Sensors 2010, 10(1), 734-747; doi:10.3390/s100100734
Article

Evaluation of Oil-Palm Fungal Disease Infestation with Canopy Hyperspectral Reflectance Data

1 CIRAD, UMR TETIS, 500 Rue J.-F. Breton, 34093 Montpellier Cedex 5, France 2 Cemagref, UMR ITAP, 361 Rue J.-F. Breton, 34196 Montpellier Cedex 5, France 3 P.T. SMART, SMARTRI, Padang Halaban, North Sumatra, Indonesia 4 P.T. SMART, SMARTRI, Pekanbaru, Riau, Sumatra, Indonesia 5 CIRAD, UR34 & P.T. SMART, SMARTRI, Pekanbaru, Riau, Sumatra, Indonesia
* Author to whom correspondence should be addressed.
Received: 18 November 2009; in revised form: 28 December 2009 / Accepted: 18 January 2010 / Published: 20 January 2010
(This article belongs to the Section Remote Sensors)
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Abstract: Fungal disease detection in perennial crops is a major issue in estate management and production. However, nowadays such diagnostics are long and difficult when only made from visual symptom observation, and very expensive and damaging when based on root or stem tissue chemical analysis. As an alternative, we propose in this study to evaluate the potential of hyperspectral reflectance data to help detecting the disease efficiently without destruction of tissues. This study focuses on the calibration of a statistical model of discrimination between several stages of Ganoderma attack on oil palm trees, based on field hyperspectral measurements at tree scale. Field protocol and measurements are first described. Then, combinations of pre-processing, partial least square regression and linear discriminant analysis are tested on about hundred samples to prove the efficiency of canopy reflectance in providing information about the plant sanitary status. A robust algorithm is thus derived, allowing classifying oil-palm in a 4-level typology, based on disease severity from healthy to critically sick stages, with a global performance close to 94%. Moreover, this model discriminates sick from healthy trees with a confidence level of almost 98%. Applications and further improvements of this experiment are finally discussed.
Keywords: hyperspectral reflectance; spectroscopy; partial least square; classification; Ganoderma; oil palm

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MDPI and ACS Style

Lelong, C.C.D.; Roger, J.-M.; Brégand, S.; Dubertret, F.; Lanore, M.; Sitorus, N.A.; Raharjo, D.A.; Caliman, J.-P. Evaluation of Oil-Palm Fungal Disease Infestation with Canopy Hyperspectral Reflectance Data. Sensors 2010, 10, 734-747.

AMA Style

Lelong CCD, Roger J-M, Brégand S, Dubertret F, Lanore M, Sitorus NA, Raharjo DA, Caliman J-P. Evaluation of Oil-Palm Fungal Disease Infestation with Canopy Hyperspectral Reflectance Data. Sensors. 2010; 10(1):734-747.

Chicago/Turabian Style

Lelong, Camille C. D.; Roger, Jean-Michel; Brégand, Simon; Dubertret, Fabrice; Lanore, Mathieu; Sitorus, Nurul A.; Raharjo, Doni A.; Caliman, Jean-Pierre. 2010. "Evaluation of Oil-Palm Fungal Disease Infestation with Canopy Hyperspectral Reflectance Data." Sensors 10, no. 1: 734-747.

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