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

Abstract

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.