  In recent years, the drive of the Industry 4.0 initiative has enriched
industrial and scientific approaches to build self-driving cars or smart
factories. Agricultural applications benefit from both advances, as they are in
reality mobile driving factories which process the environment. Therefore,
acurate perception of the surrounding is a crucial task as it involves the
goods to be processed, in contrast to standard indoor production lines.
Environmental processing requires accurate and robust quantification in order
to correctly adjust processing parameters and detect hazardous risks during the
processing. While today approaches still implement functional elements based on
a single particular set of sensors, it may become apparent that a unified
representation of the environment compiled from all available information
sources would be more versatile, sufficient, and cost effective. The key to
this approach is the means of developing a common information language from the
data provided. In this paper, we introduce and discuss techniques to build so
called inverse sensor models that create a common information language among
different, but typically agricultural, information providers. These can be
current live sensor data, farm management systems, or long term information
generated from previous processing, drones, or satellites. In the context of
Industry 4.0, this enables the interoperability of different agricultural
systems and allows information transparency.
