Research on Key Technologies for Intelligent Monitoring and Early Warning of Agricultural Disasters Based on Multi source Meteorological Data Fusion

Abstract

In response to the frequent occurrence of agricultural meteorological disasters in China, insufficient monitoring accuracy from a single data source, low timeliness and spatial resolution of early warning, and difficulties in identifying multiple disaster types, this article focuses on the full chain of multi-source meteorological data fusion, intelligent disaster identification, precise early warning, and business application. Construct an integrated multi-source meteorological data acquisition system for space-based, space-based, and ground-based systems, propose spatiotemporal registration and quality control methods, and design a multimodal data fusion algorithm based on dynamic weights and deep learning; Develop intelligent identification models for major agricultural disasters such as drought, floods, low-temperature freezing damage, dry hot winds, and pests and diseases, and establish a dynamic assessment index system for disaster risks by crop, growth period, and region; Build a seamless intelligent early warning model and multi-level early warning release mechanism for long, medium, and short periods, forming a technical system that integrates data fusion, monitoring and identification, risk assessment, early warning release, and decision support. The fusion framework is compatible with satellite remote sensing, weather radar and farmland sensor networks.