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
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
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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Precision Agriculture applies state-of-the-art GPS technology in connection with site-specific, sensor-based crop management. 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. Given a yield prediction model, which of the predictor variables are the important ones? In the past, a number of approaches have been proposed towards this problem. For yield prediction, a broad variety of regression models for non-spatial data can be adapted for spatial data using a novel spatial cross-validation technique. Since this procedure is at the core of variable importance assessment, it will be briefly introduced here. Given this spatial yield prediction model, a novel approach towards assessing a variable’s importance will be presented. It essentially consists of picking each of the predictor variables, one at a time, permutating its values in the test set and observing the deviation of the model’s RMSE. This article uses two real-world data sets from precision agriculture and evaluates the above procedure.