한국소프트웨어감정평가학회 논문지 (Journal of Software Assessment and Valuation)

  • 제17권1호
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  • Pages.75-82
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  • 2021
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  • 2092-8114(pISSN)
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  • 2733-4384(eISSN)
한국소프트웨어감정평가학회 (Korea Software Assessment and Valuation Society)
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다변량 데이터와 순환 신경망을 이용한 젖소의 유방염 진단예측 방법

Method for predicting the diagnosis of mastitis in cows using multivariate data and Recurrent Neural Network

  • 박기철 (중앙대학교 컴퓨터공학과) ;
  • 이성훈 (중앙대학교 컴퓨터공학과) ;
  • 박재화 (중앙대학교 소프트웨어학과)
  • 투고 : 2021.06.03
  • 심사 : 2021.06.20
  • 발행 : 2021.06.30
⟨ 이전 논문 다음 논문 ⟩

초록

젖소에 있어 유방염은 농가의 낙농 생산성을 저해하는 주된 요인이며 이를 해결하기 위해 지난동안 폭넓은 연구가 이루어졌다. 하지만 유방염에 대한 연구는 사후 진단에 국한되어왔으며 이마저도 단일 센서를 활용하는 것이 주류이다. 본 연구에서는 생체 데이터와 환경 데이터를 이용하여 다음 날의 유방염 발병여부를 예측하는 모델을 개발하였다. 데이터는 충청남도 농가에 설치된 착유기와 센서들로부터 수집되었으며 3주간의 데이터를 다변량 데이터로 구성하였다. 유방염 진단예측을 위해 순환 신경망 모델을 사용하였고, 그 결과 유방염을 82.9%의 정확도로 예측하였다. 데이터 수집 기간을 다양하게 하여 예측 성능을 비교하였고 여러 모델과 성능을 비교하여 모델의 우수성을 확인하였다.

Mastitis in cows is a major factor that hinders dairy productivity of farms, and many attempts have been made to solve it. However, research on mastitis has been limited to diagnosis rather than prediction, and even this is mostly using a single sensor. In this study, a predictive model was developed using multivariate data including biometric data and environmental data. The data used for the analysis were collected from robot milking machines and sensors installed in farmhouses in Chungcheongnam-do, South Korea. The recurrent neural network model using three weeks of data predicts whether or not mastitis is diagnosed the next day. As a result, mastitis was predicted with an accuracy of 82.9%. The superiority of the model was confirmed by comparing the performance of various data collection periods and various models.

키워드

과제정보

본 결과물은 농림축산식품부의 재원으로 첨단생산기술개발사업의 지원을 받아 연구되었음(318005-4).

참고문헌

  1. 농림축산식품부, 농협경제지주 젖소개량사업소, "2019년도 한국 유우군 능력검정 사업보고서", 2019.
  2. S.Tamminga, "Nutrition Management of Dairy Cows as a Contribution to Pollution Control", Journal of Dairy Science, vol. 75, Issue 1, 345-3576, 1992. https://doi.org/10.3168/jds.S0022-0302(92)77770-4
  3. Yuri Fujimoto, HIrotaka Ito, Hidetoshi Higuchi, Hiroshi Ohno, Kohei Makita, "A case-control study of herd-and cow-level risk factors associated with an outbreak of Mycoplasma mastitis in Nemuro, Japan", Preventive Veterinary Medicine, vol. 177, 2020.
  4. Slawomir Zdunczyk, Tomasz Janowski, "Bacteriophages and associated endolysins in therapy and prevention of mastitis and metritis in cow: Current knowledge", Animal Reproduction Science, vol. 218, 2020.
  5. N.Tzanidakis, C.N.Brozos, N.Voutzourakis, A.Stefanakis, E.Malama, D.Zoller, A.Zdragkas, J.Hickford, S.Sotiraki, E.Kiossis, "Effect of abiotic and biotic factors on subclinical mastitis occurrence in low-input dairy sheep production systems", Small Ruminant Research, Volume 198, 106341, May 2021. https://doi.org/10.1016/j.smallrumres.2021.106341
  6. M.A. Puerto, E. Shepley, R.I Cue, D. Warner, J. Dubuc, E. Vasseur, "The hidden cost of disease: I. Impact of the first incidence of mastitis on production and economic indicators of primiparous dairy cows", Journal of Dairy Science, Volume 104, 2021. DOI: 10.3168/jds.2020-19584
  7. Shivank Vyas, Vipin Shukla, Nishant Doshi, "FMD and Mastitis Disease Detection in Cows Using Internet of Things(IOT)", Procedia Computer Science, vol. 160, 728-733. 2019. https://doi.org/10.1016/j.procs.2019.11.019
  8. Xudong Zhang, Xi Kang, Ningning Feng, Gang Liu, "Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector", Computers and Electronics in Agriculture, vol. 178, 2020.
  9. Colin Tobin, Derek W. Bailey, Mark G. Trotter, Lauren O'Connor, "Sensor base disease detection: A Case study using accelerometers to recognize symptoms of Bovin Ephemeral Fever", Computers and Electronics in Agriculuter, vol. 175, 2020.
  10. Bettina Miekley, Imke Traulsen, Joachim Krieter, "Detection of mastitis and lameness in dairy cows using wavelet analysis", Livestock Science, vol. 148, Issue 3, 227-236, 2012. https://doi.org/10.1016/j.livsci.2012.06.010
  11. Leo Weissbart, "Performance Analysis of Multilayer Perceptron in Profiling Side-Channel Analysis", Cryptology ePrint Archive, Report 2019/1476, 2019.
  12. Jose Carlos de Figueiredo Pantoja, Pamela Ruegg, "Understanding and using somatic cell counts to improve milk quality", Irish Journal of Agricultural and Food Research, vol. 52, Issue 21, 101-117, 2013.
  13. Guangfen Wei, Jie Zhao, Yanli Feng, Aixiang He, Jun Yu, "A Novel hybrid feature selection method based on dynamic feature importance", Applied Soft Computing, vol. 93, 2020.
  14. Ron Kohavi, "A Study of Cross-Validation and BootStrap for Accuracy Estimation and Model Selection", Appears in the International Joint Conference on Artificial Intelligence, vol. 2, 1137-1143, 1995.
  15. Xueliang Zhang, Xuezhi Feng, Pengfeng Xiao, Guangjun He, Liujun Zhu, "Segmentation quality evaluation using region-based precision and recall measures for remote sensing images", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 102, 73-84, 2015. https://doi.org/10.1016/j.isprsjprs.2015.01.009