RT info:eu-repo/semantics/article
T1 UAV-Based Disease Detection in Palm Groves of Phoenix canariensis Using Machine Learning and Multispectral Imagery
A1 Arbelo Pérez, Manuel Imeldo
A1 Casas Más, Enrique José
A1 Moreno-Ruiz, José A.
A1 Hernández-Leal, Pedro A.
A1 Reyes-Carlos, José A.
A2 Física
K1 probabilistic classification modelling
K1 support vector machine
K1 random forest
K1 spectral separability analysis
K1 structure insensitive pigment index
K1 NDVI
K1 Canary Islands
AB Climate change and the appearance of pests and pathogens are leading to the disappearance of palm groves of Phoenix canariensis in the Canary Islands. Traditional pathology diagnostic techniques are resource-demanding and poorly reproducible, and it is necessary to develop new monitoring methodologies. This study presents a tool to identify individuals infected by Serenomyces phoenicis and Phoenicococcus marlatti using UAV-derived multispectral images and machine learning. In the first step, image segmentation and classification techniques allowed us to calculate a relative prevalence of affected leaves at an individual scale for each palm tree, so that we could finally use this information with labelled in situ data to build a probabilistic classification model to detect infected specimens. Both the pixel classification performance and the model’s fitness were evaluated using different metrics such as omission and commission errors, accuracy, precision, recall, and F1-score. It is worth noting the accuracy of more than 0.96 obtained for the pixel classification of the affected and healthy leaves, and the good detection ability of the probabilistic classification model, which reached an accuracy of 0.87 for infected palm trees. The proposed methodology is presented as an efficient tool for identifying infected palm specimens, using spectral information, reducing the need for fieldwork and facilitating phytosanitary treatment
YR 2023
FD 2023
LK http://riull.ull.es/xmlui/handle/915/36916
UL http://riull.ull.es/xmlui/handle/915/36916
LA en
NO https://doi.org/10.3390/rs15143584
DS Repositorio institucional de la Universidad de La Laguna
RD 03-ene-2025