Global food security is increasingly threatened by climate change, population growth, and market instability. Reliable monitoring of crop conditions in major 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 and annual changes in maize and soybean cultivation. Growth dynamics are assessed using time-series analysis of the NIRv index, enabling real-time comparison with long-term averages and the previous year. A Z-score-based method detects abnormal growth, allowing early identification of crop stress from 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, and datasets. It provides timely, quantitative, and accessible crop information. This study demonstrates the feasibility of an independent crop monitoring system, reducing reliance on overseas platforms such as USDA NASS and EU MARS. The system supports evidence-based food import strategies and strengthens national food security through scientific analysis.



Keywords : Anomaly detection, crop classification, crop monitoring, decision support system, food security, remote sensing