Evaluation of oil-palm fungal disease infestation with canopy hyperspectral reflectance data

Sensors

Volumn 10, Issue 1, 2010, Pages 734-747

  Lelong, Camille C D ^a   Roger, Jean Michel ^b   Brégand, Simon ^a   Dubertret, Fabrice ^a   Lanore, Mathieu ^a   Sitorus, Nurul A ^c   Raharjo, Doni A ^d   Caliman, Jean Pierre ^e  

^a UMR TETIS   (France)

^b UMR ITAP   (France)

^c SMARTRI ^*  (Indonesia)

^d PT SMART   (Indonesia)

^e CIRAD   (France)

Author keywords

Classification; Ganoderma; Hyperspectral reflectance; Oil palm; Partial least square; Spectroscopy

Indexed keywords

GANODERMA; HYPERSPECTRAL MEASUREMENTS; HYPERSPECTRAL REFLECTANCE; LINEAR DISCRIMINANT ANALYSIS; OIL PALM; PARTIAL LEAST SQUARE (PLS); PARTIAL LEAST SQUARE REGRESSION; STATISTICAL MODELING;

CLASSIFICATION (OF INFORMATION); DIAGNOSIS; FORESTRY; REFLECTION; SPECTROSCOPY; TISSUE;

PALM OIL;

ARECACEAE; ARTICLE; CLASSIFICATION; GANODERMA; ISOLATION AND PURIFICATION; METHODOLOGY; MICROBIOLOGY; PHOTOMETRY; PLANT DISEASE; SPECTROSCOPY;

ARECACEAE; GANODERMA; PHOTOMETRY; PLANT DISEASES; SPECTRUM ANALYSIS;

EID: 77953657939     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s100100734     Document Type: Article

References (33)

1. Wood, B.J. Opportunities for oil palm R&D in further meeting the challenges of the new dynamics. Planters 2007, 83, 155-177.
2. Breton, F.; Hasan, Y.; Hariadi; Lubis, Z.; de Franqueville, H. Characterization of parameters for the development of an early screening test for basal stem rot tolerance in oil palm progenies. J. Oil Palm Res. 2006, April, 24-36.
3. Ganoderma Diseases of Perennial Crops; Flood, J., Bridge, P.D., Holderness, M., Eds.; CABI Publishing: Wallingford, UK, 2000.
4. Utomo, C.; Nielpold, F. Development of diagnostic methods for detecting Ganoderma-infected oil palms. J. Phytopathol. 2000, 148, 235-247.
5. Bridge, P.D.; O'Grady, E.B.; Pilotti, C.A. Development of molecular diagnostics for the detection of Ganoderma isolates pathogenic to oil palm. In Ganoderma Diseases of Perennial Crops; CABI Publishing: Wallingford, UK, 2000; pp. 225-234.
6. Chaerle, L.; van der Straeten, D. Imaging techniques and the early detection of plant stress. Trends Plant Sci. 2000, 5, 295-500.
7. Larsolle, A.; Muhammed, H.H. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Prec. Agric. 2007, 8, 1385-2256.
8. Liew, O.W.; Chong, P.C.J.; Li, G.; Asundi, A.K. Signature optical clues: emerging technologies for monitoring plant health. Sensors 2008, 8, 3205-3239.
9. Muhammed, H.H. Hyperspectral crop reflectance data for characterizing and estimating Fungal Disease Severity in Wheat. Biosyst. Eng. 2005, 91, 9-20.
10. Wang, W.; Zhang, M.; Zhu, J.; Geng, S. Spectral prediction of phytophtora infestans infection on tomatoes using artificial neural netword (ANN). Int. J. Remote Sens. 2008, 29, 1693-1706.
11. Nilsson, H.E. Remote sensing and image analysis in plant pathology. Ann. Rev. Phytopathol. 1995, 15, 489-527.
12. Apan, A.; Held, A.; Phinn, S.; Markley, J. Detecting sugarcane 'orange rust' disease using EO-1 hyperion hyperspectral imagery. Int. J. Remote Sens. 2004, 25, 489-498.
13. Goodwin, N.; Coops, C.C.; Stone, C. Assessing plantation canopy condition from airborne imagery using spectral mixture analysis and fractional abundances. Int. J. Appl. Earth Observ. Geoinf. 2005, 7, 11-28.
14. Shafri, H.Z.M.; Hamdam, N. Hyperspectral imagery for mapping disease infection in oil palm plantation using vegetation indices and red edge techniques. Am. J. Appl. Sci. 2009, 6, 1031-1035.
15. Shafri, H.Z.M.; Anuar, M.I. Hyperspectral analysis for detecting disease infection in oil palms. In Proceedings of International Conference on Computer and Electrical Engineering, Phuket, Thailand, December 20-22, 2008; pp. 312-316.
16. Jorgensen, R.N.; Christensen, L.K.; Bros, R. Spectral reflectance at sub-leaf scale including the spatial distribution discriminating NPK stress characteristics in barley using multiway partial least square regression. Int. J. Remote Sens. 2007, 28, 943-962.
17. Huang, J.F.; Apan, A. Detection of sclerotinia rot disease on celery using hyperspectral data and partial least squares regression. J. Space Sci. 2006, 51, 129-142.
18. Zhang, M.; Liu, X.; O'Neill, M. Spectral discrimination of Phytophtora infestans infection on tomatoes based on principal component and cluster analyses. Int. J. Remote Sens. 2002, 23, 1095-1107.
19. Fairhurst, T.; Caliman, J.P.; Härdter, R.; Witt, C. Oil Palm: Nutrient Disorders and Nutrient Management; Oil Palm Series; PPI/PPIC-IPI: Singapore, 2005; Vol. 7.
20. Savitsky, A.; Golay, M.J.E. Smoothing and differentiation of data by simplified least-squares procedures. Anal. Chem. 1964, 36, 1627-1639.
21. Tsai, F.; Philpot, W. Derivative analysis of hyperspectral data. Remote Sens. Environ. 1998, 66, 41-51.
22. Estep, L.; Carter, G.A. Derivative analysis of AVIRIS data for crop stress detection. Photogram. Eng. Remote Sens. 2005, 71, 1417-1421.
23. Ruffin, C.; King, R.L.; Younani, N.H. A combined derivative spectroscopy and Savitsky-Golay filtering method for the analysis of hyperspectral data. Geosci. Remote Sens. 2008, 45, 1-15.
24. Browne, M.; Mayer, N.;.Cutmore, T.R.H. A multiscale polynomial filter for adaptive smoothing. Digital Signal Process. 2007, 17, 69-75.
25. Roger, J.M.; Palagos, B.; Guillaume, S.; Bellon-Maurel, V. Discriminating from highly multivariate data by focal eigen function discriminant analysis; application to NIR spectra. Chemometr. Intell. Lab. Syst. 2005, 79, 31-41.
26. Gorretta, N.; Roger, J.M.; Aubert, M.; Bellon-Maurel, V.; Campan, F.; Roumet, P. Determining vitreousness of durum wheat kernels using near infrared hyperspectral imaging. J. Near Infrared Spectrosc. 2006, 14, 231-239.
27. Martens, H.; Naes, T. Multivariate Calibration; Wiley & Sons: Chichester, UK, 1992.
28. Luinge, H.J.; vander Maas, J.H.; Visser, T. Partial least squares regression as a multivariate tool for the interpretation of infrared spectra. Chemiometr. Intell. Lab. Syst. 1995, 28, 129-138.
29. Preda, C.; Saporta, G. PLS regression on a stochastic process. Comput. Stat. Data Anal. 2005, 48, 149-158.
30. Lebart, L.; Morineau, A.; Warwick, K. Multivariate Descriptive Statistical Analysis: Correspondence Analysis and Related Techniques for Large Matrices; Wiley & Sons: New York, NY, USA, 1984.
31. Seasholtz, M.B.; Kowalski, B. The parsimoy principle applied to multivariate calibration. Anal. Chim. Acta 1993, 277, 165-177.
32. Anderssen, E.; Dyrstad, K.; Westad, F.; Martens, H. Reducing over-optimism in variable selection by cross-model validation. Chemiometr. Intell. Lab. Syst. 2006, 84, 69-74.
33. Bro, R.; Kjeldahl, K.; Smilde, A.K.; Kiers, H.A.L. Cross-validation of components models: A critical look at current methods. Anal. Bioanal. Chem. 2008, 390, 1241-1251.