大数据技术在智慧农业气象实践中的现状与展望OA

The rapid development of big data technology has provided powerful tools for innovation in agricultural meteorological research and operations.This paper focuses on the"data-scenario-action"closed-loop framework in agrometeorological services.It elucidates the application advantages of big data technology through five representative scenarios:crop growth monitoring,yield prediction,pest and disease forecasting,agricultural disaster warning,and soil moisture monitoring.For crop growth monitoring,big data integrates terabyte-scale datasets from satellite remote sensing,UAV-based multispectral imagery,and ground IoT sensors,enabling dynamic growth analysis from leaf to field scales.In yield prediction,it enables more accurate modeling of weather-crop interactions,supporting yield prediction and risk reduction.For pest and disease forecasting,big data facilitates pattern mining and correlation analysis from massive historical and real-time data,enabling the provision of timely control strategies.Regarding meteorological disaster prediction,big data technology significantly improves spatiotemporal accuracy and reliability by integrating multi-source heterogeneous data.In soil moisture monitoring,big data-driven fusion of remote sensing and in-situ sensor data enables real-time monitoring,accurate prediction,and intelligent management.Finally,the paper highlights future directions to unlock the full potential of big data in agriculture,such as optimizing algorithms,achieving cross-scale data fusion,upgrading real-time processing architectures,and advancing agricultural system intelligence.