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
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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
Spatial variable importance assessment for yield prediction in precision agriculture
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
GPS-based framework towards more realistic and real-time construction equipment operation simulation
Proceedings of the Winter Simulation Conference
An Intelligent Decision Support System for Cropland Suitability Evaluation
Journal of Integrated Design & Process Science
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Precision Agriculture is the application of state-of-the-art GPS technology in connection with site-specific, sensor-based treatment of the crop. It can also be described as a data-driven approach to agriculture, which is strongly connected with a number of data mining problems. One of those is also an inherently important task in agriculture: yield prediction. The question is: can a field's yield be predicted in-season using available geo-coded data sets?. In the past, a number of approaches have been proposed towards this problem. Often, a broad variety of regression models for non-spatial data have been used, like regression trees, neural networks and support vector machines. But in a cross-validation learning approach, issues with the assumption of the data records' statistical independence keep emerging. Hence, the geographical location of data records should clearly be considered while establishing a regression model and assessing its predictive performance. This paper gives a short overview of the available data, points out in detail the main issue with the classical learning approaches and presents a novel spatial cross-validation technique to overcome the problems with the classical approach towards the aforementioned yield prediction task.