Smart Media Journal (스마트미디어저널)

THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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A Comparative Study Between Linear Regression and Support Vector Regression Model Based on Environmental Factors of a Smart Bee Farm

  • Rahman, A. B. M. Salman (Department of Information and Communication Engineering at Sunchon National University) ;
  • Lee, MyeongBae (Department of Information and Communication Engineering at Sunchon National University) ;
  • Venkatesan, Saravanakumar (Department of Information and Communication Engineering, Sunchon National University) ;
  • Lim, JongHyun (Information and Communication Engineering at Sunchon University) ;
  • Shin, ChangSun (Dept. of Information & Communication Engineering in Sunchon National University)
  • Received : 2022.05.06
  • Accepted : 2022.06.29
  • Published : 2022.06.30
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Abstract

Honey is one of the most significant ingredients in conventional food production in different regions of the world. Honey is commonly used as an ingredient in ethnic food. Beekeeping is performed in various locations as part of the local food culture and an occupation related to pollinator production. It is important to conduct beekeeping so that it generates food culture and helps regulate the regional environment in an integrated manner in preserving and improving local food culture. This study analyzes different types of environmental factors of a smart bee farm. The major goal of this study is to determine the best prediction model between the linear regression model (LM) and the support vector regression model (SVR) based on the environmental factors of a smart bee farm. The performance of prediction models is measured by R2 value, root mean squared error (RMSE), and mean absolute error (MAE). From all analysis reports, the best prediction model is the support vector regression model (SVR) with a low coefficient of variation, and the R2 values for Farm inside temperature, bee box inside temperature, and Farm inside humidity are 0.97, 0.96, and 0.44.

Keywords

Acknowledgement

This work was supported by a Research promotion program of SCNU

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