A Cloud-Based Framework for Agricultural Data Integration: A Top-Down-Bottom-Up Approach

Anat Goldstein, Lior Fink, Gilad Ravid

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


In recent years, growing use of information technology (IT) and the big data revolution in agriculture have led many farms to adopt precision agriculture methods and to accumulate large amounts of data. Exploiting these data for effective decision support requires the ability to integrate data from various sources, to conveniently analyze the data, and to infer valuable insights. This paper presents a framework for integrating and analyzing agricultural data from various sources, which leverages cloud-computing, thereby contributing to the scalability, flexibility, affordability, and maintainability of the solution compared to existing solutions. The framework defines a functional infrastructure of cloud-based services that facilitate integration, analysis, and data visualization. These services can be either end-user applications or services intended as a platform for creating new services. To design the framework's architecture, we applied a top-down and bottom-up approach. Based on the top-down analysis of information collected through interviews, questionnaires, and literature review, we defined an initial architecture of the framework. We then used this initial architecture to develop different applications, and based on the experience and insights gained and new requirements that were faced, the architecture was revised in an iterative process. We demonstrate the application of the framework through several use cases. Each use case represents different data integration requirements and is based on different services of the proposed framework.

Original languageEnglish
Pages (from-to)88527-88537
Number of pages11
JournalIEEE Access
StatePublished - 2022


  • Agricultural services
  • cloud computing
  • cloud services
  • data integration
  • framework
  • precision agriculture


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