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

Lelong C., Roger J.M., Brégand S., Dubertret F., Lanore M., Sitorus N.A., Raharja D.A., Caliman J.P.. 2009. In : IEEE-GRS, Gipsa-Lab, INP-Grenoble. First Workshop on hyperspectral image and signal processing : evolutions in remote sensing, Grenoble, France, 26-28 august 2009. s.l. : s.n., 4 p.. Workshop on hyperspectral image and signal processing : evolutions in remote sensing. 1, 2009-08-26/2009-08-28, Grenoble (France).

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. (Résumé d'auteur)

Mots-clés : elaeis guineensis; ganoderma; sumatra

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