Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Abstract Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher's linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level.

[1]  N. Elliott,et al.  Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat , 2007 .

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

[3]  Moon S. Kim,et al.  The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par) , 1994 .

[4]  Wilhelm Claupein,et al.  Identification and discrimination of water stress in wheat leaves (Triticum aestivum L.) by means of reflectance measurements , 2007, Irrigation Science.

[5]  F. J. Pierce,et al.  The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars , 2009 .

[6]  Walter E. Riedell,et al.  Leaf Reflectance Spectra of Cereal Aphid-Damaged Wheat , 1999 .

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

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

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

[10]  B. Cooke,et al.  The Epidemiology of Plant Diseases , 1998, Springer Netherlands.

[11]  Moon S. Kim,et al.  Hyperspectral imaging for detection of scab in wheat , 2000, SPIE Optics East.

[12]  Ruiliang Pu,et al.  Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses , 2012 .

[13]  Qihao Weng.  Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications , 2011 .

[14]  Mahesh N. Rao,et al.  Using ground-based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation , 2005 .

[15]  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.

[16]  Ruiliang Pu,et al.  Comparing Canonical Correlation Analysis with Partial Least Squares Regression in Estimating Forest Leaf Area Index with Multitemporal Landsat TM Imagery , 2012 .

[17]  Jan Kuckenberg,et al.  Detection and differentiation of nitrogen-deficiency, powdery mildew and leaf rust at wheat leaf and canopy level by laser-induced chlorophyll fluorescence , 2009 .

[18]  Z. Yanga,et al.  Using ground-based multispectral radiometry to detect stress in wheat caused by greenbug ( Homoptera : Aphididae ) infestation , 2005 .

[19]  Wenjiang Huang,et al.  Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[20]  N. M. Kelly,et al.  Spectral absorption features as indicators of water status in coast live oak ( Quercus agrifolia ) leaves , 2003 .

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

[22]  R. Merton,et al.  MONITORING COMMUNITY HYSTERESIS USING SPECTRAL SHIFT ANALYSIS AND THE RED-EDGE VEGETATION STRESS INDEX , 1998 .

[23]  P. Gong,et al.  Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia , 2002 .

[24]  Z. Niu,et al.  Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging , 2007, Precision Agriculture.

[25]  Wenjiang Huang,et al.  Evaluation of spectral indices and continuous wavelet analysis to quantify aphid infestation in wheat , 2012, Precision Agriculture.

[26]  P. R. Scott,et al.  Plant disease: a threat to global food security. , 2005, Annual review of phytopathology.

[27]  Norman C. Elliott,et al.  Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera: Aphididae) in winter wheat , 2006 .

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

[29]  Theofanis Sapatinas,et al.  Discriminant Analysis and Statistical Pattern Recognition , 2005 .

[30]  R. Pu,et al.  Spectral feature analysis for assessment of water status and health level in coast live oak (Quercus agrifolia) leaves , 2004 .

[31]  Xin Huang,et al.  Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance , 2011 .

[32]  John R. Miller,et al.  Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy , 2005 .

[33]  Bo-Cai Gao,et al.  Normalized difference water index for remote sensing of vegetation liquid water from space , 1995, Defense, Security, and Sensing.

[34]  Won Suk Lee,et al.  Original paper: Diagnosis of bacterial spot of tomato using spectral signatures , 2010 .

[35]  Reza Ehsani,et al.  Review: A review of advanced techniques for detecting plant diseases , 2010 .