Ricerc@Sapienza

The ectoparasitic mite Varroa destructor is the most serious threat to honey bees in Italy and worldwide and has played a fundamental role in the decline of honey bee colonies all over the Northern Hemisphere in the last decade. Honeybee mortality rates range from 5% to 30% and the Varroa mite represents one of the main causes. This pathology must be continuously contained on levels of infestation compatible with the survival of bee colonies through periodic pharmacological treatments.
Current diagnostic techniques are based on manual visual inspection of characteristic regions in the body of bee or other time-consuming laboratory methods. Late diagnosis of this ectoparasite causes a number of harmful effects either physical, physiological and pathological at the individual and at colony level. The aim of this project is to find valuable solutions to overcome these challenges. The project aims to minimize the detection time of the critical infestation threshold of varroa in bees colonies and to track such infestation over time. This is achieved by designing an automatic detection system of the level of infestation based on visual deep learning algorithms running as a distributed computing system on hives.
The project contains challenging and innovative aspects. The first aspect is a reliable automatic identification method of static and dynamic varroa markers together with the analysis of the correlation between such distinctive features and the level of infestation of a colony. Thanks to the use of hyperspectral and thermal cameras installed inside the hive, the behavior of the bees will be available as new dynamic markers that can improve current state of the art diagnostic methods. Another aspect concerns the design of energy-efficient deep learning algorithms for the detection of the infestation. Since the hives are not connected to any source of electricity, local microcomputers installed in the hives will be powered by solar panels mounted on top of the hives.