Smart Media Journal (스마트미디어저널)

THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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A study on the impact on predicted soil moisture based on machine learning-based open-field environment variables

머신러닝 기반 노지 환경 변수에 따른 예측 토양 수분에 미치는 영향에 대한 연구

  • 정광훈 (순천대학교 정보통신공학전공 ) ;
  • 이명훈 (순천대학교 스마트농업전공 )
  • Received : 2023.10.31
  • Accepted : 2023.11.21
  • Published : 2023.11.30
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Abstract

As understanding sudden climate change and agricultural productivity becomes increasingly important due to global warming, soil moisture prediction is emerging as a key topic in agriculture. Soil moisture has a significant impact on crop growth and health, and proper management and accurate prediction are key factors in improving agricultural productivity and resource management. For this reason, soil moisture prediction is receiving great attention in agricultural and environmental fields. In this paper, we collected and analyzed open field environmental data using a pilot field through random forest, a machine learning algorithm, obtained the correlation between data characteristics and soil moisture, and compared the actual and predicted values of soil moisture. As a result of the comparison, the prediction rate was about 92%. It was confirmed that the accuracy was . If soil moisture prediction is carried out by adding crop growth data variables through future research, key information such as crop growth speed and appropriate irrigation timing according to soil moisture can be accurately controlled to increase crop quality and improve productivity and water management efficiency. It is expected that this will have a positive impact on resource efficiency.

지구 온난화로 인해 갑작스러운 기후변화와 농업 생산성에 대한 이해가 점점 중요해지면서, 토양 수분 예측은 농업에서 핵심 주제로 떠오르고 있다. 토양 수분은 농작물의 성장과 건강에 큰 영향을 미치며, 적절한 관리와 정확한 예측은 농업 생산성 향상과 자원 관리의 핵심 요소이다. 이러한 이유로 토양 수분 예측은 농업 및 환경 분야에서 큰 주목을 받고 있다. 본 논문에서는 머신러닝 알고리즘인 랜덤 포레스트를 통하여 시범포를 이용하여 노지 환경 데이터를 수집하고 분석하여 데이터 특성들과 토양 수분의 상관관계를 구하고 토양 수분 실제 값과 예측값을 비교하였으며 비교 결과 예측률이 약 92%의 정확성을 갖는다는 것을 확인하였다. 추후 연구를 통해 작물의 생장 데이터 변수들을 추가하여 토양 수분 예측을 진행한다면 토양 수분에 따른 작물의 생장 속도, 적절한 관수 타이밍 등의 주요 정보를 정확하게 제어함으로써 작물의 품질 상승, 물 관리 효율 증가 등 생산성 및 자원 효율성에 좋은 영향을 미칠 것이라고 기대된다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터사업의 연구결과로 수행되었음 (RS-2023-00259703)

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