  Current automation approaches in the Industry 4.0 have generated increased
interest in the utilization of Integration Platforms as a Service (iPaaS) cloud
architectures in order to unify and synchronize several systems, applications,
and services in order to build smart solutions for automated and adaptive
industrial process management. Existing iPaaS solutions present several
out-of-the-box connectors and automation engines for easier integration of
customers' projects, but show issues regarding overall adaptation outside their
scope, brand locking, and occasionally high prices. Moreover, existing
platforms fail to respond adequately to the needs of deploying multiple
decision models capable of offering automated or semi-automated management of
processes, thanks to the integration of the large diversity of data and event
sources as well as the different physical or logical action entities. With the
popularization of open-source software and applications such as BPM Engines,
Machine Learning libraries, and Integration suites and libraries, it is
possible to develop a fully customizable and adaptable, open-source iPaaS that
can be used both in and outside industrial applications. In this paper, we
propose a generic iPaaS architecture implemented on the basis of several open
source solutions boasting integration, interoperability, and automated
decision-making capabilities in the domain of Agriculture 4.0. A
proof-of-concept based on these solutions is presented, as well as a case study
on MA{\"I}SADOUR's grain storage process with a comparison with the currently
human-operated tasks.
