Neurocomputing
Neural Computation
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Practical Application of a KDD Process to a Sulphuric Acid Plant
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Data Mining of Agricultural Yield Data: A Comparison of Regression Models
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Data mining in precision agriculture: management of spatial information
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Regression models for spatial data: an example from precision agriculture
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Spatial variable importance assessment for yield prediction in precision agriculture
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
A neural network ensemble method for precision fertilization modeling
Mathematical and Computer Modelling: An International Journal
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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.