TY  - CONF
ID  - russ2008icdm
T1  - Data Mining with Neural Networks for Wheat Yield Prediction
A1  - Ruß, Georg
A1  - Kruse, Rudolf
A1  - Wagner, Peter
A1  - Schneider, Martin
ED  - Perner, Petra
TI  - Advances in Data Mining -- Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
T3  - LNAI
Y1  - 2008
VL  - 5077
SP  - 47
EP  - 56
PB  - Springer Verlag
CY  - Leipzig
AD  - Berlin, Heidelberg
SN  - 978-3-540-70717-2
SN  - 0302-9743
M2  - doi: 10.1007/978-3-540-70720-2_4
KW  - Data Mining
KW  - Neural Networks
KW  - Precision Agriculture
KW  - Prediction
N2  - 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. For this purpose, the challenge is to change these raw data into useful information. In this paper we deal with neural networks and their usage in mining these data. Our particular focus is whether neural networks can be used for predicting wheat yield from cheaply-available in-season data. Once this prediction is possible, the industrial application is quite straightforward: use data mining with neural networks for, e.g., optimizing fertilizer usage, in economic or environmental terms.
ER  -