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Save the Bees, one buzz at a time

Primary supervisor

Bernd Meyer


  • Lori Lach (JCU)

In this project, we will use machine learning methods to diagnose the health status of bee colonies and individual bees.

Bee populations are threatened worldwide due to a number of factors, including parasites and virus infections, climate change, intensive farming, and other environmental stress factors. Australia, until recently, has been relatively protected from infections, but these are now increasingly taking place here as well. 

Early identification of infected colonies is a crucial component for treating infections effectively. Unfortunately, this currently requires very labour intensive and unreliable techniques and is thus often not done so that infected colonies are discovered far too late. 

In this project, we aim to build Ai tools based on Deep Learning to automatically classify the health status of a colony by analysing data resulting from automatic observations of individuals. This will open the path to continuous low cost monitoring the health status of bee colonies without requiring human intervention. 

This project is a collaboration with the Centre for Tropical Environmental and Sustainability Science at the University of Townsville.


Required knowledge

The ideal candidate has a strong interest in interdisciplinary work (and ideally biology), good knowledge of general classification methods and specifically CNNs, and skills in Python. Additional skills in R and the usual NN tools (Tensorflow etc) are desirable but not strictly required. 

Project funding


Learn more about minimum entry requirements.