Detection of powdery mildew in two winter wheat cultivars using canopy hyperspectral reflectance

To determine the most sensitive spectral parameters for powdery mildew detection, hyperspectral canopy reflectance spectra of two winter wheat cultivars with different susceptibilities to powdery mildew were measured at Feekes growth stage (GS) 10, 10.5, 10.5.3, 10.5.4 and 11.1 in 2007–2008 and 2008–2009 seasons. As disease indexes increased, reflectance decreased significantly in near infrared (NIR) regions and it was significantly correlated with disease index at GS 10.5.3, 10.5.4 and 11.1 for both cultivars in both seasons. For the two cultivars, red edge slope (drred), the area of the red edge peak (Σdr680−760 nm), difference vegetation index (DVI) and soil adjusted vegetation index (SAVI) were significantly negatively correlated with disease index at GS 10.5.3, 10.5.4 and 11.1 in both seasons. Compared with other parameters, Σdr680−760 nm was the most sensitive parameter for powdery mildew detection. The regression models based on Σdr680−760 nm were constructed at GS 10.5.3, 10.5.4 and 11.1 in both seasons. These results indicated that canopy hyperspectral reflectance can be used in wheat powdery mildew detection in the absence of other stresses resulting in unhealthy symptoms. Therefore, disease management strategies can be applied when it is necessary based on canopy hyperspectral reflectance data.

[1]  D. Moshou,et al.  The potential of optical canopy measurement for targeted control of field crop diseases. , 2003, Annual review of phytopathology.

[2]  B. S. Ausmus,et al.  Reflectance studies of healthy, maize dwarf mosaic virus-infected, and Helminthosporium maydis-infected corn leaves , 1971 .

[3]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[4]  A. Giebel,et al.  Early detection of Fusarium infection in wheat using hyper-spectral imaging , 2011 .

[5]  Chwen-Ming Yang,et al.  Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance , 2010, Precision Agriculture.

[6]  J. M. Prescott,et al.  A scale for appraising the foliar intensity of wheat diseases , 1975 .

[7]  E. L. Sharp.  Monitoring Cereal Rust Development With A Spectral Radiometer , 1985 .

[8]  H. Ramon,et al.  Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self-organising maps , 2006, Precision Agriculture.

[9]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[10]  Clive H. Bock,et al.  Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging , 2010 .

[11]  H. Ramon,et al.  Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks , 2004 .

[12]  Gunter Menz,et al.  Multi-temporal wheat disease detection by multi-spectral remote sensing , 2007, Precision Agriculture.

[13]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[14]  U. Steiner,et al.  Spectral signatures of sugar beet leaves for the detection and differentiation of diseases , 2010, Precision Agriculture.

[15]  S. Ustin,et al.  Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing , 2003 .

[16]  R. D. Jackson,et al.  Sun-angle and canopy-architecture effects on the spectral reflectance of six wheat cultivars , 1985 .

[17]  D. Lamb,et al.  Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves , 2008, Precision Agriculture.

[18]  R. Jackson.  Remote sensing of biotic and abiotic plant stress , 1986 .

[19]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[20]  D. M. Gates,et al.  Spectral Properties of Plants , 1965 .

[21]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[22]  Hervé Nicolas,et al.  Using remote sensing to determine of the date of a fungicide application on winter wheat , 2004 .

[23]  Kiyoshi Ishiguro,et al.  Ratio of rice reflectance for estimating leaf blast severity with a multispectral radiometer , 2003, Journal of General Plant Pathology.

[24]  H. Nilsson.  Remote sensing and image analysis in plant pathology. , 1995, Annual review of phytopathology.

[25]  H. Shafri,et al.  Hyperspectral imagery for mapping disease infection in oil palm plantation using vegetation indices and red edge techniques. , 2009 .

[26]  C. Rush,et al.  Remote detection of rhizomania in sugar beets. , 2003, Phytopathology.

[27]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[28]  B. Lorenzen,et al.  Changes in leaf spectral properties induced in barley by cereal powdery mildew , 1989 .

[29]  Johanna Link,et al.  Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements , 2006, Central European Journal of Biology.

[30]  Ruiliang Pu,et al.  Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements , 2012 .

[31]  Marvin E. Bauer,et al.  Effects of nitrogen fertilizer on growth and reflectance characteristics of winter wheat , 1986 .

[32]  J. J. Colls,et al.  Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks , 2004 .

[33]  H. Ramon,et al.  Early Disease Detection in Wheat Fields using Spectral Reflectance , 2003 .

[34]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[35]  T. Volk,et al.  Control of fungal diseases in winter wheat with appropriate dose rates and weather‐based decision support systems 1 , 1996 .

[36]  M. Wolfe.  Trying to understand and control powdery mildew , 1984 .

[37]  E. C. Large.  GROWTH STAGES IN CEREALS ILLUSTRATION OF THE FEEKES SCALE , 1954 .

[38]  P. J. Pinter,et al.  Remote sensing for crop protection , 1993 .

[39]  C. Rush,et al.  Comparison of Visual and Multispectral Radiometric Disease Evaluations of Cercospora Leaf Spot of Sugar Beet. , 2005, Plant disease.

[40]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[41]  I. M. Scotford,et al.  Applications of Spectral Reflectance Techniques in Northern European Cereal Production: A Review , 2005 .