At the last Christchurch AI meetup, I met up with Ian Harris who told me about the work he had done with neXtgen Agri for The New Zealand Merino Company (NZM) as part of Sensing Wellbeing, a collaborative Sustainable Farming Fund Project. This work involved analysing data collected from accelerometers attached to jaws of sheep to try to identify their behaviour.
Like any machine learning project, the critical part is the data. In this case the raw data came from the tri-axial accelerometers, sampled at 30 Hz. This meant that for each of the three channels there were 300 samples over a 10 second period. This data was collected from 15 sheep over a period of 6 days in conjunction with Massey University.
6 of the sheep were filmed during that time and their behaviour was categorised into 12 different activities. An initial review of the data showed that there was only good data for 5 of the 12 activities and so the focus was on those activities:
- ruminating while sitting
- ruminating while standing
For those of you (like me) who are not intricately familiar with the lives of sheep, ruminating is the process or regurgitating, re-chewing and re-swallowing food.
Random forest approach
31 different metrics were calculated from the raw data, metrics like energy, maximum, etc. The initial approach Ian took was to use a random forest algorithm with these metrics or features as an input. With this approach the model correctly classifed 81% of the activities. This was a replication of an approach taken by a South African team who got similar results and helped validate the overall set up.
Deep learning approach
Ian is an experienced Java developer and had taught himself python and Deep Learning. For this problem he was using Tensorflow and set up a relatively simple 3-layer network that used the raw data for input rather than the calculated features used with the random forest approach. His most successful model had a binary output to detect whether (or should that be wether?) the sheep was grazing or not. This model had 93% accuracy.
Only six of the fifteen sheep had their behaviour recorded, so Ian had a lot of unlabelled data. In an effort to leverage this data he used an unsupervised clustering algorithm to try to generate more data from which to learn. This did improve the detection rates, but only for models for which there was relatively low accuracy.
You could imagine a connected device that could accurately predict whether a sheep is grazing or not may be useful if it could alert a farmer when a sheep has stopped grazing for a period of time so he or she could check on the sheep. It’s been quite a few decades since I lived on a sheep farm – so I’m not able to make any sort of informed commentary on that. Even if this was useful, there are some engineering challenges in designing a device that is cheap enough to make this economic.
That said, I would like to congratulate NZM, and neXtgen Agri for undertaking this project. Whether or not it leads to a commercial product, they will have learned a lot about the capabilities of the AI technologies used along with the data requirements and the time and investment needed. And in Ian, New Zealand has another engineer with AI experience which I’m sure will be put to good use.