한국환경과학회지 제28권 제8호 (p.701-707)

|TECHNICAL NOTE|
기상 및 토양정보가 고랭지배추 단수예측에 미치는 영향

The Effect of Highland Weather and Soil Information on the Prediction of Chinese Cabbage Weight
키워드 :
Chinese cabbage,Random forest,Regression model,Ubiquitous sensor network

목차

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

초록

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.