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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Health Monitoring of Livestock using Neck Sensor based on Machine Learning

목걸이형 센서를 이용한 머신러닝 기반 가축상태 모니터링

  • Lee, Woongsup (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University) ;
  • Park, Seongmin (Dairy Science Division, National Institute of Animal science, Rural Development Administration) ;
  • Ban, Tae-Won (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University) ;
  • Kim, Seong Hwan (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University) ;
  • Ryu, Jongyeol (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University) ;
  • Sung, Kil-Young (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
  • Received : 2018.09.17
  • Accepted : 2018.10.10
  • Published : 2018.11.30
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Abstract

Due to the rapid development of Internet-of-Things technology, different types of smart sensors are now devised and deployed widely. These smart sensors are now used in animal husbandry which was traditionally managed by the experience of farmers, such that wearable sensors for livestock, and the smart farm which is equipped with multiple sensors are utilized to increase the efficiency of livestock management. Herein, we consider a scheme in which the body temperature and the level of activity are measured by smart sensor which is attached to the neck of dairy cattle and the health condition is monitored based on collected data. Especially, we find that the estrous of dairy cattle which is one of most important metric in milk production, can be predicted with high precision using various machine learning techniques. By utilizing the proposed prediction scheme, estrous of cattle can be detected immediately and this can improve the efficiency of cattle management.

사물 인터넷 기술의 급속한 발전으로 다양한 종류의 스마트 센서들이 개발 보급되고 있다. 이러한 스마트 센서들은 주로 관리자의 경험에 의해서 관리되던 축산업에도 최근 적용되어 가축 개체에 웨어러블 센서를 달거나 사물인터넷 센서를 갖춘 스마트팜 사용을 통해서 가축관리의 효율성을 향상시키고 있다. 본 논문에서는 목걸이형 스마트 센서를 이용하여 젖소의 체온과 운동량을 측정하고 이를 기반으로 개체의 상태를 파악하는 방안을 개발하였다. 특히 젖소 관리에서 제일 중요한 요소인 젖소의 발정여부를 파악하는 방안을 다양한 머신러닝 방법을 이용하여 분석하였고 이를 통해서 높은 정확도로 발정여부를 예측할 수 있음을 보였다. 제안한 방안의 사용을 통해서 젖소의 발정여부를 빠르게 확인하고 이를 통해서 젖소 관리의 효율성을 향상시킬 수 있다.

Keywords

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Fig. 1 Wearable sensor for dairy cow by SCR.

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Fig. 2 Wearable sensor for dairy cow to predict estrous.

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Fig. 3 Scatter plot of Body temperature, Average movement and Estrous.

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Fig. 4 Hexagonal graph of body temperature, average movement and probability of estrous.

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Fig. 5 Performance comparison of regression algorithms for estrous prediction.

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Fig. 6 Performance comparison of classification algorithms for estrous prediction.

Table. 1 Attribute of data for dairy cattle.

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Table. 2 Accuracy of regression algorithms for estrous prediction.

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Table. 3 Accuracy of classification algorithms for estrous prediction.

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