A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
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
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
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
Exploratory hierarchical clustering for management zone delineation in precision agriculture
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
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The term precision agriculture refers to the application of state-of-theart GPS technology in connection with small-scale, sensor-based treatment of the crop. This data-driven approach to agriculture poses a number of data mining problems. One of those is also an obviously important task in agriculture: yield prediction. Given a precise, geographically annotated data set for a certain field, can a season's yield be predicted? Numerous approaches have been proposed to solving this problem. In the past, classical regression models for non-spatial data have been used, like regression trees, neural networks and support vector machines. However, in a cross-validation learning approach, issues with the assumption of statistical independence of the data records appear. Therefore, the geographical location of data records should clearly be considered while employing a regression model. This paper gives a short overview about the available data, points out the issues with the classical learning approaches and presents a novel spatial cross-validation technique to overcome the problems and solve the aforementioned yield prediction task.