Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Ubiquitous Sensor Network Quality Control Algorithm for Highland Cabbage

고랭지배추 생육을 위한 유비쿼터스 센서 네트워크 품질관리 알고리즘 개발

  • Cho, Changje (Department of Statistics, Daegu University) ;
  • Hwang, Guenbo (Division of Mathematics and Big Data Science, Daegu University) ;
  • Yoon, Sanghoo (Division of Mathematics and Big Data Science, Daegu University)
  • 조창제 (대구대학교 일반대학원 통계학과) ;
  • 황근보 (대구대학교 수리빅데이터학부) ;
  • 윤상후 (대구대학교 수리빅데이터학부)
  • Received : 2018.09.17
  • Accepted : 2018.11.09
  • Published : 2018.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Weather causes much of the risk of agricultural activity. For efficient farming, we need to use weather information. Modern agriculture has been developed to create high added value through convergence with state-of-the-art Information and Communication Technology (ICT). This study deals with the quality control algorithms of weather monitoring equipment through Ubiquitous Sensor Network (USN) observational equipment for efficient cultivation of cabbage. Accurate weather observations are important. To achieve this goal, the Korea Meteorological Administration, for example, developed various quality control algorithms to determine regularity of the observation. The research data of this study were obtained from five USN stations, which were installed in Anbandegi and Gwinemi from 2015 to 2017. Quality control algorithms were developed for flat line check, temporal outliers check, time series consistency check and spatial outliers check. Finally, the quality control algorithms proposed in this study can also identify potential abnormal observations taking into account the temporal and spatial characteristics of weather data. It is expected to be useful for efficient management of highland cabbage production by providing quality-controlled weather data.

농업활동의 위험은 대부분은 기상에 의해 발생한다. 효율적인 농작업을 위해선 기상정보를 활용해야 한다. 현대 농업은 첨단 기술인 ICT와 융합을 통해 고부가가치를 창출하는 방향으로 발전하고 있다. 본 연구에서는 고랭지배추의 효율적인 재배를 위한 USN 관측장비를 통한 기상관측장비의 품질관리 알고리즘을 다룬다. 기상관측에서 정확한 관측이 중요하다. 이를 위해서 기상청에서는 기상관측 장비별로 품질관리 알고리즘을 개발하여 기상정보의 정확성 검증을 통해 정상자료 여부를 판정한 후 이를 활용한다. 연구자료는 2015년부터 2017년까지 3년간 대표적인 고랭지배추 재배지인 안반덕, 귀네미에 설치된 5개 USN 자료이다. 품질관리 알고리즘은 지속성검사, 기후범위검사, 시간변동성검사, 공간분포검사로 구성되어 있다. 마지막으로 본 연구에서 제안하는 품질관리 알고리즘은 기상자료의 공간적 특성을 고려한 잠재적 이상관측 여부도 확인할 수 있다. 또한 품질관리를 거친 자료를 토대로 고랭지배추와 기상관측자료의 상관성을 분석함으로써 효율적 농산업 관리에 도움이 될 것으로 보여진다.

Keywords

NRGSBM_2018_v20n4_337_f0001.png 이미지

Fig. 1. The sensor network for USN.

NRGSBM_2018_v20n4_337_f0002.png 이미지

Fig. 3. An expected cause of error.

NRGSBM_2018_v20n4_337_f0003.png 이미지

Fig. 2. The spatial distribution of equipments.

NRGSBM_2018_v20n4_337_f0004.png 이미지

Fig. 4. The Flowchart of QC algorithm.

NRGSBM_2018_v20n4_337_f0005.png 이미지

Fig. 5. The distribution of temperature and relative humidity.

NRGSBM_2018_v20n4_337_f0006.png 이미지

Fig. 6. Distribution of deviations by variables.

NRGSBM_2018_v20n4_337_f0007.png 이미지

Fig. 7. Before applying QC algorithm.

NRGSBM_2018_v20n4_337_f0008.png 이미지

Fig. 8. After applying QC algorithm.

Table 1. Location of USN & ASOS

NRGSBM_2018_v20n4_337_t0001.png 이미지

Table 2. Errors by stations for flat line check

NRGSBM_2018_v20n4_337_t0002.png 이미지

Table 3. The result of temporal outliers check given threshold

NRGSBM_2018_v20n4_337_t0003.png 이미지

Table 4. The result of temporal outliers check given threshold

NRGSBM_2018_v20n4_337_t0004.png 이미지

Table 5. The result of time series consistency check given threshold

NRGSBM_2018_v20n4_337_t0005.png 이미지

Table 6. The result of spatial outlier check given threshold

NRGSBM_2018_v20n4_337_t0006.png 이미지

Table 7. The result for quality control of USN data

NRGSBM_2018_v20n4_337_t0007.png 이미지

References

  1. Ahn, J. H., K. D. Kim, and J. T. Lee, 2014: Growth modeling of Chinese cabbage in an Alpine area. Korean Journal of Agricultural and Forest Meteorology 16(4), 309-315. https://doi.org/10.5532/KJAFM.2014.16.4.309
  2. Choi, S. W., J. S. Lee, J. Kim, B. L. Lee, K. R. Kim, and B. C. Choi, 2015: Agrometeorological observation environment and periodic report of Korea Meteorological Administration: Current Status and Suggestions. Korean Journal of Agricultural and Forest Meteorology 17(2), 144-155. https://doi.org/10.5532/KJAFM.2015.17.2.144
  3. Kang, S. S., S. H. Kim, J. W. Lee, and H. J. Kang, 2011: USN based agricultural IT convergence technology trends. Electronics and Telecommunications Trends 26(6), 97-107.
  4. Kim, D. J., 2011: An implementation of meteorological observation network based on RFID/USN. Master Thesis, Soongsil University.
  5. Kim, J. H., and J. I. Yun, 2015: A thermal time - Based phenology estimation in Kimchi cabbage (Brassica campestris L. ssp. pekinensis). Korean Journal of Agricultural and Forest Meteorology 17(4), 333-339. https://doi.org/10.5532/KJAFM.2015.17.4.333
  6. Kim, Y. J., S. Y. Gouk, Y. R. Kim, M. G. Lee, J. S. Kim, Y. H. Kim, K. T. Min, I. B. Ji, and J. H. Sim, 2013: The present status and development direction of smart agriculture. Korea Rural Economic Institute, 1-159.
  7. KMA (Korea Meteorological Administration), 2016: Guideline for comprehensive quality management of meteorological agency data.
  8. Lee, J. D., Y. E. Choi, and C. Y. Park, 2010: Developments of quality control algorithms for Korean temperature data. Journal of climate research 5(2), 162-174.
  9. Nam, Y. U., D. W. Kim, Y. H. Lee, and Y. H. Kim, 2014: A survey on quality control of automatic weather Station. Korea Information Science Society 2014(6), 16-18.
  10. Oh, G. L., S. J. Lee, B. C. Choi, J. Kim, K. R. Kim, S. W. Choi, and B. L. Lee, 2015: Quality control of Agro-meteorological data measured at Suwon weather station of Korea Meteorological Administration. The Korean Society of Agricultural and Forest Meteorology 17(1), 25-34. https://doi.org/10.5532/KJAFM.2015.17.1.25
  11. Reek, T., S. R. Doty, and T. W. Owen, 1992: A deterministic approach to the validation of historical daily temperature and precipitation data from the cooperative network. Bulletin of the American Meteorological Society 73(6), 753-762. https://doi.org/10.1175/1520-0477(1992)073<0753:ADATTV>2.0.CO;2
  12. Wilcox, R. R., 1996: Statistics for the social sciences. Academic Press.
  13. Yoo, N. H., G. J. Song, J. H. Yoo, S. Y. Yang, C. S. Son, J. G. Koh, and W. J. Kim, 2009: Design and implementation of the management system of cultivation and tracking for agricultural products using USN. Korea Information Science Society 15(9), 661-674.