In der heutigen Ausgabe (03.Juni 2008) der F.A.Z. findet sich erneut ein Artikel zum Thema Precision Farming (PF), diesmal auf Seite T6 im Teil „Technik und Motor“. Viel Neues steht für mich persönlich nicht drin, eine kurze Zusammenfassung:

• kurze Einleitung mit der Begründung der Notwendigkeit von PF
• recht häufige Erwähnung von Claas
• Erwähnung des Crop Meters, eines Pendelsensors, der den Widerstand der Halme mißt und daraus auf den Bestand schließt
• Beschreibung eines Parallelfahrversuchs
• Erwähnung von GPS mit lokalem Korrektursignal
• Dienstleistungsmodell: elektronische Datenerfassung, Ertrags- und Erfolgskontrolle, Dokumentation

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At the moment we’re in the final stage of preparing one paper that presents our SOM research from recent months on the agriculture data. Future ideas will probably (at least in part) result from exploiting the SOM toolbox further. Some hints as to what’s possible can be found in the gallery.

The technical report that I linked to in the latest article is really comprehensive. Together with the supplied iris dataset (that can, of course, also be obtained elsewhere), the SOM toolbox works well and out-of-the-box, as expected. Seems as if the agricultural yield data we have are really interesting and can be visualised appealingly. The first labeled map shows the clustering capability of the SOM. There have been two fertilization strategies on the field where the data come from, simply named „B“ and „N“ here. The second map shows how much fertilizer has been used on the field, from low (blue) to high (red) values. The correlation between the labels and the colored map is obvious. Nevertheless, the ultimate goal still is to try to outperform a neural network in predicting the current year’s yield from sensor data and historical data and, along the way, identify indicators of a field’s heterogeneity.

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Inzwischen erreicht das Thema Precision Farming (oder Präziser Ackerbau bzw. Teilflächenspezifische Bewirtschaftung) auch die Massenmedien. In der Frankfurter Allgemeinen Zeitung vom 10.05.2008 ist auf Seite 20 ein Artikel mit der Überschrift Mit GPS in die Ackerfurche erschienen. Die vor allem wirtschaftlichen Vorteile von PF beim Pflügen, Düngen und bei der Aussaat werden klar herausgestellt. Auch die Möglichkeiten von Satellitenfotos werden erwähnt und die damit zu erzielenden teilweise beträchtlichen Kosteneinsparungen sind nicht zu verachten.

Hinzugefügt am 19.05.2008 Ähnlicher Artikel, etwas emotionaler, in der Wirtschaftswoche.

I feel as if I had just returned from AI-2007 (at least the expenses were paid just recently by the University) and there’s the deadline approaching for AI-2008, again held at Peterhouse College, UK.
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Scientific American just posted an interesting article on Science 2.0, which is namely describing the use of Web2.0-techniques in scientific work. Well, I’ve somehow done that transition already with this blog. There’s also, for larger research groups, the wiki technique to enable closer collaboration. There have been earlier attempts from the MIT, called Open WetWare, which is used to

promote the sharing of information, know-how, and wisdom among researchers and groups who are working in biology & biological engineering.

According to the SciAm article, people publish almost all of their work in progress online, like How-Tos, intermediate results from experiments, publication discussion and so on. This is also my philosophy of working in research. It’s not publish or perish but to get into discussion or high-level arguments, receiving early feedback and promoting useful ideas and hints to other researchers.

At the moment I’m still collecting ideas for data mining of agricultural data. It looks as if it’s going to be research and experimentation with supervised learning methods on those data and, in the process, describing the data flow from the wheat field to the machine learning task and back to the field. The next paper deadline is for the AI-2008, again held at Peterhouse College, Cambridge, UK. I’ll have to sort out the content first, though.

As recent as of March 28th, another paper of my/our work on the agriculture data got accepted. The conference is the Industrial Conference on Data Mining 2008, which will be taking place not far away in Leipzig, Germany. Based on the results and the information from those conferences, I might try to submit advanced work at BCS AI-2008, which will again take place at (frosty) Peterhouse College in Cambridge, UK. I won’t be there for long, though, as offspring is already on his/her way.

Two papers of mine have been accepted recently. Both describe the process of data mining with neural networks for agriculture data. Therefore I’m quite confident that  this will be my PhD thesis‘ major point of interest. The two conferences, which also provided worthwile and in-depth reviews, are IPMU 2008, in Málaga, Spain and IFIP AI 2008 in Milano, Italy. Of course, papers have to be redacted and my presentation will have to be prepared, but it’s encouraging nevertheless.

There are quite a few deadlines for publications approaching in January and I will submit another paper detailing some of the recent accomplishments on the agriculture data there. One of the conferences is ICDM 2008, held in Leipzig, Germany. It targets industrial applications of data mining and I felt the paper fit in there quite nicely.

I have also received more data sets from Martin Schneider at Martin-Luther-University of Halle-Wittenberg which will have to be mined. There are quite a lot of interesting tasks to be performed on those data — that requires thorough planning. I probably won’t be able to do that planning until my return from the organizational business trip to Melbourne, starting a cooperation project between our research group and the one that I worked with in 2004/2005.

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As I mentioned earlier, I’m still busy writing a paper on the agriculture data. Essentially, I’ve taken the wheat yield data, put a thorough description into the paper and have done some mining. This consisted of optimizing a neural network learned from the data or at least finding the optimal network topology to suit the data. The work was really interesting, given all the matlab scripting and evaluation of the network’s output. Actually, I’m lucky to have been provided with the data on this interesting problem.

Main outcome is that the wheat yield data (see details) can be modeled sufficiently well using neural networks with two hidden layers of 16 neurons each. It could also be seen experimentally that the size of the first hidden layer should be larger than or equal to the second hidden layer’s size. Something else that had been expected is that, as more data get available at the different times of fertilization, the approximation of the network gets better and hence the prediction gets better as well. The mse (mean squared error) sank from 0.3 to 0.2 t/ha where the mean yield was around 9 t/ha.

The preliminary abstract of the paper „Wheat yield prediction using Neural Networks“:

Precision agriculture (PA) and information technology (IT) are closely
interwoven. The former usually refers to the application of nowadays‘
technology to agriculture. Due to the use of sensors and GPS technology, in
today’s agriculture many data are collected. Making use of those data via IT
often leads to dramatic improvements in efficiency. This paper deals with
suitable modeling techniques for those agricultural data. In consequence, yield
prediction is enabled based on cheaply available site data. Based on this
prediction, economic or environmental optimization of, e.g., fertilization can
be carried out.

The corresponding matlab 2007b script can be downloaded here: agricult_10.m.