Abstract
We propose a microservices-based framework for scalable data analysis in agriculture with IoT integration, leveraging the flexibility and modularity of microservices architecture to build a highly adaptable, maintainable, and efficient data analysis system. This framework allows for faster data processing and carry a diversity of agricultural data analysis tasks while maintaining scalability and fault tolerance. Despite the potential benefits, several challenges and obstacles need to be addressed, such as data integration and standardization, the development of agricultural-specific analytical microservices, and ensuring data security and privacy. Practical application and real-world validation are required to assess the impact of the proposed framework on the agricultural sector and inform future research directions.
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