Clustering-regression-ordering steps for knowledge discovery in spatial databases

Precision agriculture is a new approach to farming in which environmental characteristics at a sub-field level are used to guide crop production decisions. Instead of applying management actions and production inputs uniformly across entire fields, they are varied to match site-specific needs. A first step in this process is to define spatial regions having similar characteristics and to build local regression models describing the relationship between field characteristics and yield. From these yield prediction models, one can then determine optimum production input levels. Discovery of “similar” regions in fields is done by applying the DBSCAN clustering algorithm on data from more than one field ignoring spatial attributes (x and y coordinates) and the corresponding yield values. Using these models, constructed on training field regions of obtained clusters, we aim to achieve better prediction on identified regions than using global prediction models. The experimental results on real life agriculture data show observable improvements in prediction accuracy, although there are many unresolved issues in applying the proposed method in practice.