Evaluation of oil-palm fungal disease infestation with canopy hyperspectral reflectance data.
Sensors (Basel)
; 10(1): 734-47, 2010.
Article
en En
| MEDLINE
| ID: mdl-22315565
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Fotometría
/
Enfermedades de las Plantas
/
Análisis Espectral
/
Arecaceae
/
Ganoderma
Tipo de estudio:
Guideline
Idioma:
En
Revista:
Sensors (Basel)
Año:
2010
Tipo del documento:
Article
País de afiliación:
Francia
Pais de publicación:
Suiza