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Visualization of Agriculture Data Using Self-Organizing Maps
| Type of publication: | Inproceedings |
| Citation: | russ2009sgai |
| Booktitle: | Applications and Innovations in Intelligent Systems |
| Series: | Proceedings of AI-2008 |
| Volume: | 16 |
| Year: | 2009 |
| Month: | January |
| Pages: | 47--60 |
| Publisher: | Springer |
| Location: | London |
| Organization: | BCS SGAI |
| ISBN: | 978-1-84882-214-6 |
| URL: | http://www.springer.com/comput... |
| Abstract: | The importance of carrying out effective and sustainable agriculture is getting more and more obvious. In the past, additional fallow ground could be tilled to raise production. Nevertheless, even in industrialized countries agriculture can still improve on its overall yield. Modern technology, such as GPS-based tractors and sensor-aided fertilization, enables farmers to optimize their use of resources, economically and ecologically. However, these modern technologies create heaps of data that are not as easy to grasp and to evaluate as they have once been. Therefore, techniques or methods are required which use those data to their full capacity -- clearly being a data mining task. This paper presents some experimental results on real agriculture data that aid in the first part of the data mining process: understanding and visualizing the data. We present interesting conclusions concerning fertilization strategies which result from data mining. |
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| Added by: | [ADM] |
| Total mark: | 0 |
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