Xylella fastidiosa is a devastating pathogen that has significantly impacted olive cultivation, particularly in Southern Italy, since 2013. Monitoring and early identification are crucial for managing the spread of this disease and minimising associated economic losses. This study presents a low-cost system employing electrical conductivity (EC) probes to monitor the water status of olive tree stems, enabling the early detection of X. fastidiosa. The system provides real-time data by detecting changes in EC, which are possibly correlated with the presence of the pathogen. In contrast to traditional detection methods like PCR, which are expensive and require laboratory facilities, this system offers a practical, field-deployable, and sustainable solution. In a testbed, EC probes were installed on an olive tree in Southern Italy, with data transmitted via a GSM network to a central server for analysis. Powered by solar energy, the system successfully detected changes in the stem’s water content, which were subsequently confirmed to be associated with pathogen presence. This low-cost EC probe system initially demonstrates promising potential for the early detection of X. fastidiosa, enabling proactive management strategies. Future research will focus on improving the system’s sensitivity and conducting extensive field trials across diverse environments and applications.

The spread of Xylella fastidiosa, a xylem-limited bacterial pathogen, has caused widespread mortality among olive trees in Apulian region, Italy in more than a decade, and represents a significant threat to Mediterranean agroecosystems. To encourage evidence-based containment strategies, we developed a stochastic, spatiotemporal simulation model that represents pathogen transmission at the individual-tree level. This work integrates high-resolution georeferenced olive-tree data and implicitly incorporates vector population dynamics through a tree-specific vulnerability index, which considers local host density and landscape connectivity. Vector dispersal is approximated using a radial transmission kernel, which preserves host–vector spatial interactions while avoiding the explicit modeling of insect trajectories. The system’s spatial structure is additionally formulated as a proximity graph, facilitating network-based analysis of spread pathways. A series of Monte Carlo simulation experiments is employed for calibration against the observed epidemic footprint, while validation utilizes independent infection records and global sensitivity analysis of key parameters. The findings indicate that the model effectively replicates realistic propagation patterns, and its calibrated parameters are consistent with out-of-sample data. This makes it an appropriate exploratory tool for scenario testing, assessing the potential impact of intervention strategies, and offering risk-based decision support for handling Xylella fastidiosa outbreaks. Subsequently, graph centrality metrics are used to identify epidemiologically critical trees that function as transmission bridges, thus representing priority targets for surveillance or removal efforts. Thus, multiple tests have been conducted using betweenness and closeness centrality, while comparing both methods leads to effective node-tree removal decisions.