Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/216639
COMPARTIR / EXPORTAR:
logo share SHARE logo core CORE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invitar a revisión por pares abierta
Título

Detection of white root rot in avocado trees by remote sensing

AutorPérez-Bueno, María Luisa CSIC ORCID; Pineda Dorado, Mónica CSIC ORCID ; Vida, Carmen; Fernández-Ortuño, D.; Torés, J. A.; Vicente, A. de; Cazorla, Francisco Manuel; Barón Ayala, Matilde CSIC ORCID
Palabras claveNDVI
Machine learning
Thermal imaging.
Fecha de publicación2019
EditorAmerican Phytopathological Society
CitaciónPlant Disease 103: 1119-1125 (2019)
ResumenWhite root rot, caused by the soilborne fungus Rosellinia necatrix, is an important constraint to production for a wide range of woody crop plants such as avocado trees. The current methods of detection of white root rot are based on microbial and molecular techniques, and their application at orchard scale is limited. In this study, physiological parameters provided by imaging techniques were analyzed by machine learning methods. Normalized difference vegetation index (NDVI) and normalized canopy temperature (canopy temperature - air temperature) were tested as predictors of disease by several algorithms. Among them, logistic regression analysis (LRA) trained on NDVI data showed the highest sensitivity and lowest rate of false negatives. This algorithm based on NDVI could be a quick and feasible method to detect trees potentially affected by white root rot in avocado orchards.
Versión del editorhttp://dx.doi.org/10.1094/PDIS-10-18-1778-RE
URIhttp://hdl.handle.net/10261/216639
DOI10.1094/PDIS-10-18-1778-RE
Identificadoresdoi: 10.1094/PDIS-10-18-1778-RE
issn: 0191-2917
Aparece en las colecciones: (EEZ) Artículos
(IHSM) Artículos




Ficheros en este ítem:
Fichero Descripción Tamaño Formato
2019_Perez-Bueno_OA.pdf1,05 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo

CORE Recommender

SCOPUSTM   
Citations

20
checked on 24-mar-2024

WEB OF SCIENCETM
Citations

20
checked on 26-feb-2024

Page view(s)

150
checked on 28-mar-2024

Download(s)

83
checked on 28-mar-2024

Google ScholarTM

Check

Altmetric

Altmetric


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.