Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

doi:10.1016/S1671-2927(00)8679

Journal of Integrative Agriculture ›› 2012, Vol. 12 ›› Issue (9): 1474-1484.DOI: 10.1016/S1671-2927(00)8679

Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

ZHANG Jing-cheng, YUAN Lin, WANG Ji-hua, HUANG Wen-jiang, CHEN Li-ping, ZHANG Dong-yan

1.Beijing Research Center for Information Technology in Agriculture, Beijing 100097, R.P.China
2.Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, R.P.China
3.Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, R.P.China

收稿日期:2011-07-11 出版日期:2012-09-01 发布日期:2012-09-09
通讯作者: Correspondence WANG Ji-hua, Tel: +86-10-51503488, Fax:+86-10-51503750, E-mail: wangjh@nercita.org.cn
作者简介:ZHANG Jing-cheng, Tel: +86-10-51503750, Fax: +86-10-51503750, E-mail: zjc19840222@gmail.com
基金资助:
This work was subsidized by the National Natural Science Foundation of China (41101395, 41071276, 31071324), the Beijing Municipal Natural Science Foundation, China (4122032), and the National Basic Research Program of China (2011CB311806).

Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

ZHANG Jing-cheng, YUAN Lin, WANG Ji-hua, HUANG Wen-jiang, CHEN Li-ping, ZHANG Dong-yan

1.Beijing Research Center for Information Technology in Agriculture, Beijing 100097, R.P.China
2.Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, R.P.China
3.Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, R.P.China

Received:2011-07-11 Online:2012-09-01 Published:2012-09-09
Contact: Correspondence WANG Ji-hua, Tel: +86-10-51503488, Fax:+86-10-51503750, E-mail: wangjh@nercita.org.cn
About author:ZHANG Jing-cheng, Tel: +86-10-51503750, Fax: +86-10-51503750, E-mail: zjc19840222@gmail.com
Supported by:
This work was subsidized by the National Natural Science Foundation of China (41101395, 41071276, 31071324), the Beijing Municipal Natural Science Foundation, China (4122032), and the National Basic Research Program of China (2011CB311806).

摘要/Abstract

摘要： Powdery mildew (Blumeria graminis) is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis (CWA) was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features (SFs) were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression (MLR) analysis and the partial least square regression (PLSR) analysis were adopted as training methods in model development. The spectral characteristics of the powdery mildew on leaf level were found to be closely related with the spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed (R2=0.77, relative root mean square error RRMSE=0.28) the model composed by 14 optimal conventional SFs (R2=0.69, RRMSE=0.32) in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.

关键词: powdery mildew, disease severity, continuous wavelet analysis, partial least square regression

Abstract: Powdery mildew (Blumeria graminis) is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis (CWA) was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features (SFs) were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression (MLR) analysis and the partial least square regression (PLSR) analysis were adopted as training methods in model development. The spectral characteristics of the powdery mildew on leaf level were found to be closely related with the spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed (R2=0.77, relative root mean square error RRMSE=0.28) the model composed by 14 optimal conventional SFs (R2=0.69, RRMSE=0.32) in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.

Key words: powdery mildew, disease severity, continuous wavelet analysis, partial least square regression

ZHANG Jing-cheng, YUAN Lin, WANG Ji-hua, HUANG Wen-jiang, CHEN Li-ping, ZHANG Dong-yan. Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis[J]. Journal of Integrative Agriculture, 2012, 12(9): 1474-1484.

参考文献

[1]Baret F, Vanderbilt V C, Steven M D, Jacquemoud S. 1994. Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties. Remote Sensing of Environment, 48, 253-260.

[2]Blackburn G A. 2007. Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation. International Journal of Remote Sensing, 28, 2831-2855.

[3]Blackburn G A, Ferwerda J G. 2008. Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sensing of Environment, 112, 1614-1632.

[4]Bravo C, Moshou D, West J, McCartney A, Ramon H. 2003. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering, 84, 137-145.

[5]Broge N H, Leblanc E. 2001. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156-172.

[6]Bruce L M, Li J. 2001. Wavelets for computationally efficient hyperspectral derivative analysis. IEEE Transactions on Geoscience and Remote Sensing, 39, 1540-1546.

[7]Cao X R, Zhou Y L, Duan X Y, Cheng D F. 2009. Estimation of the effects of powdery mildew on wheat yield and protein content using hyperspectral remote sensing. Acta Phytophylacica Sinica, 36, 32-36. (in Chinese)

[8]Cheng T, Rivard B, S¨¢nchez-Azofeifa G A, Feng J, Calvo-Polanco M. 2010. Continuous wavelet analysis for the detection of green attack due to mountain pine beetle infestation. Remote Sensing of Environment, 114, 899-910.

[9]Cheng T, Rivard B, S¨¢nchez-Azofeifa G A. 2011. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment, 115, 659-670.

[10]Christou P, Twyman R M. 2004. The potential of genetically enhanced plants to address food insecurity. Nutrition Research Reviews, 17, 23-42.

[11]Curran P J. 1989. Remote sensing of foliar chemistry. Remote Sensing of Environment, 30, 271-278.

[12]Datt B. 1998. remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves. Remote Sensing of Environment, 66, 111-121.

[13]Daughtry C S, Walthall C L, Kim M S, de Colstoun E B, McMurtrey J E. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229-239.

[14]Devadas R, Lamb D W, Simpfendorfer S, Backhouse D. 2009. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture, 10, 459-470.

[15]Faber N M, Rajk¨® R. 2007. How to avoid over-fitting in multivariate calibration-The conventional validation approach and an alternative. Analytica Chimica Acta, 595, 98-106.

[16]Filella I, Serrano L, Serra J, Penuelas J. 1995. Evaluating wheat nitrogen status with canopyreflectance indices and discriminant analysis. Crop Science, 35, 1400-1405.

[17]Franke J, Menz G. 2007. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture, 8, 161-172.

[18]Gamon J A, Penuelas J, Field C B. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41, 35-44.

[19]Gong P, Pu R, Heald R C. 2002. Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia. International Journal of Remote Sensing, 23, 1827-1850.

[20]Graeff S, Link J, Claupein W. 2006. Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici)in wheat (Triticumaestivum L.) bymeans of leaf reflectance measurements. Central European Journal of Biology, 1, 275-288.

[21]Haboudane D, Miller J R, Pattery E, Zarco-Tejad P J, Strachan I B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing Environment, 90, 337-352.

[22]Huang W J, David W L, Niu Z, Zhang Y J, Liu L Y, Wang J H. 2007. Identification of yellow rust in wheat using insitu spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 8, 187-197.

[23]Kim M S, Daughtry C S T, Chappelle E W, McMurtrey J E. 1994. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (APAR). In: Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing. Val d¡¯Isere, France. pp. 299-306.

[24]Li B, Morris J, Martin E B. 2002. Model selection for partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 64, 79-89.

[25]Li X Y, Liu G S, Yang Y F, Zhao C H, Yu Q W, Song S X. 2007. Relationship between hyperspectral parameters and physiological and biochemical indexes of flue-cured tobacco leaves. Agricultural Sciences in China, 6, 316-321.

[26]Lorenzen B, Jensen A. 1989. Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sensing of Environment, 27, 201-209.

[27]Luedeling E, Hale A, Zhang M, Bentley W J, Dharmasri L C. 2009. Remote sensing of spider mite damage in California peach orchards. International Journal of Applied Earth Observation and Geoinformation, 11, 244-255.

[28]Merton R, Huntington J. 1999. Early simulation of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. In: Summaries of the 8th JPL Airborne Earth Science Workshop. Pasadena, JPL Publication, CA. pp. 299-307.

[29]Merzlyak M N, Gitelson A A, Chivkunova O B, Rakitin V Y. 1999. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106, 135-141.

[30]Miller J R, Wu J, Boyer M G, Belanger M, Hare E W. 1991. Season patterns in leaf reflectance red edge characteristics. International Journal of Remote Sensing, 12, 1509-1523.

[31]Moshou D, Bravo C, West J, Wahlen S, McCartney A, Ramon H. 2004. Automatic detection of ¡®yellow rust¡¯ in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture, 44, 173-188.

[32]Nofal M A, Haggag W M. 2006. Integrated management of powdery mildew of mango in Egypt. Crop Protection, 25, 480-486.

[33]Olsen M, Rasmussen S, Nischwitz C. 2003. Effect of powdery mildew of pecan shucks on nut weight and quality and relevance to fungicide application. Crop Protection, 22, 679-682.

[34]Penuelas J, Gamon J A, Fredeen A L, Merino J, Field C B. 1994. Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves. Remote Sensing of Environment, 48, 135-146.

[35]Pu R, Foschi L, Gong P. 2004. Spectral feature analysis for assessment of water status and health level in coast live oak (Quercus agrifolia) leaves. International Journal of Remote Sensing, 25, 4267-4286.

[36]Pu R, Ge S, Kelly N M, Gong P. 2003. Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves. International Journal of Remote Sensing, 24, 1799-1810.

[37]Reisen W K. 2010. Landscape epidemiology of vector-borne diseases. Annual Review of Entomology, 55, 461-483.

[38]Rumpf T, Mahlein A K, Steiner U, Oerke E C, Dehne H W, Plümer L. 2010. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74, 91-99.

[39]Sankaran S, Mishra A, Ehsani R, Davis C. 2010. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72, 1-13.

[40]Strange R N, Scott P R. 2005. Plant disease: a threat to global food security. Annual Review of Phytopathology, 40, 83-116.

[41]Thenkabail P S, Smith R B, de Pauw E. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 158-182.

[42]orrence C, Compo G P. 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61-78.

[43]West J S, Bravo C, Oberti R, Lemaire D, Moshou D, McCartney H A. 2003. The potential of optical canopy measurement for targeted control of field crop diseases. Annual Review of Phytopathology, 41, 593-614.

[44]Xiang Y L, Liu G S, Yang Y F, Zhao C H, Yu Q W, Song S X. 2007. Relationship between hyperspectral parameters and physiological and biochemical indexes of flue-cured tobacco leaves. Agricultural Sciences in China, 6, 665-672.

[45]Wang Y Y, Chen Y H, Li J, Huang W J. 2007. Two new red edge indices as indicators for stripe rust disease severity of winter wheat. Journal of Remote Sensing, 11, 875-881. (in Chinese)

[46]Zhang D Y, Liu R Y, Song X Y, Xu X G, Huang W J, Zhu D Z, Wang J H. 2011. A field-based pushbroom imaging spectrometer for estimating chlorophyll content of maize. Spectroscopy and Spectral Analysis, 31, 771-775. (in Chinese)

Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 5

编辑推荐

Metrics

[1]	ZHANG Dong, OUYANG Shu-hong, WANG Li-li, CUI Yu, WU Qiu-hong, LIANG Yong, WANG Zhen-zhong, XIE Jing-zhong, ZHANG De-yun, WANG Yong, CHEN Yong-xing, LIU Zhi-yong. Comparative genetic mapping revealed powdery mildew resistance gene MlWE4 derived from wild emmer is located in same genomic region of Pm36 and Ml3D232 on chromosome 5BL[J]. Journal of Integrative Agriculture, 2015, 14(4): 603-609.
[2]	CAO Xue-ren, YAO Dong-ming, DUAN Xia-yu, LIU Wei, FAN Jie-ru, DING Ke-jian, ZHOU Yi-lin. Effects of Powdery Mildew on 1000-Kernel Weight, Crude Protein Content and Yield of Winter Wheat in Three Consecutive Growing Seasons[J]. Journal of Integrative Agriculture, 2014, 13(7): 1530-1537.
[3]	Pietro D Spanu. Messages from Powdery Mildew DNA: How the Interplay with a Host Moulds Pathogen Genomes[J]. Journal of Integrative Agriculture, 2014, 13(2): 233-236.
[4]	REN Hong-min, FAN Fan, CAO Ke-qiang. Ultrastructural Changes of Sphaerotheca fuliginea (Schlechtend.:Fr.) Pollacci in Cucumber After Treated by Chrysophanol[J]. Journal of Integrative Agriculture, 2012, 12(6): 970-977.
[5]	LI Ning, JIA Shao-feng, WANG Xiu-na, DUAN Xia-yu, ZHOU Yi-lin, WANG Zong-hua , LU Guodong . The Effect of Wheat Mixtures on the Powdery Mildew Disease and Some Yield Components[J]. Journal of Integrative Agriculture, 2012, 12(4): 611-620.