Marek Urban’s research while affiliated with Slovak University of Agriculture and other places

Precision beekeeping has emerged as a response to the need for optimizing beekeeping management using sensors and specialized programs, aiming to provide beekeepers with insights into hive conditions without the need for frequent inspections. While the field has made solid advances, particularly with data such as hive weight and temperature, recent years have seen the emergence of sound and image-based methods, opening up new possibilities. There is a need for sensors capable of detecting queen bee presence, Varroa mite levels, disease and predator presence and prediction of swarming behaviour. Sensors that can detect nectar scents and other hive odours, as well as accurately predict the timing for honey harvest and feeding requirements, needs to be designed or improved. Integrating data collection, machine learning, artificial intelligence, and best management practices into an intelligent apiary management system is crucial for the advancement of precision beekeeping.