  Wide use and availability of the machine learning and computer vision
techniques allows development of relatively complex monitoring systems in many
domains. Besides the traditional industrial domain, new application appears
also in biology and agriculture, where we could speak about the detection of
infections, parasites and weeds, but also about automated monitoring and early
warning systems. This is also connected with the introduction of the easily
accessible hardware and development kits such as Arduino, or RaspberryPi
family. In this paper, we survey 50 existing papers focusing on the methods of
automated beehive monitoring methods using the computer vision techniques,
particularly on the pollen and Varroa mite detection together with the bee
traffic monitoring. Such systems could also be used for the monitoring of the
honeybee colonies and for the inspection of their health state, which could
identify potentially dangerous states before the situation is critical, or to
better plan periodic bee colony inspections and therefore save significant
costs. Later, we also include analysis of the research trends in this
application field and we outline the possible direction of the new
explorations. Our paper is aimed also at veterinary and apidology professionals
and experts, who might not be familiar with machine learning to introduce them
to its possibilities, therefore each family of applications is opened by a
brief theoretical introduction and motivation related to its base method. We
hope that this paper will inspire other scientists to use machine learning
techniques for other applications in beehive monitoring.
