Discrimination of winter wheat disease and insect stresses using continuous wavelet features extracted from foliar spectral measurements

Discrimination of crop diseases and insect damages is a critical task in pest management. As a non-contact and non-destructive method, spectroscopy has been recognised as an efficient way for crop pest detection. In this study, an advanced spectral analysis method, the continuous wavelet analysis (CWA), was used to discriminate three common diseases and insect damages in wheat crop: yellow rust, powdery mildew and aphid. In this research, leaf spectra were measured in both infected and reference plots at early grain filling stage. An algorithm was developed based on the continuously decomposed wavelet scalogram to identify types and severities of the damages. Its sensitivity and discrimination capability to damages were evaluated. Utilising an overlapping strategy, a wavelet feature selection method was established to identify optimal wavelet features discriminate the damages. Then, the discriminant model was developed based on the Fisher's linear discriminant analysis (FLDA). A total of six wavelet features with a central wavelength varying from 430 to 930 nm and scale factors of 4–8 were identified. According to a k-fold cross-validation, the averaged overall accuracy of the developed discriminant model was 77%. The CWA-based spectral discrimination approach showed good potential to serve as a basis to develop in-field, real-time, multi-damage mapping systems.

[1]  Rong-Kuen Chen,et al.  Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder , 2007 .

[2]  B. Rivard,et al.  Spectroscopic determination of leaf water content using continuous wavelet analysis , 2011 .

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

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

[5]  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 .

[6]  D. Bulanon,et al.  Classification of grapefruit peel diseases using color texture feature analysis , 2009 .

[7]  Lutz Plümer,et al.  A review of advanced machine learning methods for the detection of biotic stress in precision crop protection , 2014, Precision Agriculture.

[8]  Dionysis Bochtis,et al.  Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops , 2011 .

[9]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

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

[11]  Xiang-Dong Liu,et al.  Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis) , 2012 .

[12]  Jingcheng Zhang,et al.  Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis , 2012 .

[13]  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.

[14]  Paul Christou,et al.  The potential of genetically enhanced plants to address food insecurity , 2004, Nutrition Research Reviews.

[15]  M. Peña,et al.  Use of satellite-derived hyperspectral indices to identify stress symptoms in an Austrocedrus chilensis forest infested by the aphid Cinara cupressi , 2009 .

[16]  Benoit Rivard,et al.  Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation , 2010 .

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

[18]  Anne-Katrin Mahlein,et al.  Fusion of sensor data for the detection and differentiation of plant diseases in cucumber , 2014 .

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

[20]  Eike Luedeling,et al.  Remote Sensing of Spider Mite Damage in California Peach Orchards Keywords: Aerial Imagery Integrated Pest Management Partial Least Squares (pls) Regression Prunus Persica Remote Sensing Spectral Reflectance Spectroradiometer , 2022 .

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

[22]  F. M. Danson,et al.  Advances in environmental remote sensing , 1995 .

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

[24]  Yong Luo,et al.  Detection of powdery mildew in two winter wheat cultivars using canopy hyperspectral reflectance , 2013 .

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

[26]  Wenjiang Huang,et al.  Detecting Aphid Density of Winter Wheat Leaf Using Hyperspectral Measurements , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[28]  Benoit Rivard,et al.  Continuous wavelets for the improved use of spectral libraries and hyperspectral data , 2008 .

[29]  Jiang Li,et al.  Correction to "Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals" , 2004 .

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

[31]  E. Oerke.  Crop losses to pests , 2005, The Journal of Agricultural Science.

[32]  Jiang Li,et al.  Automated detection of subpixel hyperspectral targets with adaptive multichannel discrete wavelet transform , 2002, IEEE Trans. Geosci. Remote. Sens..

[33]  Christian Nansen,et al.  Agricultural Case Studies of Classification Accuracy, Spectral Resolution, and Model Over-Fitting , 2013, Applied spectroscopy.

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

[35]  Jingcheng Zhang,et al.  Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects , 2014 .

[36]  L. Plümer,et al.  Development of spectral indices for detecting and identifying plant diseases , 2013 .

[37]  Sinthop Kaewpijit,et al.  Automatic reduction of hyperspectral imagery using wavelet spectral analysis , 2003, IEEE Trans. Geosci. Remote. Sens..

[38]  R. Congalton.  A Quantitative Method to Test for Consistency and Correctness in Photointerpretation , 1983 .

[39]  Minghua Zhang,et al.  Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN) , 2008, International Journal of Remote Sensing.

[40]  Ruiliang Pu,et al.  Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat , 2014 .

[41]  Y. G. Prasad,et al.  Remote Sensing of Biotic Stress in Crop Plants and Its Applications for Pest Management , 2012 .

[42]  R. Congalton,et al.  Accuracy assessment: a user's perspective , 1986 .