Cacophony Blog

Trapping, poison, hunting, and fences have all been turned against pests to help prevent the ongoing slaughter of New Zealand birds. However, we know that current trapping methods aren’t cutting it. As part of our experimenting we have had video trail cameras pointing at existing (traditional) traps to measure how well different digital lures work. What has completely surprised us is that as little as 1% of the time that a possum turned up on camera a trap was activated.

By Brent Martin, adjunct senior research fellow, University of Canterbury

The aim of this project was to see if the latest Machine Learning (Artificial Intelligence) tools could correctly identify the difference between rats, stoats, possums and others from videos collected from the field. This project was given to a set of 28 final year honours students at University of Canterbury.

The Cacophony Project Coordinator, Grant Ryan, recently sat down with RNZ presenter, Jesse Mulligan,  on his weekday program to talk about creating a healthier New Zealand ecosystem.

Machine learning (a branch of Artificial Intelligence) is the general term for how computers learn to recognise patterns. Machine Learning has become very powerful with the advent of inexpensive computer processing and the explosion in available data enabled by the internet and cloud computing. Capitalising on all this data, deep neural networks have recently shown order-of-magnitude improvements in tasks such as automatic speech recognition and computer vision.

The Cacophony Project has been founded on the idea that some problems are too big, too complex (and too important) for any one commercial entity to solve. For a really huge problem like restoring NZ's native ecosystems, innovation is needed from all over the place. We recognise that a few experts can contribute what they know at low cost to themselves but at high value to the rest of us.