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Development of Framework for Onion Yield Prediction using Ensemble Learning Technique KCI 등재
앙상블 학습을 활용한 양파 생산량 예측 프레임워크 개발
- 언어KOR
- URLhttps://db.koreascholar.com/Article/Detail/438210
pp.215-222
Rapidly changing environmental factors due to climate change are increasing the uncertainty of crop growth, and the importance of crop yield prediction for food security is becoming increasingly evident in Republic of Korea. Traditionally, crop yield prediction models have been developed by using statistical techniques such as regression models and correlation analysis. However, as machine learning technique develops, it is able to predict the crop yield more accurate than the statistical techniques. This study aims at proposing the onion yield prediction framework to accurately predict the onion yield by using various environmental factor data. Temperature, humidity, precipitation, solar radiation, and wind speed are considered as climate factors and irrigation water and nitrogen application rate are considered as soil factors. To improve the performance of the prediction model, ensemble learning technique is applied to the proposed framework. The coefficient of determination of the proposed stacked ensemble framework is 0.96, which is a 24.68% improvement over the coefficient of determination of 0.77 of the existing single machine learning model. This framework can be applied to the particular farmland so that each farm can get their customized prediction model, which is visualized by the web system.
2. 배경이론
2.1 머신러닝을 활용한 작물 생산량 예측
2.2 스태킹 앙상블
3. 앙상블 학습을 활용한 양파 생산량 예측프레임워크
3.1 데이터베이스 모듈
3.2 작물 생산량 예측 모듈
3.3 웹 시스템 모듈
4. 실 험
4.1 실험 데이터
4.2 실험 결과
5. 결 론
Acknowledgement
References
