| K-Maryblyt 모델 구동을 위한 FBcastS 정보시스템 개발 |
안문일1 
, 양현지1, 박은우1, 이용환2, 최효원3, 윤성철4
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1(주)에피넷생물환경연구소
2국립농업과학원작물보호과
3농촌진흥청재해대응과
4선문대학교제약생명공학과 |
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| FBcastS: An Information System Leveraging the K-Maryblyt Forecasting Model |
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Mun-Il Ahn1
, Hyeon-Ji Yang1, Eun Woo Park1, Yong Hwan Lee2, Hyo-Won Choi3, Sung-Chul Yun4
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1Agro-environment Research Institute, EPINET Co., Ltd., Anyang 14057, Korea
2Crop Protection Division, National Institute of Agricultural Science, Rural Development Administration, Wanju 55365, Korea
3Disaster Management Division, Rural Development Administration, Jeonju 54875, Korea
4Department of Pharmaceutical Engineering and Biotechnology, Sunmoon University, Asan 31460, Korea |
Correspondence:
Sung-Chul Yun, Tel: +82-41-530-2282, Fax: +82-41-530-2939,
Email: scyun@sunmoon.ac.kr
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Received: July 04, 2024 Revised: August 06, 2024 Accepted: August 16, 2024 |
| Abstract |
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We have developed FBcastS (Fire Blight Forecasting System), a cloud-based information system that leverages the K-Maryblyt forecasting model. The FBcastS provides an optimal timing for spraying antibiotics to prevent flower infection caused by Erwinia amylovora and forecasts the onset of disease symptoms to assist in scheduling field scouting activities. FBcastS comprises four discrete subsystems tailored to specific functionalities: meteorological data acquisition and processing, execution of the K-Maryblyt model, distribution of web-based information, and dissemination of spray timing notifications. The meteorological data acquisition subsystem gathers both observed and forecasted weather data from 1,583 sites across South Korea, including 761 apple or pear orchards where automated weather stations are installed for fire blight forecast. This subsystem also performs post-processing tasks such as quality control and data conversion. The model execution subsystem operates the K-Maryblyt model and stores its results in a database. The web-based service subsystem offers an array of internet-based services, including weather monitoring, mobile services for forecasting fire blight infection and symptoms, and nationwide fire blight monitoring. The final subsystem issues timely notifications of fire blight spray timing alert to growers based on forecasts from the K-Maryblyt model, blossom status, pesticide types, and field conditions, following guidelines set by the Rural Development Administration. FBcastS epitomizes a smart agriculture internet of things (IoT) by utilizing densely collected data with a spatial resolution of approximately 4.25 km to improve the accuracy of fire blight forecasts. The system’s internet-based services ensure high accessibility and utility, making it a vital tool in data-driven smart agricultural practices. |
| Key Words:
Forecast, K-Maryblyt, Smart agriculture |
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