Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Optimization of Growth Environments Based on Meteorological and Environmental Sensor Data

기상 및 환경 센서 데이터 기반 생육 환경 최적화 연구

  • Sook Lye Jeon (ABCLABS Inc.) ;
  • Jinheung Lee (ABCLABS Inc.) ;
  • Sung Eok Kim (Agricultural Corporation ParmFarm Co., Ltd) ;
  • Jeonghwan Park (ABCLABS Inc.)
  • 전숙례 ((주)에이비씨랩스) ;
  • 이진흥 ((주)에이비씨랩스) ;
  • 김성억 (농업회사법인 팜팜 (주)) ;
  • 박정환 ((주)에이비씨랩스)
  • Received : 2024.07.08
  • Accepted : 2024.07.22
  • Published : 2024.07.31
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Abstract

This study aimed to analyze the environmental factors affecting tomato growth by examining the correlation between weather and growth environment sensor data from P Smart Farm located in Gwangseok-myeon, Nonsan-si, Chungcheongnam-do. Key environmental variables such as the temperature, humidity, sunlight hours, solar radiation, and daily light integral (DLI) significantly affect tomato growth. The optimal temperature and DLI conditions play crucial roles in enhancing tomato growth and the photosynthetic efficiency. In this study, we developed a model to correct and predict the time-series variations in internal environmental sensor data using external weather sensor data. A linear regression analysis model was employed to estimate the external temperature variations and internal DLI values of P Smart Farm. Then, regression equations were derived based on these data. The analysis verified that the estimated variations in external temperature and internal DLI are explained effectively by the regression models. In this research, we analyzed and monitored smart-farm growth environment data based on weather sensor data. Thereby, we obtained an optimized model for the temperature and light conditions crucial for tomato growth. Additionally, the study emphasizes the importance of sensor-based data analysis in dynamically adjusting the tomato growth environment according to the variations in weather and growth conditions. The observations of this study indicate that analytical solutions using public weather data can provide data-driven operational experiences and productivity improvements for small- and medium-sized facility farms that cannot afford expensive sensors.

Keywords

Acknowledgement

본 연구는 중소벤처기업부 중소기업 연구인력지원사업 고경력 연구인력채용지원사업(2021~2024년, 과제 번호 S3099934)의 지원으로 수행된 연구임.

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