Neurocomputing
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Machine Learning
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Data Mining with Neural Networks for Wheat Yield Prediction
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Support Vector Machines for Visualization and Dimensionality Reduction
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Evolutionary feature and parameter selection in support vector regression
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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
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Nowadays, precision agriculture refers to the application of state-of-the-art GPS technology in connection with small-scale, sensor-based treatment of the crop. This introduces large amounts of data which are collected and stored for later usage. Making appropriate use of these data often leads to considerable gains in efficiency and therefore economic advantages. However, the amount of data poses a data mining problem --- which should be solved using data mining techniques. One of the tasks that remains to be solved is yield prediction based on available data. From a data mining perspective, this can be formulated and treated as a multi-dimensional regression task. This paper deals with appropriate regression techniques and evaluates four different techniques on selected agriculture data. A recommendation for a certain technique is provided.