Evidence map›Paper›PMID 40285052›Full record

SynthesisSensors (Basel, Switzerland)2025

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

Emmanuel Ahoa, Ayalew Kassahun, Cor Verdouw, Bedir Tekinerdogan

Abstract readSystematic Review
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Emmanuel AhoaInformation Technology Group, Wageningen University and Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands.ORCID 0000-0001-7285-1945
Ayalew KassahunInformation Technology Group, Wageningen University and Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands.ORCID 0000-0003-1066-7127
Cor VerdouwInformation Technology Group, Wageningen University and Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands.
Bedir TekinerdoganInformation Technology Group, Wageningen University and Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands.ORCID 0000-0002-8538-7261

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AgricultureInformation SystemsArtificial IntelligenceHumansdata interoperabilityontologysemantic interoperabilitysmart farmingsmart systemssystem of systems

Identifiers

PMID40285052
PMCPMC12031551

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.