  Large scale monitoring systems, enabled by the emergence of networked
embedded sensing devices, offer the opportunity of fine grained online
spatio-temporal collection, communication and analysis of physical parameters.
Various applications have been proposed and validated so far for environmental
monitoring, security and industrial control systems. One particular application
domain has been shown suitable for the requirements of precision agriculture
where such systems can improve yields, increase efficiency and reduce input
usage. We present a data analysis and processing approach for distributed
monitoring of crops and soil where hierarchical aggregation and modelling
primitives contribute to the robustness of the network by alleviating
communication bottlenecks and reducing the energy required for redundant data
transmissions. The focus is on leveraging the fog computing paradigm to exploit
local node computing resources and generate events towards upper decision
systems. Key metrics are reported which highlight the improvements achieved. A
case study is carried out on real field data for crop and soil monitoring with
outlook on operational and implementation constraints.
