications , 2007 .

[56]  John R. Miller,et al.  Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[57]  Georg Noga,et al.  Use of blue-green and chlorophyll fluorescence measurements for differentiation between nitrogen deficiency and pathogen infection in winter wheat. , 2011, Journal of plant physiology.

[58]  Giovanni Agati,et al.  New portable optical sensors for the assessment of winegrape phenolic maturity based on berry fluorescence , 2008 .

[59]  Georg Bareth,et al.  ASSESSING HYPERSPECTRAL VEGETATION INDICES FOR ESTIMATING LEAF CHLOROPHYLL CONCENTRATION OF SUMMER BARLEY , 2012 .

[60]  H. Lichtenthaler,et al.  Chlorophyll fluorescence imaging of photosynthetic activity with the flash-lamp fluorescence imaging system , 2005, Photosynthetica.

[61]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[62]  S. Delalieux,et al.  Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology , 2009 .

[63]  E. V. Thomas,et al.  Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information , 1988 .

[64]  J. McMurtrey,et al.  Laser-induced fluorescence of green plants. 1: A technique for the remote detection of plant stress and species differentiation. , 1984, Applied optics.

[65]  Georg Noga,et al.  Physiological response of sugar beet (Betavulgaris) genotypes to a temporary water deficit, as evaluated with a multiparameter fluorescence sensor , 2013, Acta Physiologiae Plantarum.