UAV-Based Disease Detection in Palm Groves of Phoenix canariensis Using Machine Learning and Multispectral Imagery

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 methodologi...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-07, Vol.15 (14), p.3584
Main Authors: Casas, Enrique, Arbelo, Manuel, Moreno-Ruiz, José A., Hernández-Leal, Pedro A., Reyes-Carlos, José A.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Climate change
Crop diseases
Disease detection
Fieldwork
Image classification
Image processing
Image segmentation
Islands
Learning algorithms
Leaves
Machine learning
Medical imaging
NDVI
Pests
Phoenix canariensis
Pixels
Plant diseases
probabilistic classification modelling
random forest
Remote sensing
Satellites
spectral separability analysis
structure insensitive pigment index
support vector machine
Surveillance
Trees
Vegetation
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15143584