Ahoa Emmanuel, Kassahun Ayalew, Verdouw Cor, Tekinerdogan Bedir
Information Technology Group, Wageningen University and Research, Hollandseweg 1, 6706 KN Wageningen, The Netherlands.
Wageningen Social and Economic Research, Wageningen University and Research, Droevendaalsesteeg 4, 6708 PB Wageningen, The Netherlands.
Sensors (Basel). 2025 Apr 8;25(8):2362. doi: 10.3390/s25082362.
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.
在数字化进步的推动下,传统农业已从独立的计算系统发展到智能农业。这导致了各种信息系统(IS)的激增,如物联网和传感器系统、决策支持系统以及农场管理信息系统(FMIS)。这些系统往往孤立运行,限制了它们的整体影响。将信息系统集成到互联的智能系统中被广泛认为是解决这些问题的关键驱动力。然而,这是一个复杂的、多方面的问题,不容易实现。以往的研究提供了有价值的见解,但它们往往侧重于特定案例,如单个信息系统和某些集成方面,缺乏对各种集成维度的全面概述。这项对74篇关于信息系统集成的科学论文的系统综述通过概述所涉及的数字技术、集成水平和类型、阻碍集成的障碍以及克服这些挑战的可用方法,填补了这一空白。研究结果表明,集成主要依赖点对点方法,其次是基于云的集成。企业服务总线、轮毂辐条式和语义网方法提及较少,但正受到关注。该研究将27项集成挑战识别并讨论为三个主要领域:组织、技术和数据治理相关挑战。区块链、数据空间、人工智能、边缘计算和微服务等技术以及面向服务的架构方法被视为数据治理和互操作性问题的解决方案。该研究的见解有助于提高互操作性,从而实现数据驱动的智能农业,增加粮食产量、缓解气候变化并优化资源利用。