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Feature Selection for Wheat Yield Prediction
Type of publication: Inproceedings
Citation: russ2010sgai
Booktitle: Research and Development in Intelligent Systems XXVI, Incorporating Applications and Innovations in Intelligent Systems XVII
Series: Proceedings of AI-2009
Volume: 26
Year: 2010
Month: January
Pages: 465--478
Publisher: Springer
Organization: BCS SGAI
Address: London
ISBN: 978-1-84882-982-4
URL: http://www.springerlink.com/co...
DOI: 10.1007/978-1-84882-983-1_36
Abstract: Carrying out effective and sustainable agriculture has become an important issue in recent years. Agricultural production has to keep up with an ever-increasing population by taking advantage of a field's heterogeneity. Nowadays, modern technology such as the global positioning system (GPS) and a multitude of developed sensors enable farmers to better measure their fields' heterogeneities. For this small-scale, precise treatment the term precision agriculture has been coined. However, the large amounts of data that are (literally) harvested during the growing season have to be analysed. In particular, the farmer is interested in knowing whether a newly developed heterogeneity sensor is potentially advantageous or not. Since the sensor data are readily available, this issue should be seen from an artificial intelligence perspective. There it can be treated as a feature selection problem. The additional task of yield prediction can be treated as a multi-dimensional regression problem. This article aims to present an approach towards solving these two practically important problems using artificial intelligence and data mining ideas and methodologies.
Keywords: Data Mining, Feature Selection, Precision Agriculture
Authors Ruß, Georg
Kruse, Rudolf
Editors Allen, Tony
Ellis, Richard
Petridis, Miltos
Added by: [GR]
Total mark: 0
Attachments
  • russ2009sgai.pdf
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