Edge Vision against Varroa (EV2)


Informazioni sul progetto

Descrizione del progetto

The Varroa destructor ectoparasite mite is the most serious threat to bees in Italy and the world [30,31]. Mortality rates of honey bees vary from 5% to almost 30% depending on the country and year, being the Varroa mite some of the main causes [41].

This pathology must be continuously contained on levels of infestation compatible with the survival of bee colonies through periodic drug treatments, on pain of the death of families in one or two seasons of bees. Current diagnostic techniques for this parasite are based on manual visual inspection of some characteristic regions in the body of bees or other time-consuming laboratory methods. Late diagnosis of this ectoparasite causes several harmful physical, physiological, and pathological effects on bees at the individual and colony levels.

The project aims at the design and prototyping of a visual detection system of the varroa mite, which exploits energy-aware resource management and application algorithms based on the cloud-to-IoT continuum paradigm, tailored and extended to run on cooperating energy-constrained devices. This innovative automatic detection system of the level of infestation uses visual deep learning algorithms that dramatically reduce the state-of-the-art detection time of the varroa infestation level.

Research Objectives and KPI