TY  - CONF
ID  - russ2010sgai
T1  - Feature Selection for Wheat Yield Prediction
A1  - Ruß, Georg
A1  - Kruse, Rudolf
ED  - Allen, Tony
ED  - Ellis, Richard
ED  - Petridis, Miltos
TI  - Research and Development in Intelligent Systems XXVI, Incorporating Applications and Innovations in Intelligent Systems XVII
T3  - Proceedings of AI-2009
Y1  - 2010
VL  - 26
SP  - 465
EP  - 478
PB  - Springer
T2  - BCS SGAI
AD  - London
SN  - 978-1-84882-982-4
UR  - http://www.springerlink.com/content/j27614612tn44664
M2  - doi: 10.1007/978-1-84882-983-1_36
KW  - Data Mining
KW  - Feature Selection
KW  - Precision Agriculture
N2  - 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.
ER  -