Highland farming is agriculture that takes place 400 m above sea level and typically involves both low temperatures and long sunshine hours. Most highland Chinese cabbages are harvested in the Gangwon province. The Ubiquitous Sensor Network (USN) has been deployed to observe Chinese cabbages growth because of the lack of installed weather stations in the highlands. Five representative Chinese cabbage cultivation spots were selected for USN and meteorological data collection between 2015 and 2017. The purpose of this study is to develop a weight prediction model for Chinese cabbages using the meteorological and growth data that were collected one week prior. Both a regression and random forest model were considered for this study, with the regression assumptions being satisfied. The Root Mean Square Error (RMSE) was used to evaluate the predictive performance of the models. The variables influencing the weight of cabbage were the number of cabbage leaves, wind speed, precipitation and soil electrical conductivity in the regression model. In the random forest model, cabbage width, the number of cabbage leaves, soil temperature, precipitation, temperature, soil moisture at a depth of 30 cm, cabbage leaf width, soil electrical conductivity, humidity, and cabbage leaf length were screened. The RMSE of the random forest model was 265.478, a value that was relatively lower than that of the regression model (404.493); this is because the random forest model could explain nonlinearity.

Abstract
1. 서 론
2. 연구 방법
    2.1. 유비쿼터스 네트워크 장비 (USN)
    2.2. 회귀모형
    2.3. 랜덤 포레스트
    2.4. 연구 자료
3. 결과 및 고찰
4. 결 론
REFERENCES

• 권태용(대구대학교 일반대학원 통계학과) | Taeyong Kwon (Department of Statistics, Daegu University)
• 김래용(대구대학교 수리빅데이터학부) | Rae Yong Kim (Division of Mathematics and big data science, Daegu University)
• 윤상후(대구대학교 수리빅데이터학부) | Sanghoo Yoon (Division of Mathematics and big data science, Daegu University) Corresponding author