DOI QR코드

DOI QR Code

양돈 데이터 기반의 급이 관리 서비스 모델 구현

Implementation of Feeding Management Service Model based on Pig Raising Data

  • 김봉현 (서원대학교 IT학부 컴퓨터공학과)
  • Kim, Bong-Hyun (Department of Computer Engineering, Seowon University)
  • 투고 : 2021.08.11
  • 심사 : 2021.10.20
  • 발행 : 2021.10.28

초록

양돈 ICT 자동 급이기는 설정된 조건에 맞춰 사료 등을 자동 급이가 가능하다. 그러나, 설정 조건 자체는 사용자의 경험에 의존해야 하는 단점이 있다. 그렇기 때문에, 시행착오를 유발하고, 효율성이 떨어지는 문제가 발생하고 있다. 따라서, 데이터에 기반한 최적의 급이 설정 조건을 제시해 양돈 생산성을 향상시킬 수 있는 시스템 개발 및 서비스 모델 구현이 필요하다. 따라서, 본 논문에서는 기존의 급이 데이터, 사양관리 데이터 및 양돈 생산 관리 시스템 등의 성적분석 프로그램을 활용한 양돈 급이 관리 서비스 모델을 개발하였다. 이를 통해, 양돈 데이터 분석으로 효율적으로 활용할 수 있는 수요자 중심의 급이 관리 서비스 모델을 개발하였다. 또한, 지능화된 자동 급이 관리 서비스로 농가의 폐사율 감소 및 MSY 증가에 일조함으로써 양돈 농가의 생산성 향상과 이로 인해 양돈 농가의 소득 증대에 기여하는 서비스를 제공할 수 있다.

The pig ICT automatic feeder is capable of automatically feeding feed, etc. according to the set conditions. However, there is a disadvantage that the setting condition itself must depend on the user's experience. Therefore, trial and error is caused, and there is a problem that the efficiency is lowered. Therefore, it is necessary to develop a system and implement a service model that can improve pig productivity by suggesting optimal feeding setting conditions based on data. Therefore, in this paper, a pig feeding management service model was developed using the performance analysis program such as the existing feeding data, breeding management data, and pig production management system. Through this, we developed a consumer-oriented feed management service model that can be efficiently utilized by analyzing pig data. In addition, it is possible to provide a service that contributes to a decrease in the mortality rate and an increase in the MSY of the farms with the intelligent automatic feeding management service, thereby improving the productivity of the pig farms and thereby increasing the income of the pig farms.

키워드

참고문헌

  1. B. Ekkarat, C. Oran & S. Anukit. (2018). Smart Farm: Applying the Use of NodeMCU, IOT, NETPIE and LINE API for a Lingzhi Mushroom Farm in Thailand. IEICE Transactions on Communications, 101(1), 16-23. DOI : 10.1587/transcom.2017ITI0002
  2. S. G. Kwon, S. C. Kang & H. H. Tack. (2018). Implimentation of Smart Farm System Using the Used Smart Phone. Journal of the Korea Institute of Information and Communication, 22(11), 1524-1530. DOI : 10.6109/JKIICE.2018.22.11.1524
  3. K. J. Kim. (2015). Trends and Prospects of Smart Farm Technology. Electronics and Telecommunications Trends, 30(5), 1-10. DOI : 10.22648/ETRI.2015.J.300501
  4. H. Gan & W. S. Lee. (2018). Development of a Navigation System for a Smart Farm. IFAC-PapersOnLine, 51(17), 1-4. DOI : 10.1016/j.ifacol.2018.08.051
  5. C. Priyanka, B. Ankita & C. Harsh. (2018). Smart Irrigation and Remote Farm Monitoring System. International Journal of Computer Applications, 180(24), 24-26. DOI : 10.5120/ijca2018917011
  6. B. H. Kim. (2020). Study on Next-generation Smart Farm Business Model Optimization Based on Heterogeneous System Integration. Journal of Next-generation Convergence Technology Association, 4(3), 265-271. DOI : 10.33097/JNCTA.2020.04.03.265
  7. M. Jirapond, B. Nathaphon, K. Siriwan, L. Narongsak & Wani. (2019). IoT and agriculture data analysis for smart farm. Computers and electronics in agriculture, 156, 467-474. DOI : 10.1016/j.compag.2018.12.011
  8. Hashem, Nesrein M. & Gonzalez-Bulnes, Antonio. (2020). State-of-the-Art and Prospective of Nanotechnologies for Smart Reproductive Management of Farm Animals. Animals, 10(5), 840. DOI : 10.3390/ani10050840
  9. M. H. Ahn & C. M. Heo. (2019). The Effect of Technical Characteristics of Smart Farm on Acceptance Intention by Mediating Effect of Effort Expectation. Journal of Digital Convergence, 17(6), 145-157. DOI : 10.14400/JDC.2019.17.6.145
  10. S. Y. Joo & G. S. Yeom. (2017). A Study on Integrated Management Platform for Smart Farm. Proceedings of the Korea Information Processing Society Conference, 450-453. DOI : 10.3745/PKIPS.Y2017M04A.450
  11. Yichi Zhang, Yingmeng Xiang & Lingfeng Wang. (2017). Power System Reliability Assessment Incorporating Cyber Attacks Against Wind Farm Energy Management Systems. IEEE Transactions on Smart Grid, 8(5), 2343-2357. DOI : 10.1109/TSG.2016.2523515
  12. T. H. Kim & J. S. Han. (2017). Agricultural Management Innovation through the Adoption of Internet of Things: Case of Smart Farm. Journal of Digital Convergence, 15(3), 65-75. DOI : 10.14400/JDC.2017.15.3.65
  13. Mahjabin, T, Xiao, Y & Sun, G. (2017). A survey of distributed denial-of-service attack, prevention, and mitigation techniques. Int J Distrib Sens N., 13(12), 1-33.
  14. Shen, J. & Chang, S. (2018). A lightweight multi-layer authentication protocol for wireless body area networks. Future Gener Comp System, 78, 956-963. https://doi.org/10.1016/j.future.2016.11.033
  15. Zhang, M. Ding, X. & Li, L. (2018). Screening of lactic acid bacteria feeding for pig, preparing of compound probiotics and its feeding effect on growing pigs. New biotechnology, 44, 81-82. DOI : 10.1016/j.nbt.2018.05.914
  16. Li, Biao. Zeng, Qinghua. Song, Yukun. Gao, Zhendong. Jiang, Liang. Ma, Haiming & He, Jun. (2020). The effect of fly maggot in pig feeding diets on growth performance and gut microbial balance in Ningxiang pigs. Journal of Animal Physiology and Animal Nutrition, 104(6), 1867-1874. DOI : 10.1111/jpn.13248
  17. D. J. Shin & H. S. Yang. (2009). Design and implementation of an intrusion detection system based on outflow traffic analysis. Journal of Korea Content Association, 9(4), 131-141 https://doi.org/10.5392/JKCA.2009.9.4.131