Abstract
In the first part of this article, the main results from applying data mining methods and algorithms to spatial precision agriculture data sets will be outlined. In particular, the task of yield prediction will be handled as a spatial regression problem. To account for the spatial nature of the data sets, a few modeling pitfalls resulting from spatial autocorrelation will be tackled. Based on a cross-validation approach, the yield prediction setting will be used to determine spatial variable importance. Another task called management zone delineation will be briefly outlined. A novel hierarchical spatially constrained clustering algorithm will be presented which aims to provide a tradeoff between spatial contiguity of the resulting clusters and cluster similarity. These two tasks are a summary of [26]. In the second part of this article, the emerging field of environmental data mining will be briefly laid out.
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Ruß, G. (2013). From Spatial Data Mining in Precision Agriculture to Environmental Data Mining. In: Moewes, C., Nürnberger, A. (eds) Computational Intelligence in Intelligent Data Analysis. Studies in Computational Intelligence, vol 445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32378-2_18
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DOI: https://doi.org/10.1007/978-3-642-32378-2_18
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