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2026 Vol.68, Issue 1 Preview Page
Design and Development of a Web-Based System for Monitoring Overseas Grain Crop Conditions Using Satellite Imagery

위성영상을 활용한 국외 곡물 작황 모니터링 웹 기반 시스템 설계 및 구축

Jaeyoung Junga
,
Solhee Kimb
,
Ryoungseob Kwonc
,
Weiguo Yud
,
Pengfei Tange
,
Kyungdo Leef
,
Taegon Kimg†
정 재영a
,
김 솔희b
,
권 령섭c
,
유 웨이궈d
,
탕 펑페이e
,
이 경도f
,
김 태곤g†
aResearcher, Department of Agricultural Engineering, Jeonbuk National University
bAssociate Research Professor, Department of Smart Farm, Jeonbuk National University
cResearcher, Interdisciplinary Program in Landscape Architecture, Integrated Major in Smart City Global Convergence, Seoul National University
dResearcher, Research Institute of Agriculture and Life Sciences, Seoul National University
eResearcher, Research Institute of Agriculture and Life Sciences, Seoul National University
fSenior Researcher, Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration
gAssociate Professor, Department of Agricultural Engineering, Department of Smart Farm, Institute of Agricultural Science & Technology, Jeonbuk National University
†Corresponding author Tel.: +82-63-270-2557 E-mail: taegon@jbnu.ac.kr
2026. pp. 29-41
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Abstract

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
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Information
  • Publisher :The Korean Society of Agricultural Engineers
  • Publisher(Ko) :한국농공학회
  • Journal Title :Journal of Korean Society of Agricultural Engineers
  • Journal Title(Ko) :한국농공학회논문집
  • Volume : 68
  • No :1
  • Pages :29-41
  • Received Date : 2025-09-11
  • Revised Date : 2025-11-03
  • Accepted Date : 2025-11-04
  • DOI :https://doi.org/10.5389/KSAE.2026.68.1.029
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