Vol. 9, Special Issue 10, Part X (2025)

Precision agro-meteorology integrates advanced technologies such as statistical forecasting models, artificial intelligence (AI), and the Internet of Things (IoT) to provide real-time, data-driven agricultural advisories. This review explores how these tools enhance decision-making by delivering accurate, site-specific weather predictions and actionable insights for farmers. Statistical models, including time-series analysis and regression-based techniques, leverage historical and real-time meteorological data to forecast weather variables critical to crop management. AI tools, such as machine learning and deep learning, process complex datasets from IoT-enabled sensors, satellites, and weather stations to predict microclimate conditions, pest outbreaks, and irrigation needs. IoT facilitates seamless data collection and transmission, enabling real-time advisories tailored to specific fields. This article reviews methodologies, including data acquisition through IoT networks, AI-driven forecasting models, and their integration for precision agriculture. Results from global case studies demonstrate improved crop yields, reduced resource waste, and enhanced resilience to climate variability. Challenges such as data interoperability, scalability, and accessibility for smallholder farmers are discussed, alongside future prospects for democratizing these technologies. This review underscores the transformative potential of precision agro-meteorology in optimizing agricultural productivity and sustainability.