Precision Agriculture has become an emerging topic over the last ten years. It is concerned with the integration of information technology into agricultural processes. This is especially true for the ongoing and growing data collection in agriculture. Novel ground-based sensors, aerial and satellite imagery as well as soil sampling provide large georeferenced data sets with high spatial resolution. However, these data lead to the data mining problem of finding novel and useful information in these data sets. One of the key tasks in the area of precision agriculture is management zone delineation: given a data set of georeferenced data records with high spatial resolution, we would like to discover spatially mostly contiguous zones on the field which exhibit similar characteristics within the zones and different characteristics between zones. From a data mining point of view, this task comes down to a variant of spatial clustering with a constraint of keeping the resulting clusters spatially mostly contiguous. This article presents a novel approach tailored to the specifics of the available data, which do not allow for using an existing algorithm. A variant of hierarchical agglomerative clustering will be presented, in conjunction with a spatial constraint. Results on available multi-variate data sets and subsets will be presented.
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