Discrimination of fungal disease infestation in oil-palm canopy hyperspectral reflectance data

Lelong Camille, Roger Jean-Michel, Brégand Simon, Dubertret Fabrice, Lanore Mathieu, Sitorus Nurul Amin, Raharja Doni Artanto, Caliman Jean-Pierre. 2009. Discrimination of fungal disease infestation in oil-palm canopy hyperspectral reflectance data. In : First Workshop on hyperspectral image and signal processing : evolutions in remote sensing, Grenoble, France, 26-28 august 2009. IEEE-GRS, Gipsa-Lab, INP-Grenoble. s.l. : s.n., 4 p. Workshop on hyperspectral image and signal processing : evolutions in remote sensing. 1, Grenoble, France, 26 Août 2009/28 Août 2009.

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Résumé : This study focuses on the calibration of a statistical model of discrimination between different stages of a fungal disease attack on oil palm, based on field hyperspectral measurements at the canopy scale. Combinations of preprocessing, partial least square regression and factorial discriminant analysis are tested on a hundred of samples to prove the efficiency of canopy reflectance to provide 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 levels from the sane to the critically sick tree with a global performance of more than 92%. Applications and further improvements of this experiment are discussed.

Mots-clés Agrovoc : Elaeis guineensis, Ganoderma

Mots-clés géographiques Agrovoc : Sumatra

Classification Agris : H20 - Maladies des plantes
U10 - Informatique, mathématiques et statistiques
U30 - Méthodes de recherche

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