Characterization of brown planthopper damage on rice crops through hyperspectral remote sensing under field conditions
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- 作者：N. R. Prasannakumar (1)
Subhash Chander (2)
R. N. Sahoo (3) - 关键词：Nilaparvata lugens (St?l.) ; Spectral reflectances ; Wavebands
- 刊名：Phytoparasitica
- 出版年：2014
- 出版时间：July 2014
- 年：2014
- 卷：42
- 期：3
- 页码：387-395
- 全文大小：
- 参考文献：1. Asner, G. P. (1998). Biophysical and biological sources of variability in canopy reflectance. / Remote Sensing of Environment, 64, 234-53. CrossRef
2. Bauer, M. E. (1985). Spectral inputs in crop identification and condition assessment. / Proceedings of the Institute of Electrical and Electronic Engineers, 73, 1071-085. CrossRef
3. Carter, G. A. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. / International Journal of Remote Sensing, 15, 697-03. CrossRef
4. Curran, P. J. (1985). / Principles of remote sensing. London, UK: Longman.
5. Gamon, J. A., Peneulas, J., & Field, C. B. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. / Remote Sensing of Environment, 41, 35-4. CrossRef
6. Gausman, H. W. (1982). Visible light reflectance, transmittance, and absorptance of differently pigmented cotton leaves. / Remote Sensing of Environment, 13, 233-38. CrossRef
7. Gitelson, A. A., & Merzlyak, M. N. (1996). Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. / Journal of Plant Physiology, 148, 494-00. CrossRef
8. Guyot, G., & Baret, F. (1988). Utilisation de la haute résolution spectrale pour suivre l’état des couverts végétaux. In: / Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing, pp. 279-86, ESA SP-287 (Assois, France)
9. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. / Remote Sensing of Environment, 81, 416-26. CrossRef
10. Huete, A. R. (1988). Soil influences in remotely sensed vegetation-canopy spectra. pp. 107-41. In G. Asrar (Ed.), / Theory and applications of optical remote sensing. New York, NY: John Wiley and Sons.
11. Hunt, J., Raymond, E., & Rock, B. W. (1989). Detection in changes in leaf water content using near and mid infrared reflectance. / Remote Sensing of Environment, 30, 45-4.
12. INGER Genetic Resources Center. (1996). / Standard evaluation of system for rice (p. 26). Manila, Philippines: International Rice Research Institute.
13. Inoue, Y., Moran, S. M., & Horie, T. (1998). Analysis of spectral measurements in paddy field for predicting rice growth and yield based on a simple crop simulation model. / Plant Production Science, 1, 269-79. CrossRef
14. Inoue, Y., Pe?uelas, J., Miyata, A., & Mano, M. (2008). Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice. / Remote Sensing of Environment, 112, 156-72. CrossRef
15. Jones, J. D., Jones, J. B., & Lee, W. S. (2010). Diagnosis of bacterial spot in tomato using spectral signatures. / Computers and Electronics in Agriculture, 74, 329-35. CrossRef
16. Khush, G. S. (2005). What it will take to feed 5 billion rice consumers in 2030. / Plant Molecular Biology, 59, 1-. CrossRef
17. Kumar, J., Vashisth, A., Sehgal, V. K., & Gupta, V. K. (2010). Identification of aphid infestation in mustard by hyperspectral remote sensing. / Journal of Agricultural Physics, 10, 53-0.
18. Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). / Remote sensing and image interpretation. New York, NY: John Wiley & Sons Inc.
19. Mass, S. J., & Dunlap, J. R. (1989). Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves. / Agronomy Journal, 81, 105-10. CrossRef
20. Mirik, M., Michels, G. J., Jr., Sabina, K. M., & Elliott, N. C. (2007). Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. / Computers and Electronics in Agriculture, 57, 123-34. CrossRef
21. Mirik, M., Michels, G. J., Jr., Sabina, K. M., Elliott, N. C., & Bowling, R. (2006). Hyperspectral spectrometry as a means to differentiate uninfested and infested winter wheat by greenbug (Hemiptera: Aphididae). / Journal of Economic Entomology, 99, 1682-690. CrossRef
22. Myneni, R. B., & Ross, J. (1991). / Photon–vegetation interactions. Berlin, Germany: Springer-Verlag.
23. Penuelas, J., Gamon, J. A., Griffin, K. L., & Field, C. B. (1993). Assessing community type, plant biomass, pigment composition and photosynthetic efficiency of aquatic vegetation from spectral reflectance. / Remote Sensing of Environment, 46, 110-18. CrossRef
24. Prabhakar, M., Prasad, Y. G., Thirupathi, M., Sreedevi, G., Dharajothi, B., & Venkateswarlu, B. (2011). Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). / Computers and Electronics in Agriculture, 79, 189-98. CrossRef
25. Prasannakumar, N. R., Chander, S., Sahoo, R. N., & Gupta, V. K. (2013). Assessment of brown planthopper, ( / Nilaparvata lugens) [St?l], damage in rice using hyperspectral remote sensing. / International Journal of Pest Management, 59, 180-88. CrossRef
26. Riedell, W. E., & Blackmer, T. M. (1999). Leaf reflectance spectra of cereal aphid-damaged wheat. / Crop Science, 39, 835-840.
27. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. / Remote Sensing of Environment, 55, 95-07. CrossRef
28. Rouse, J. W. J., Haas, H. R., Schell, A. J., & Deering, W. D. (1974). In: Monitoring vegetation systems in the Great Plains with ERTS. pp. 309-317. NASA special publication no. 1.
29. Salisbury, F. B., & Ross, C. (1969). / Plant physiology. Belmont, CA, USA: Wadsworth.
30. 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, 117-26. CrossRef
31. Srivastava, C., Chander, S., Sinha, S. R., & Palta, R. K. (2009). Toxicity of various insecticides against Delhi and Palla populations of brown planthopper ( / Nilaparvata lugens). / Indian Journal of Agricultural Sciences, 79, 1003-006.
32. Yang, C. M., & Chen, R. K. (2004). Modeling rice growth using hyperspectral reflectance data. / Crop Science, 44, 1283-290. CrossRef
33. Yang, C. M., & Cheng, C. H. (2001). Spectral characteristics of rice plants infested by brown planthoppers. / Proceedings of the National Science Council R.O.C., 25,180-86.
34. Yang, C. M., Cheng, C. H., & Chen, R. K. (2007). Changes in spectral characteristics of rice canopy infested with brown plant hopper and leaf folder. / Crop Science, 47, 329-35. CrossRef
35. Yang, C. M., Liu, C., Yi, C., & Wang, W. (2008). Using Formosat-2 satellite data to estimate leaf area index of rice crop. / Journal of Photogrammetry and Remote Sensing, 13, 253-60.
36. Yang, Z., Rao, M. N., Elliott, N. C., Kindler, S. D., & Popham, T. W. (2005). Using ground based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation. / Computers and Electronics in Agriculture, 47, 121-35. CrossRef
37. Zhang, J. H., Wang, K., Bailey, J. S., & Wang, R. C. (2006). Predicting nitrogen status of rice using multispectral data at canopy scale. / Pedosphere, 16, 108-17. CrossRef - 作者单位：N. R. Prasannakumar (1)
Subhash Chander (2)
R. N. Sahoo (3)
1. Division of Entomology, Indian Agricultural Research Institute, Regional Station, Katrain (Kullu Valley), Himachal Pradesh, India, 175 129
2. Division of Entomology, Indian Agricultural Research Institute, New Delhi, India, 110012
3. Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi, India, 110012 - ISSN：1876-7184
文摘
Field experiments were conducted to characterize the brown planthopper (BPH) (Nilaparvata lugens (St?l.) damage stress on rice crops through hyperspectral remote sensing. The BPH-damaged rice crop had higher reflectance in visible (VIS) and lower reflectance in near-infrared regions (NIR) of the electromagnetic spectrum compared with uninfested plants. Mean reflectance of the rice crop varied among different BPH damage levels in various wavebands, with the greatest variation in NIR (740-25?nm). Correlations between plant reflectance and BPH damage depicted four sensitive wavelengths, at 764, 961, 1201 and 1664?nm in relation to BPH stress on the rice crop. Three new brown planthopper spectral indices (BPHI) were formulated by combining two or more of these sensitive wavelengths. Some of the hyperspectral indices reported in the literature were also tested for their suitability to detect BPH stress on rice crops. Based on crop reflectance corresponding to the sensitive wavelengths, a multiple-linear regression model was developed (R2=0.71, RMSE=1.74, P<0.0001) and validated (R2=0.73, RMSE--.71, P<0.0001) that would help to monitor BPH stress on a rice crop and to issue forewarnings to growers.