BeeLife

Canary

Canary is the BeeLife pathway for pollution investigation using Citizen Scientist apiarists to facilitate the understanding of pollution distribution - particularly heavy metals - across the Manchester area. A key strategic priority is environmental sustainability. Canary will help to monitor and sustain biodiversity in the Manchester Oxford corridor, as well as educating younger generations on the importance of sustaining our habitat for wildlife in the urban environment. We have Research Scientists participating from Computer Science and Earth and Atmospheric Science here at Manchester, and from the School for Resource and Environmental Studies at Dalhousie University in Canada.

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Collecting Bee Data

Bee hives serve as rich sources of data, including but not limited to: internal and external temperature, internal and external sound, and vibration. Finding relations and patterns in these datasets can help us to understand the bees and their environment.

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Historical Positioning

By monitoring bees from a rather far distance (10-20m) and calculating the historical trajectories, we can determine the health conditions and foraging preferences of bees and help improve pollination strategy. To achieve this, inflight behaviours and foraging patterns are detected by processing the video data with object tracking algorithms and classifiers. The video recording equipment is a 4k@60fps chromatic camera and a 2k@170Hz monochrome camera.

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Identification of Bees

By removing the ambient noise of the bees’ environment, an audio profile can be created for different bee species. Further, by using the same noise removal techniques, we are attempting to monitor bee health through the analysis of differences in multiple recordings.

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Real-Time Imaging, Motion Tracking and Audio Identification of Bees in Field Conditions

This project is investigating the feasibility of developing an advanced multi-modal system for remote daytime video tracking and acoustic characterisation of pollinating species in field conditions. The system will prove particularly beneficial in studying the behaviours of insects that have economic significance, for example pollinating bees, and by extension, insects that act as disease vectors. To exemplify the technology, the sensor system will initially address the challenge of dynamically mapping the exchange of pollinators from the floral field margins to the bulk crop.

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Tracking

In the UK, about 20% of cropland relies on insect pollination, for which the honey bee is the most important, so the 50% decline of the honey bee population since the second world war is of major concern. The overall goal of the project is to explore the requirements for an integrated bee hive sensor that is connected to the internet and capable of recording various metrics relevant to the health of the bee colony.

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