Evaluation of visible-near infrared reflectance spectra of avocado leaves as a non-destructive sensing tool for detection of laurel wilt

Plant Disease

Volumn 96, Issue 11, 2012, Pages 1683-1689

  Sankaran, Sindhuja ^a   Ehsani, Reza ^a   Inch, Sharon A ^b   Ploetz, Randy C ^b  

^a UNIVERSITY OF FLORIDA   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FUNGI; PERSEA AMERICANA; RAFFAELEA;

EID: 84868147795     PISSN: 01912917     EISSN: None     Source Type: Journal    
DOI: 10.1094/PDIS-01-12-0030-RE     Document Type: Article

References (36)

1. Chen, L., Wang, J., Ye, Z., Zhao, J., Xue, X., Heyden, Y. V., and Sun, Q.2012. Classification of Chinese honeys according to their floral origin by near infrared spectroscopy. Food Chem. Online publication. doi:http:// dx.doi.org/10.1016/j.foodchem.2012.02.156
2. Chen, Q., Cai, J., Wan, X., and Zhao, J. 2011. Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy. LWT Food Sci. Technol. 44:2053-2058.
3. Dixon, S. J., and Brereton, R. G. 2009. Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure. Chemom. Intell. Lab. Syst. 95:1-17.
4. Fraedrich, S. W., Harrington, T. C., Rabaglia, R. J., Mayfield, A. E., Hanula, J. L., Eickwort, J. M., and Miller, D. R. 2008. A fungal symbiont of the redbay ambrosia beetle causes a lethal wilt in redbay and other Lauraceae in the Southeastern United States. Plant Dis.92:215-224.
5. Gamon, J. A., Serrano, L., and Surfus, J. S. 1997. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types and nutrient levels. Oecologia 112:492-501.
6. Gamon, J. A., and Surfus, J. S. 1999. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 143:105-117.
7. Garrett, D., Peterson, D. S., Anderson, C. W., and Thaut, M. H. Comparison of linear, nonlinear, and feature selection methods for ECG signal classification. 2003. IEEE Trans. Neural Syst. Rehabil. Eng. 11:141-144.
8. Gramling, J. M. 2010. Potential effects of laurel wilt on the flora of North America. Southeast Nat. 9:827-836.
9. Huang, J., Liao, H., Zhu, Y., Sun, J., Sun, Q., and Liu, X. 2012. Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis). Compt. Electron. Agric. 82:100-107.
10. Inch, S. A., and Ploetz, R. C. 2011. Impact of laurel wilt, caused by Raffaelea lauricola, on xylem function in avocado, Persea americana. For. Pathol. 42:239-245.
11. Jackson, T. L., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P., and Hunt, E. R. 2004. Vegetation water content mapping using landsat data derived normalized difference water index for corn and soybeans. Remote Sens. Environ. 92:475-482.
12. Jones, C. D., Jones, J. B., and Lee, W. S. 2010. Diagnosis of bacterial spot of tomato using spectral signatures. Compt. Electron. Agric. 74:329-335.
13. Kim, J., Mowat, A., Poole, P., and Kasabov, N. 2000. Linear and non-linear pattern recognition models for classification of fruit from visible-near infrared spectra. Chemom. Intell. Lab. Syst. 51:201-216.
14. Krzanowski, W. J. 1988. Principles of Multivariate Analysis: A User's Perspective. Oxford University Press, New York.
15. Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A., Yang, C., and Mangan, R. L. 2012. Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Compt. Electron. Agric. 83:32-46.
16. Liu, Z. Y., Huang, J. F., and Tao, R. X. 2008. Characterizing and estimating fungal disease severity of rice brown spot with hyperspectral reflectance data. Rice Sci. 15:232-242.
17. Mahlein, A.-K., Steiner, U., Dehne, H.-W., and Oerke, E.-C. 2010. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Prec. Agric. 11:413-431.
18. Mayfield, A. E., Peña, J. E., Crane, J. H., Smith, J. A., Branch, C. L., Ottoson, E. D., and Hughes, M. 2008. Ability of the redbay ambrosia beetle (Coleoptera: Curculionidae: Scolytinae) to bore into young avocado (Lauraceae) plants and transmit the laurel wilt pathogen (Raffaelea sp.). Fla. Entomol. 91:485-487.
19. Müller, K. R., Anderson, C. W., and Birch, G. E. 2003. Linear and nonlinear methods for brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 11:165-169.
20. Ploetz, R. C., Harrington, T., Hulcr, J., Fraedrich, S., Smith, J. A., Inch, S., Kendra, P., Mayfield, A. E., Hanula, J., Rabaglia, R., Palmateer, A., Peña, J., Eskalen, A., Crane, J., Faber, B., Bostock, R., Harmon, C., Schnell, R., and Wingfield, W. 2011. Recovery plan for laurel wilt of avocado (caused by Raffaelea lauricola). National Plant Disease Recovery System. Homeland Security Presidential Directive Number 9 (HSPD-9). http://www.ars.usda. gov/research/docs.htm?docid=14271
21. Ploetz, R. C., Peña, J. E., Smith, J. A., Dreaden, T. J., Crane, J. H., Schubert, T., and Dixon, W. 2011. Laurel wilt is confirmed in Miami-Dade County, center of Florida's commercial avocado production. Plant Dis. 95:1589.
22. Ploetz, R. C., Pérez-Martínez, J. M., Smith, J. A., Hughes, M., Dreaden, T. J., and Fu, Y. 2012. Responses of avocado to laurel wilt, caused by Raffaelea lauricola. Plant Pathol. 61:801-808.
23. Ray, S. S., Jain, N., Miglani, A., Singh, J. P., Singh, A. K., Panigrahy, S., and Parihar, J. S. 2010. Defining optimum spectral narrow bands and bandwidths for agricultural applications. Curr. Sci. 98:1365-1369.
24. Roggo, Y., Chalus, P., Maurer, L., Lema-Martinez, C., Edmond, A., and Jent, N. 2007. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J. Pharmaceut. Biomed. 44:683-700.
25. Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W. 1973. Monitoring vegetation systems in the Great Plains with ERTS. Pages 309-317 in: Third ERTS Symp. NASA SP-351 I.
26. Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., and Plümer, L. 2010. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Compt. Electron. Agric. 74:91-99.
27. Sankaran, S., and Ehsani, R. 2012. Detection of Huanglongbing disease in citrus using fluorescence spectroscopy. Trans. ASABE 55:313-320.
28. Sankaran, S., Mishra, A., Ehsani, R., and Davis, C. 2010. A review of advanced techniques for detecting plant diseases. Compt. Electron. Agric. 72:1-13.
29. Sankaran, S., Mishra, A., Maja, J. M., and Ehsani, R. 2011. Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards. Compt. Electron. Agric. 77:127-134.
30. Sellers, P. J. 1985. Canopy reflectance, photosynthesis and transpiration. International J. Remote Sens. 6:1335-1372.
31. Sims, D. A., and Gamon, J. A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81:337-354.
32. Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127-150.
33. U.S. Dep. Agric. 2010. Distribution map for laurel wilt. http://www.fs. fed.us/r8/foresthealth/laurelwilt/dist_map.shtml
34. Wu, D., Feng, L., Zhang, C., and He, Y. 2008. Early detection of Botrytis cinerea on eggplant leaves based on visible and near-infrared spectroscopy. Trans. ASABE 51:1133-1139.
35. Xu, H. R., Ying, Y. B., Fu, X. P., and Zhu, S. P. 2007. Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosyst. Eng. 96:447-454.
36. Zhang, M., Qin, Z., Liu, X., and Ustin, S. L. 2003. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. Int. J Appl. Earth Obs. Geoinf. 4:295-310.