한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제20권12호
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  • Pages.2348-2354
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  • 2016
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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의사결정트리를 이용한 돈사 환경데이터와 일당증체 간의 연관성 분석 모델 개발

Development of a model to analyze the relationship between smart pig-farm environmental data and daily weight increase based on decision tree

  • Han, KangHwi (Department of Information and Communication Engineering, Gyeongsang National University) ;
  • Lee, Woongsup (Department of Information and Communication Engineering, Gyeongsang National University) ;
  • Sung, Kil-Young (Department of Information and Communication Engineering, Gyeongsang National University)
  • 투고 : 2016.09.22
  • 심사 : 2016.10.03
  • 발행 : 2016.12.31
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초록

최근 농업분야에서 IoT(Internet of Things)기술을 통해 다양한 생체 및 환경 정보를 DB(data base)로 구축할 수 있게 되면서 빅 데이터를 이용한 기계학습 분석이 증가하고 있다. 기계학습 분석을 통해 농업의 생산량과 가축의 질병 등을 예측할 수 있게 되어 농업경영에서 효율적인 의사결정을 돕는다. 본 논문에서는 스마트 돈사의 다양한 환경데이터와 몸무게데이터를 이용하여 환경정보와 일당증체의 연관성 모델을 도출하고 그 정확도를 분석하였다. 이를 위해 기계학습의 M5P tree기법을 적용하였다. 분석을 통해 일당증체량이 풍속에 큰 영향을 받는 것을 확인하였다.

In recent days, IoT (Internet of Things) technology has been widely used in the field of agriculture, which enables the collection of environmental data and biometric data into the database. The availability of big data on agriculture results in the increase of the machine learning based analysis. Through the analysis, it is possible to forecast agricultural production and the diseases of livestock, thus helping the efficient decision making in the management of smart farm. Herein, we use the environmental and biometric data of Smart Pig farm to derive the accurate relationship model between the environmental information and the daily weight increase of swine and verify the accuracy of the derived model. To this end, we applied the M5P tree algorithm of machine learning which reveals that the wind speed is the major factor which affects the daily weight increase of swine.

키워드

참고문헌

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피인용 문헌

  1. 축사에서 딥러닝을 이용한 질병개체 파악방안 vol.21, pp.5, 2016, https://doi.org/10.6109/jkiice.2017.21.5.1009
  2. 기계학습을 이용한 돈사 급수량 예측방안 개발 vol.21, pp.8, 2016, https://doi.org/10.6109/jkiice.2017.21.8.1560
  3. Prediction of average daily gain of swine based on machine learning vol.36, pp.2, 2016, https://doi.org/10.3233/jifs-169869
  4. 기계학습기반 양돈생산성 예측방안 vol.24, pp.1, 2016, https://doi.org/10.6109/jkiice.2020.24.1.130