Precision agrometeorology: Integrating AI and IoT for real-time agro-advisories

Precision agrometeorology leverages high-resolution weather and climate data through IoT sensor networks and advanced AI analytics to provide timely, site-specific farming advice. By combining in-situ microclimate sensors, remote sensing and cloud/edge computing, farmers can monitor soil moisture, temperature, humidity and crop health in real time. Machine learning and deep learning models analyze these data streams to forecast irrigation needs, pest and disease risk and yield outcomes. The AI-enhanced system enables adaptive management e.g., optimizing water and fertilizer use and issuing frost or storm alerts thus enhancing resource efficiency, productivity and climate resilience. Challenges include network connectivity in rural areas, data quality and interoperability, but emerging solutions (edge computing, federated learning, blockchain) are addressing them. This review outlines key IoT sensor technologies and AI methods (Table 1, Table 2), describes architectures for data flow from field to advisory (Figure 1) and discusses applications (Table 3, Table 4) and future directions for global precision agrometeorology.