CLC number: TP7; S43
On-line Access: 2010-01-01
Received: 2009-07-06
Revision Accepted: 2009-11-16
Crosschecked: 2009-11-19
Cited: 17
Clicked: 7640
Zhan-yu LIU, Jing-jing SHI, Li-wen ZHANG, Jing-feng HUANG. Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification[J]. Journal of Zhejiang University Science B, 2010, 11(1): 71-78.
@article{title="Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification",
author="Zhan-yu LIU, Jing-jing SHI, Li-wen ZHANG, Jing-feng HUANG",
journal="Journal of Zhejiang University Science B",
volume="11",
number="1",
pages="71-78",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B0900193"
}
%0 Journal Article
%T Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification
%A Zhan-yu LIU
%A Jing-jing SHI
%A Li-wen ZHANG
%A Jing-feng HUANG
%J Journal of Zhejiang University SCIENCE B
%V 11
%N 1
%P 71-78
%@ 1673-1581
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0900193
TY - JOUR
T1 - Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification
A1 - Zhan-yu LIU
A1 - Jing-jing SHI
A1 - Li-wen ZHANG
A1 - Jing-feng HUANG
J0 - Journal of Zhejiang University Science B
VL - 11
IS - 1
SP - 71
EP - 78
%@ 1673-1581
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0900193
Abstract: Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stål, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.
[1] Abalos, P., Daffner, J., Pinochet, L., 2000. Evaluation of three Brucella soluble antigens used in an indirect ELISA to discriminate S19 vaccinated from naturally infected cattle. Veterinary Microbiology, 71(1-2):161-167.
[2] Blackburn, G.A., 1998a. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3):273-285.
[3] Blackburn, G.A., 1998b. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. International Journal of Remote Sensing, 19(4):657-675.
[4] Carter, G.A., 1993. Responses of leaf spectral reflectance to plant stress. American Journal of Botany, 80(3):239-243.
[5] Chang, C.C., Lin, C.J., 2001. LIBSVM: A Library for Support Vector Machines. Software available from http://www. csie.ntu.edu.tw/~cjlin/libsvm
[6] Cibula, W.G., Carter, G.A., 1992. Identification of a far-red reflectance response to ectomycorrhizae in slash pine. International Journal of Remote Sensing, 13(5):925-932.
[7] Congalton, R.G., 1991. A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing of Environment, 37(1):35-46.
[8] Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, London, UK, p.93-112.
[9] Demetriades-Shah, T., Steven, M., Clark, J., 1990. High-resolution derivative spectra in remote sensing. Remote Sensing of Environment, 33(1):55-64.
[10] Fung, T., LeDrew, E., 1987. Application of principal component analysis to change detection. Photogrammetric Engineering and Remote Sensing, 53(12):1649-1658.
[11] Holden, H., LeDrew, E., 1998. Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal component analysis, and derivative spectroscopy. Remote Sensing of Environment, 65(2):217-224.
[12] Horler, D.N.H., Dockray, M., Barber, J., 1983. The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2):273-288.
[13] Horst, G.L., Engelke, M.C., Meyers, W., 1984. Assessment of visual evaluation techniques. Agronomy Journal, 76(4):619-622.
[14] Huang, J.F., Apan, A., 2006. Detection of Sclerotinia rots disease on celery using hyperspectal data and partial least squares regression. Journal of Spatial Science, 52(1):131-144.
[15] Ingebritsen, S.E., Lyon, R.J.P., 1985. Principal component analysis of multitemporal image pairs. International Journal of Remote Sensing, 6(5):687-696.
[16] Karimi, Y., Prasher, S.O., Patel, R.M., Kim, S.H., 2006. Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and Electronics in Agriculture, 51(1-2):99-109.
[17] Kitchen, N.R., Sudduth, K.A., Myers, D.B., Drummond, S.T., Hong, S.Y., 2005. Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity. Computers and Electronics in Agriculture, 46(1-3):285-308.
[18] Knipling, E.B., 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1(3):155-159.
[19] Kobayashi, T., Kanda, E., Kitada, K., Ishiguro, K., Torigoe, Y., 2001. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology, 91(3):316-323.
[20] Liu, Z.Y., Huang, J.F., Shi, J.J., Tao, R.X., Zhou, W., Zhang, L.L., 2007. Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression. Journal of Zhejiang University-SCIENCE B, 8(10):738-744.
[21] Liu, Z.Y., Huang, J.F., Tao, R.X., Zhang, H.Z., 2008. Estimating rice brown spot disease severity based on principal component analysis and radial basis function neural network. Spectroscopy and Spectral Analysis, 28(9):2156-2160 (in Chinese).
[22] Malthus, T.J., Madeira, A.C., 1993. High-resolution spectroradiometry: spectral reflectance of field beans leaves infected by Botrytis fabae. Remote Sensing of Environment, 45(1):107-116.
[23] Mirik, M., Michels, G.J.Jr., Kassymzhanova-Mirik, S., Elliott, N.C., Catana, V., Jones, D.B., Bowling, R., 2006. Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera: Aphididae) in winter wheat. Computers and Electronics in Agriculture, 51(1-2):86-98.
[24] Mirik, M., Michels, G.J.Jr., Kassymzhanova-Mirik, S., Elliott, N.C., 2007. Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Computers and Electronics in Agriculture, 57(2):123-134.
[25] Nilsson, H.E., 1995. Remote sensing and image analysis in plant pathology. Annual Review of Phytopathology, 33(1):489-527.
[26] Ou, S.H., 1985. Rice Diseases, 2nd Ed. Commonwealth Mycological Institute, Ferry Lane, Kew, Surrey, UK, p.307-311.
[27] Panda, S.S., Hoogenboom, G., Paz, J., 2009. Distinguishing blueberry bushes from mixed vegetation land use using high resolution satellite imagery and geospatial techniques. Computers and Electronics in Agriculture, 67(1-2):51-58.
[28] Pedigo, L.P., 1995. Closing the gap between IPM theory and practice. Journal of Agricultural Entomology, 12(4):171-181.
[29] Qin, Z., Zhang, M., 2005. Detection of rice sheath blight for in-season disease management using multispectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 7(2):115-128.
[30] Qin, Z., Zhang, M., Christensen, T., Li, W.J., Tang, H., 2003. Remote Sensing Analysis of Rice Disease Stresses for Farm Pest Management Using Wide-Band Airborne Data. The 23rd International Geoscience and Remote Sensing Symposium, Toulouse, France. IEEE, New York, USA, p.2215-2217.
[31] Richardson, M.D., Karcher, D.E., Purcell, L.C., 2001. Quantifying turfgrass cover using digital image analysis. Crop Science, 41(6):1884-1888.
[32] Riedell, W.E., Blackmer, T.M., 1999. Leaf reflectance spectra of cereal aphid-damaged wheat. Crop Science, 39(6):1835-1840.
[33] Rundquist, D., Han, L., Schalles, J., Peake, J., 1996. Remote measurement of algal chlorophyll in surface waters: the case for the first derivative of reflectance near 690 nm. Photogrammetric Engineering and Remote Sensing, 62(2):195-200.
[34] Shi, J.J., Liu, Z.Y., Zhang, L.L., Zhou, W., Huang, J.F., 2009. Hyperspectral recognition of rice damaged by rice leaf roller based on support vector machine. Rice Science, 23(3):331-334 (in Chinese).
[35] Shibayama, M., Takahashi, W., Morinaga, S., Akiyama, T., 1993. Canopy water deficit detection in paddy rice using a high-resolution field spectroradiometer. Remote Sensing of Environment, 45(2):117-126.
[36] Sōgawa, K., 1982. The rice brown planthopper: feeding physiology and host plant interactions. Annual Review of Entomology, 27(1):49-73.
[37] Steddom, K., Bredehoeft, M.W., Khan, M., Rush, C.M., 2005. Comparison of visual and multispectral radiometric disease evaluations of cercospora leaf spot of sugar beet. Plant Disease, 89(2):153-158.
[38] Vigier, B.J., Pattey, E., Strachan, I.B., 2004. Narrowband vegetation indexes and detection of disease damage in soybeans. IEEE Geoscience and Remote Sensing Letters, 1(4):255-259.
[39] Wang, H.W., 1999. Partial Least Squares Regression Method and Applications. National Defense Industry Press, Beijing, China, p.1-274 (in Chinese).
[40] Wang, J.H., Zhao, C.J., Huang, W.J., 2008. Application and Basis of Quantitative Remote Sensing in Agriculture. Science Press, Beijing, China, p.356 (in Chinese).
[41] Wu, S.W., Wang, R.C., Chen, X.B., Shen, Z.Q., Shi, Z., 2002. Effects of rice leaf blast on spectrum reflectance of rice. Journal of Shanghai Jiaotong University (Agricultural Science), 20(1):73-77 (in Chinese).
[42] Yang, Z., Rao, M.N., Elliott, N.C., Kindler, S.D., Popham, T.W., 2009. Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing. Computers and Electronics in Agriculture, 67(1-2):64-70.
[43] Zhang, M., Liu, X., O'Neill, M., 2002. Spectral discrimination of Phytophthora infestans infection on tomatoes based on principal component and cluster analysis. International Journal of Remote Sensing, 23(6):1095-1107.
[44] Zhang, M., Qin, Z., Liu, X., Ustin, S.L., 2003. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation, 4(4):295-310.
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