Use of spatial structure analysis of hyperspectral data cubes for detection of insect‐induced stress in wheat plants

Wheat plants were experimentally infested with wheat stem sawflies, and hyperspectral images (reflectance range from 402.8–838.7 nm) were collected from leaves of infested and non‐infested plants. Mean and variance reflectance per leaf were calculated in five of 213 spectral bands (452, 553, 657, 725, and 760 nm) and compared with vegetation indices (NDVI, SI and PRI), and standard variogram parameters (nugget, sill and range values). Mean reflectance values and their variance values and vegetation indices showed significant effects of sawfly infestation in one dataset but not in another. Based on directional variogram analyses, we showed that: (1) better separation of leaf type and infested/non‐infested wheat plants was seen in variograms in longitudinal direction compared to transverse; (2) mainly spectral bands in the red edge and NIR showed consistent effect of sawfly infestation; (3) range values were not affected significantly by either sawfly infestation or leaf type; and (4) sawfly‐induced stress was most likely to be detected about three weeks after infestation. Variogram analysis is one of the key standards in quantitative spatial ecology, and this study supports further research into its use in remote sensing with particular emphasis on detection of biotic stress.

[1]  H. H. Bennett,et al.  Classification of Hyperspectral Data: A Comparative Study , 2004, Precision Agriculture.

[2]  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 .

[3]  David J. Mulla,et al.  Geostatistical Tools for Modeling and Interpreting Ecological Spatial Dependence , 1992 .

[4]  E. B. Knipling.  Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation , 1970 .

[5]  Moon S. Kim,et al.  Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations , 2004 .

[6]  C. Ainslie.  The Western Grass-Stem Sawfly , 2009 .

[7]  Andrew M. Liebhold,et al.  Testing for correlation in the presence of spatial autocorrelation in insect count data , 1998 .

[8]  D. K. Weaver,et al.  Spatiotemporal distributions of wheat stem sawfly eggs and larvae in dryland wheat fields , 2005, The Canadian Entomologist.

[9]  Yud-Ren Chen,et al.  Systematic approach for using hyperspectral imaging data to develop multispectral imagining systems: Detection of feces on apples , 2006 .

[10]  D. A. Devitt,et al.  Evaluating temporal variability in the spectral reflectance response of annual ryegrass to changes in nitrogen applications and leaching fractions , 2006 .

[11]  M. Ivie.  On the Geographic Origin of the Wheat Stem Sawfly (Hymenoptera: Cephidae): A New Hypothesis of Introduction from Northeastern Asia , 2001 .

[12]  H. Muhammed,et al.  Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat , 2003 .

[13]  W. L. Morrill,et al.  Wheat Stem Sawfly (Hymenoptera: Cephidae): Damage and Detection , 1992 .

[14]  C. Mao,et al.  Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.) , 2003 .

[15]  Stephan J. Maas,et al.  Spider Mite Detection and Canopy Component Mapping in Cotton Using Hyperspectral Imagery and Spectral Mixture Analysis , 2004, Precision Agriculture.

[16]  H. Muhammed,et al.  Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density , 2007, Precision Agriculture.

[17]  J. Blasco,et al.  Comparison of three algorithms in the classification of table olives by means of computer vision , 2004 .

[18]  Max P. Bleiweiss,et al.  Detection of Sitotroga cerealella (Olivier) infestation of Wheat Kernels Using Hyperspectral Reflectance , 2006 .

[19]  R. Lu,et al.  Hyperspectral Scattering for assessing Peach Fruit Firmness , 2006 .

[20]  J. Zadoks.  A decimal code for the growth stages of cereals , 1974 .

[21]  D. J. Schotzko,et al.  Geostatistical description of the spatial distribution of Lygus hesperus (Heteroptera: Miridae) in lentils , 1989 .

[22]  F. Hahn,et al.  AE—Automation and Emerging Technologies: Fungal Spore Detection on Tomatoes using Spectral Fourier Signatures , 2002 .

[23]  F. Hahn.  Multi-spectral prediction of unripe tomatoes , 2002 .

[24]  Moon S. Kim,et al.  Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. , 2004, Applied optics.

[25]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[26]  T. Borregaard,et al.  Crop–weed Discrimination by Line Imaging Spectroscopy , 2000 .

[27]  Chenghai Yang,et al.  Repeatability of hyperspectral imaging systems—quantification and improvement , 2005 .

[28]  Elizabeth Pattey,et al.  Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance , 2002 .

[29]  Andrew M. Liebhold,et al.  Geostatistics and Geographic Information Systems in Applied Insect Ecology , 1993 .

[30]  W. Nelson,et al.  Causes of Variations in Effectiveness of Bracon cephi (Gahan) (Hymenoptera: Braconidae) as a Parasite of the Wheat Stem Sawfly , 1963, The Canadian Entomologist.