%0 Conference Paper %1 russ2008ipmu %A Ruß, Georg %A Kruse, Rudolf %A Schneider, Martin %A Wagner, Peter %B Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-08) %D 2008 %E Verdegay, José Luis %E Ojeda-Aciego, Manuel %E Magdalena, Luis %K agriculture, data mining networks, neural precision prediction, %P 576--582 %T Optimizing Wheat Yield Prediction Using Different Topologies of Neural Networks %X 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 by using decision rules. These rules include the management know-how for (economic) optimal recommendations. This paper deals with suitable modeling techniques for those agricultural data where the objective is to uncover the existing patterns. 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.

@inproceedings{russ2008ipmu, abstract = {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 by using decision rules. These rules include the management know-how for (economic) optimal recommendations. This paper deals with suitable modeling techniques for those agricultural data where the objective is to uncover the existing patterns. 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.}, added-at = {2008-07-04T11:27:53.000+0200}, author = {Ru{\ss}, Georg and Kruse, Rudolf and Schneider, Martin and Wagner, Peter}, biburl = {https://www.bibsonomy.org/bibtex/2f8c799df48a2396d2bfe16d28dd807a3/gr650}, booktitle = {Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-08)}, editor = {Verdegay, Jos{\'{e}} Luis and Ojeda-Aciego, Manuel and Magdalena, Luis}, interhash = {d50cb3f69ce85830b32b242caf054115}, intrahash = {f8c799df48a2396d2bfe16d28dd807a3}, keywords = {agriculture, data mining networks, neural precision prediction,}, location = {M{\'{a}}laga}, month = {June}, organization = {University of M{\'{a}}laga}, pages = {576--582}, timestamp = {2008-07-04T11:27:53.000+0200}, title = {Optimizing Wheat Yield Prediction Using Different Topologies of Neural Networks}, year = 2008 }