Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression

Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

[1]  Y. Kosugi,et al.  Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery , 2007 .

[2]  Damaris A Mwalo.  Application Of Principal Component Analysis InAgronomy , 1988 .

[3]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

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

[5]  Ellsworth F. LeDrew,et al.  Spectral Discrimination of Healthy and Non-Healthy Corals Based on Cluster Analysis, Principal Components Analysis, and Derivative Spectroscopy , 1998 .

[6]  K. P. Singh,et al.  A remote sensing assessment of pest infestation on sorghum , 2007 .

[7]  Michael T. Manry,et al.  Attributes of neural networks for extracting continuous vegetation variables from optical and radar , 1998 .

[8]  J. C. Price.  How unique are spectral signatures , 1994 .

[9]  A. Thomsen,et al.  Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression , 2002, The Journal of Agricultural Science.

[10]  E. LeDrew,et al.  Application of principal components analysis to change detection , 1987 .

[11]  T. Kobayashi,et al.  Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. , 2001, Phytopathology.

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

[13]  R. Colwell.  Determining the prevalence of certain cereal crop diseases by means of aerial photography , 1956 .

[14]  William D. Philpot,et al.  Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation , 1999 .

[15]  G. H. Brenchley,et al.  Aerial Photography for the Study of Plant Diseases , 1968 .

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

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

[18]  Herwig W. Kressler.  Evaluation of Potential , 2003 .

[19]  Armando Apan,et al.  Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression , 2006 .

[20]  H. R. Jackson.  Microdensitometer Measurements of Sequential Aerial Photographs of Field Beans Infected with Bacterial Blight , 1975 .

[21]  D. H. Card,et al.  Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy , 1988 .

[22]  G. Carter.  Ratios of leaf reflectances in narrow wavebands as indicators of plant stress , 1994 .

[23]  B. Yoder,et al.  Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales , 1995 .

[24]  James H. Everitt,et al.  Using airborne video, global positioning system, and geographical information system technologies for detecting and mapping citrus blackfly infestations , 1994 .

[25]  A. M. Picco,et al.  Pyricularia grisea and Bipolaris oryzae: a preliminary study on the occurrence of airborne spores in a rice field , 2002 .

[26]  Rasmus Fensholt,et al.  Remote Sensing , 2008, Encyclopedia of GIS.

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

[29]  T. Warner,et al.  An Evaluation of the Potential for Fuzzy Classification of Multispect ral Data Using Artificial Neural Networks , 1997 .

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

[31]  D. M. Moss,et al.  Red edge spectral measurements from sugar maple leaves , 1993 .