Machine Learning for Remote Sensing of Xylella fastidiosa

In this Master Thesis we study Xylella fastidiosa (Xf), that 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. 



Master Thesis defense

Advisors: Jose J. Ramasco and Manuel A Matias

Jury: Emilio Hernández-García, Claudio R. Mirasso, and Jose J. Ramasco