Machine learning for remote sensing of Xylella

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dc.contributor Ramasco Sukia, José Javier
dc.contributor Matias Muriel, Manuel Alberto Galván Fraile, Javier 2020 2022-01-25T12:39:42Z 2022-01-25T12:39:42Z 2020-07-27
dc.description.abstract [eng] Xylella fastidiosa (Xf) is a plant pest able to infect over 500 plant species worldwide. This pathogen has already caused considerable economic and environmental damage to olive groves in Apulia (Italy) in recent years, and has since spread throughout Mediterranean coastal zones. However, there is no effective treatment for it and the European Commission currently establishes hard eradication measures in the some of the most affected regions. Particularly, all susceptible plants that are within a radius of 100 meters around an infected specimen must be uprooted, resulting in a great economic loss. Consequently, diverse techniques and methods have been developed to detect the presence of Xylella fastidiosa in crops and monitor its spatio-temporal spreading dynamics in a large scale in order to prevent its expansion and impact. Traditional infield survey methods are accurate but costly for regional studies and monitoring. Instead, remote sensing along with machine learning algorithms constitute a quick and cost-effective methodology for determining the presence of the disease. Hence, in this project we present a novel technique for automatic detection of Xylella fastidiosa from satellite imagery. Particularly, we employ WorldView-2 satellite imagery with their 8-band multispectral data and a selection of vegetation indices for the purpose of training selected machine learning algorithms (SVM, artificial neural networks, recurrent neural networks, etc.) to determine whether an almond tree has the disease or not. The pilot testing has been carried out in Son Cotoner d’Avall farm (Puigpunyent, Mallorca), where a sample of 749 almonds have been subjected to q-PCR tests for Xylella fastidiosa during 2018, wherefrom we are provided with a WorldView-2 satellite image dated 22 June 2011. The applied multidisciplinary approach is promising, as the trained algorithms show accuracies above 65% despite of the time lag between the Xylella tests and the satellite image. Therefore, this work shows that large-scale satellite Xf monitoring is feasible and opens the possibility of significant and promising progress based on this idea. ca
dc.format application/pdf
dc.language.iso eng ca
dc.publisher Universitat de les Illes Balears
dc.rights all rights reserved
dc.rights info:eu-repo/semantics/openAccess
dc.subject 004 - Informàtica ca
dc.subject.other Machine learning ca
dc.subject.other Xylella ca
dc.subject.other Remote sensing ca
dc.subject.other Satellite imagery ca
dc.subject.other WorldView2 ca
dc.subject.other Artificial Neural Networks ca
dc.subject.other Recurrent Neural Networks ca
dc.title Machine learning for remote sensing of Xylella ca
dc.type info:eu-repo/semantics/masterThesis ca
dc.type info:eu-repo/semantics/publishedVersion 2021-06-30T11:13:14Z

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