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. 2025 Apr 8;25(8):2362.
doi: 10.3390/s25082362.

Challenges and Solution Directions for the Integration of Smart Information Systems in the Agri-Food Sector

Emmanuel Ahoa  1 , Ayalew Kassahun  1 , Cor Verdouw  1   2 , Bedir Tekinerdogan  1
Affiliations

Challenges and Solution Directions for the Integration of Smart Information Systems in the Agri-Food Sector

Emmanuel Ahoa et al. Sensors (Basel). .

Abstract

Traditional farming has evolved from standalone computing systems to smart farming, driven by advancements in digitalization. This has led to the proliferation of diverse information systems (IS), such as IoT and sensor systems, decision support systems, and farm management information systems (FMISs). These systems often operate in isolation, limiting their overall impact. The integration of IS into connected smart systems is widely addressed as a key driver to tackle these issues. However, it is a complex, multi-faceted issue that is not easily achievable. Previous studies have offered valuable insights, but they often focus on specific cases, such as individual IS and certain integration aspects, lacking a comprehensive overview of various integration dimensions. This systematic review of 74 scientific papers on IS integration addresses this gap by providing an overview of the digital technologies involved, integration levels and types, barriers hindering integration, and available approaches to overcoming these challenges. The findings indicate that integration primarily relies on a point-to-point approach, followed by cloud-based integration. Enterprise service bus, hub-and-spoke, and semantic web approaches are mentioned less frequently but are gaining interest. The study identifies and discusses 27 integration challenges into three main areas: organizational, technological, and data governance-related challenges. Technologies such as blockchain, data spaces, AI, edge computing and microservices, and service-oriented architecture methods are addressed as solutions for data governance and interoperability issues. The insights from the study can help enhance interoperability, leading to data-driven smart farming that increases food production, mitigates climate change, and optimizes resource usage.

Keywords: data interoperability; ontology; semantic interoperability; smart farming; smart systems; system of systems.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Activity diagram showing SLR process (adapted from [57]).
Figure 2
Figure 2
Overview of search result and study selection procedure.
Figure 3
Figure 3
Quality score distribution of 74 primary studies.
Figure 4
Figure 4
Feature model showing dimensions of IS integration in agri-food sector.
Figure 5
Figure 5
Number of articles published per year.
Figure 6
Figure 6
Top eight publication sources.
Figure 7
Figure 7
Number of papers per publisher.
Figure 8
Figure 8
Top ten most cited papers.
Figure 9
Figure 9
The top ten countries that contributed three or more papers to the studies.
Figure 10
Figure 10
Occurrences of integration types addressed by analyzed papers (see references in Appendix C).
Figure 11
Figure 11
Overview of integration approaches per year (occurrences of integration approaches).
Figure 12
Figure 12
Distribution of integration challenges addressed over years (occurrences per challenge).
See this image and copyright information in PMC

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