This article outlines the design and development of a farm management information system built on microservices, cloud computing, and IoT technologies applied to a maize cultivation use case. The proposed solution integrates specialized modules for sensor data collection, advanced analytics and yield prediction, delivering real-time decision support for agricultural operations. Its microservice-based architecture provides scalability, modularity, and straightforward integration with other systems, while field data are processed to automatically generate recommendations and alerts. A core component is the prediction module, which leverages AI algorithms XGBoost and Random Forest to estimate crop yield, drawing on inputs from weather, soil, and other sensors. This intelligent farm management information system transforms traditional agriculture into a data-driven process with informed decisions and proactive interventions.