UCSC

UCSC is leader of WP2; The main goal is to establish a complete pipeline for pest and disease monitoring, from data collection to pest prediction and detection. The specific objectives are to: Establish protocols and procedures that ensure the accuracy, consistency, objectivity and efficiency of data collection in any location and by any person Utilise collected data to create AI algorithms for pest and disease detection. This task will be responsible for identifying the state-of-the-art in pest modelling towards the characterization of the epidemiological traits of the regulated plant pests STELLA will focus on.

Characterization of these parameters will allow to better rank each pest for their capacity in different aspects (survival between growing seasons, reaching the host and causing infection, producing viable inoculum to trigger epidemic, spread into the environment, producing strains or subpopulations resistant to plant protection products, etc). The generated knowledge will improve the understanding of the epidemic behaviour of each target pest and quantify the risk for each single aspect. The characterization altogether will improve the specification of efficient disease control methods and strategies, with particular regard to monitoring and early detection of the disease onset. The STELLA selected pests (quarantine and RNQP) will be studied as examples among bacteria, fungus, insects and phytoplasma. The obtained data will be used [UCSC and HORTA] to define simple bioclimatic indices about the main relevant characteristic of each studied pest, into assessing the risk of a specific disease.