Cloud-Based Top-Down and Bottom-Up Approach for Agriculture Data Integration - Paulstin K. C., Abhilash Sam; Prasanna G., Angeline (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The big data revolution and the growth of information technology have had a profound influence on many areas of our life. Many farms today employ precision farming techniques and capture enormous volumes of data. Making the most of these datasets for decision support requires integrating data from several sources, doing analysis rapidly, and generating conclusions based on the results. In order to analyse agricultural data from several sources, this study presents a framework that utilises cloud-computing. This approach is more scalable, adaptable, cost-effective, and easy to maintain than existing alternatives. Based on extensive interviews, surveys, and literature reviews, the framework offers a workable architecture for cloud-based services for data integration, analysis, and visualisation. This skeleton architecture was used to construct many programmes; as we learned more and faced tougher problems, we tweaked the plan to make it work better. We demonstrate the framework's value with several example applications. Each use case has its own specific requirements for data integration; therefore, it makes use of a different set of services from the suggested architecture.