위성영상을 활용한 국외 곡물 작황 모니터링 웹 기반 시스템 설계 및 구축 = Design and Development of a Web-Based System for Monitoring Overseas Grain Crop Conditions Using Satellite Imagery

다국어 초록 (Multilingual Abstract)

Global food security is increasingly threatened by climate change, population growth, and market instability. Reliable monitoring of crop conditions inmajor production regions is essential for food supply planning. This study develops a web-based system to monitor maize and soybean in the U.S.
Corn Belt, a key source of Korea's import crops. The system integrates four core processes: crop classification, growth monitoring, anomaly detection,and visualization. Multi-spectral satellite imagery forms the primary data source. Deep learning-based classification quantifies spatial distribution andannual changes in maize and soybean cultivation. Growth dynamics are assessed using time-series analysis of the NIRv index, enabling real-timecomparison with long-term averages and the previous year. A Z-score-based method detects abnormal growth, allowing early identification of crop stressfrom drought, flooding, or pests.
Results are delivered through a web-based dashboard with intuitive visualization for users. The platform can be extended to other countries, crops, anddatasets. It provides timely, quantitative, and accessible crop information. This study demonstrates the feasibility of an independent crop monitoringsystem, reducing reliance on overseas platforms such as USDA NASS and EU MARS. The system supports evidence-based food import strategies andstrengthens national food security through scientific analysis.