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Machine Learning for Remote Sensing of Xylella fastidiosa

AuthorsGalván Fraile, Javier
AdvisorRamasco, José J. ; Matías, Manuel A.
KeywordsMachine learning
Xylella fastidiosa
Remote sensing
Satellite imagery
Artificial Neural Networks
Recurrent Neural Networks
Issue Date2020
PublisherUniversidad de las Islas Baleares
CSIC-UIB - Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC)
AbstractXylella 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 are no effective control strategies against 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 Xf 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 Xf 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 almond trees have been subjected to q-PCR tests for Xf 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 Xf 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.
DescriptionMaster’s Degree in Physics of Complex Systems (Specialization/Pathway in Complex Systems), Centre for Postgraduate Studies, University of the Balearic Islands, academic Year 2019/2020.
Appears in Collections:(IFISC) Tesis
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