Automated in-hive monitoring and advanced data analytics to detect honey bee diseases

In the UK honey bees contribute over £430m per annum to agriculture, with the Western honey bee (Apis mellifera) providing up to 50% of pollinator ecosystem services. Some suggest the honey bee to be the third most important domesticated animal in the UK, with about 300k colonies and over 40k beekeepers. Unfortunately, honey bees have been badly affected by numerous interacting pressures in recent decades, including agricultural intensification, land use change, extreme weather events, and a growing number of pests and diseases.

This project focuses on two of these disease problems: Varroosis and chronic bee paralysis. The honey bee mite (Varroa destructor) arrived in the UK in the early 1990s, and its presence causes the indigenous deformed wing virus to become highly pathogenic, a condition known as Varroosis. This is the most serious cause of honey bee loss worldwide, and control measures include integrated pest management using pyrethroids to control mites, although mite resistance is becoming increasingly problematic.

Chronic bee paralysis is a disease of adult bees caused by the chronic bee paralysis virus (CBPV). This disease is more recent, increasing in prevalence since in the UK since 2007. As such, there are currently no evidence-based control measures for this disease.

Beekeepers need to monitor colonies carefully and on a regular basis to detect Varroosis or chronic bee paralysis. However, it is highly probable that the two diseases are present within a colony before symptoms can readily be detected through routine monitoring of mite populations or visible evidence of diseased worker bees. Previous research has demonstrated that the behaviours of affected bees starts to change in relatively subtle ways at early stages of infection, and the behaviour of healthy bees towards their diseased sisters also alters.

New, non-invasive hive-monitoring technologies provide the opportunity to detect some of these changes in behaviour, in particular the acoustics and vibration patterns within a colony. Additional data related to hive temperature, humidity, foraging worker flight exits/return counts, as well as local meteorological conditions can also be collected.

We plan to collect in-hive and external meteorological data from both research and commercial apiaries in the UK across a season. We will also undertake regular assessments of the health of the colonies for Varroosis and symptoms of chronic bee paralysis. Our monitoring will produce large data streams from the apiaries monitored.

The largest datasets will be the acoustic data from microphones in the colonies, and we will simplify the raw data to a frequency domain via Fast Fourier Transformation (FFT). The FFT data will then be modelled by either time-series autoregression or machine learning approaches, incorporating other in-hive and external monitor streams as 'meta-data'.

The two modelling approaches will be compared, to determine both their effectiveness to discriminate between diseased and healthy colonies, and also their ability to detect disease at an early stage.

We will work closely with our industrial partner (Agrisound) to implement the most practical and cost-effective in-field monitoring systems, in terms of energy use, bandwidth for data streaming etc. This will also include the most appropriate data processing pipelines (centralised or decentralised) to increase the practical value of system.