Detecting Varroa mites early is one of beekeeping's major practical challenges. Traditional methods typically require the beekeeper to open the hive or take samples from the colony.
A new study, however, investigates whether bee colonies can be continuously monitored non-invasively.
The researchers built a multimodal system where several different types of information were combined:
- camera and image analysis
- sound from the bee colony
- temperature and other environmental data
The information was analyzed using machine learning.
The system was tested on nine bee colonies, and the study encompassed over 26,000 observations.
An interesting aspect of the project is that the analysis can be performed locally, known as edge computing. This means that large amounts of data do not necessarily need to be sent to an external server before they can be analyzed.
The researchers demonstrate that the combination of multiple sensors can provide information about Varroa mite load without the beekeeper needing to disturb the colony.
The hardware for the prototype is reported to cost approximately 190 US dollars per hive.
Of course, this does not mean the technology is ready for mainstream beekeeping. Nine colonies represent a limited sample size, and the systems need to be tested under more climate conditions, in different types of apiaries, and over longer periods.
However, the direction of development is intriguing.
When camera, sound, temperature, and other observations are combined, AI can begin to detect patterns that are difficult for humans to continuously track.
Therefore, the question may no longer be whether AI will be used for monitoring bee colonies – but rather how useful, reliable, and economically viable the technology can become.
Source: A Multimodal Machine Learning System for Non-Invasive Detection of Varroa destructor Infestations in Honey Bee Colonies, Smart Agricultural Technology, 2026. Published online July 1, 2026.