Agritrop
Home

Evaluation of oil-palm fungal disease infestation with canopy hyperspectral reflectance data

Lelong Camille, Roger Jean-Michel, Brégand Simon, Dubertret Fabrice, Lanore Mathieu, Sitorus Nurul Amin, Raharja Doni Artanto, Caliman Jean-Pierre. 2010. Evaluation of oil-palm fungal disease infestation with canopy hyperspectral reflectance data. Sensors, 10 (1) : pp. 734-747.

Journal article ; Article de revue à facteur d'impact Revue en libre accès total
[img] Published version - Anglais
Use under authorization by the author or CIRAD.
document_553942.pdf

Télécharger (803kB)

Quartile : Q1, Sujet : INSTRUMENTS & INSTRUMENTATION / Quartile : Q3, Sujet : CHEMISTRY, ANALYTICAL / Quartile : Q3, Sujet : ELECTROCHEMISTRY

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. (Résumé d'auteur)

Mots-clés Agrovoc : Elaeis guineensis, Maladie des plantes, Champignon pathogène, Ganoderma, Lumière, Réflectance, Réflectométrie, Relevé (des données)

Mots-clés géographiques Agrovoc : Asie du Sud-Est

Classification Agris : H20 - Plant diseases
U30 - Research methods
F60 - Plant physiology and biochemistry

Champ stratégique Cirad : Axe 1 (2005-2013) - Intensification écologique

Auteurs et affiliations

  • Lelong Camille, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0002-4850-1010
  • Roger Jean-Michel, CEMAGREF (FRA)
  • Brégand Simon
  • Dubertret Fabrice
  • Lanore Mathieu
  • Sitorus Nurul Amin, SMART Research Institute (MYS)
  • Raharja Doni Artanto, SMART Research Institute (IDN)
  • Caliman Jean-Pierre, CIRAD-PERSYST-UPR Systèmes de pérennes (IDN)

Autres liens de la publication

Source : Cirad - Agritrop (https://agritrop.cirad.fr/553942/)

View Item (staff only) View Item (staff only)

[ Page générée et mise en cache le 2021-04-18 ]