Seems like I’m with the prefix Ca in the recent list of city names I’ve visited. Anyway, I’m back from Canberra after yet another three flights, including a 20-min bus ride at DXB and a 4-hour train ride within Germany. In hindsight it’s been really useful to present my work (past, present and future) in a comprehensive talk at the Australian Taxation Office. I had around 20 direct listeners, some of which were from The Australian National University and from the Commonwealth Scientific and Industrial Research Organisation. Some additional listeners were connected via a telephone conferencing system around the country.

My direct conversation partners and hosts were Graham Williams and Warwick Graco. I could talk about my ideas at length and got very valuable feedback from them, regarding methodologies and techniques and possible pitfalls. Apart from the business talks, the city of Canberra is really worth a visit — might be due to the fact that I’ve been shown around by these two seasoned guys who really know their city. I also happened to visit the National Gallery of Australia where Masterpieces from Paris are on display — another really worthwile exhibition.

Nevertheless, I’m off to Cambridge tomorrow, for the AI-2009 conference, yet again at freezing Peterhouse College. The slides for my talk are going to be the results of the respective paper, spiced up with some introductory and motivational slides from the ATO talk. The slides: slides-russ2009sgai.pdf.

I’ll be giving a talk at the Australian Taxation Office, likely to take place on 11th of December at 1100 local time (Canberra, ACT). The slides can be obtained here: slides-russ2009ato.pdf. The abstract is as follows:

Data Mining in Agriculture

In recent years, due to new and affordable technological advances, data
collection has turned into an everyday task. Nowadays, especially with the
advent of the global positioning system and modern farming vehicles, sensors
and equipment, even agriculture has turned into a data-driven discipline
called precision agriculture. However, as in numerous other research and
production areas, collecting data is not sufficient for economic or ecological
well-being. The collected data have to be mined and turned into usable
knowledge.

Therefore, this talk presents some approaches towards data mining in
agriculture. The talk will begin with a short overview about the origins of
the actual agriculture data. The difference between spatial and non-spatial
approaches will be emphasised using an example of yield prediction. Some of
the non-spatial techniques such as clustering, regression and feature
selection may be carried over to spatial approaches. Most of the presented
work considers very recent issues which remain unsolved in this discipline so
far (at least to the speaker’s knowledge). Furthermore, the presented work is
an excerpt of what is going to be the speaker’s PhD thesis, which is likely to
cover Data Mining in Agriculture from a computer scientist’s perspective.