한국산업정보학회논문지 (Journal of Korea Society of Industrial Information Systems)

한국산업정보학회 (Korea Society of Industrial Information Systems)
권호 표지
DOI QR코드

DOI QR Code

지능형 농업 서비스를 위한 미기상기반 스마트팜 예측 플랫폼 개발

Development of Microclimate-based Smart farm Predictive Platform for Intelligent Agricultural Services

  • 문애경 (한국전자통신연구원 지역산업IT융합연구실) ;
  • 이은령 (한국전자통신연구원 의료IT융합연구실) ;
  • 김승한 (경상북도농업기술원 풍기인삼연구소)
  • 투고 : 2020.07.27
  • 심사 : 2021.01.27
  • 발행 : 2021.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 다양한 애플리케이션 도메인을 위한 IoT 솔루션이 개발되고 있으며, 농업분야에서도 IoT 기술을 적용하여 농작물 생산량은 늘리는 반면에 손실은 줄임으로써 농업 생산성을 향상시키기 위한 데이터기반 정밀농업 연구가 진행되고 있다. 이에 본 논문은 미기상 데이터를 수집하여 서리 및 병해충 등 농업예측서비스를 제공하기 위한 스마트팜 플랫폼을 제안하고자 한다. 제안된 플랫폼에서는 실시간으로 수집한 미기상 데이터를 기반으로 서리 및 병해충을 예측하여, 농민들에게 서리 가능성과 병해충 예보 서비스를 제공한다. 실험을 통해 확인한 결과, 미기상기반 예측 플랫폼은 지역기상기반 데이터를 이용한 서리예측보다 더 높은 정밀도(Precision)값을 보임을 알 수 있었다. 정확한 실험을 위하여 시스템 설치 현장에서 실제 관측한 병해충 예찰 데이터를 수집 중에 있다. 본 플랫폼을 활용하여 서리와 병해충 발생 예측정보를 사전에 효과적으로 제공함으로써, 농민들이 작물 피해 및 불필요한 농약 사용을 줄일 수 있도록 하는 정밀농업 서비스를 제공할 수 있을 것으로 기대된다.

The emerging smart world based on IoT requires deployment of a large number of diverse sensors to generate data pertaining to different applications. Recent years have witnessed a plethora of IoT solutions beneficial to various application domains, IoT techniques also help boost agricultural productivity by increasing crop yields and reducing losses. This paper presents a predictive IoT smart farm platform for forcast services. We built an online agricultural forecasting service that collects microclimate data from weather stations in real-time. To demonstrate effectiveness of our proposed system, we designed a frost and pest forecasting modes on the microclimate data collected from weather stations, notifies the possibilities of frost, and sends pest forecast messages to farmers using push services so that they can protect crops against damages. It is expected to provide effectively that more precise climate forecasts thus could potentially precision agricultural services to reduce crop damages and unnecessary costs, such as the use of non-essential pesticides.

키워드

참고문헌

  1. M. H. Almarshadi and S. M. Ismail(2011), Effects of Precision Irrigation on Productivity and Water Use Efficiency of Alfalfa under Different Irrigation Methods in Arid Climates, Journal of Applied Sciences Research.
  2. M. R. Bendre, R. C. Thool, and V. R. Thool(2016). Big Data in Precision Agriculture Through ICT: Rainfall Prediction Using Neural Network Approach, Advances in Intelligent Systems and Computing.
  3. U. Chung, H. C. Seo, and J. I. Yun(2004), Site Specific Frost Warning based on Topoclimatic Estimation of Daily Minimum Temperature, Korean Journal of Agricultural and Forest Meteorology, vol. 6, no. 3, pp. 164- 169.
  4. J. Han. et al.(2009) Frostfall Forecasting in the Naju Pear Production Area based on Discriminant Analysis of Climatic, Korean Journal of Agricultural and Forest Meteorology, vol. 11, no. 4, pp. 135-142. https://doi.org/10.5532/KJAFM.2009.11.4.135
  5. M. A. K. Jaradat, M. A. Al-Nimr, and M. N. Alhamad(2008), Smoke Modified Environment for Crop Frost Protection: A Fuzzy Logic Approach, Comput. Electron. Agric., vol. 64, no. 2, pp. 104-110, Dec. 2. https://doi.org/10.1016/j.compag.2008.04.007
  6. Y. Jeon et al. (2020), Deep Learning-based Rice Seed Segmentation for Phynotyping, Journal of the Korea Industrial Information Systems Research vol. 25 no. 5, pp. 23-29 https://doi.org/10.9723/JKSIIS.2020.25.5.023
  7. C. Kim(2002), Development of Agricultural Micro-climate Forecasting Model and Crop Production Information System, IPET Research Report.
  8. S. Kim, and K Hong(2017) Development and Performance Analysis of Predictive Model for KOSPI 200 Index using Recurrent Neural Networks, Journal of the Korea Industrial Information Systems Research vol. 22 no.6, pp. 23-29 https://doi.org/10.9723/JKSIIS.2017.22.6.023
  9. Y. Kwon, H. Lee, W. Kwon, and K. Boo(2008), The Weather Characteristics of Frost Occurrence Days for Protecting Crops against Frost Damage, The Korean Geographic Society, pp. 824-842.
  10. W. Lee, et al.(2020) Forecasting of Iron Ore Prices using Machine Learning, Journal of the Korea Industrial Information Systems Research vol. 25 no .2, pp. 57-72 https://doi.org/10.9723/JKSIIS.2020.25.2.057
  11. R. D. A. Ludena and A. Ahrary(2013). A Big Data Approach for a New ICT Agriculture Application Development, International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Oct, pp. 140-143.
  12. P. Matzneller, K.-P. Gotz, and F.-M. Chmielewski(2016), Spring Frost Vulnerability of Sweet Cherries Under Controlled Conditions, International Journal of Biometeorology, vol. 60, no. 1, pp. 123-130. https://doi.org/10.1007/s00484-015-1010-1
  13. Nidhi(2020), Big Data for Smart Agriculture, Smart Villiage Technology, pp 181-189
  14. Olson, David L.; and Delen, Dursun (2008); Advanced Data Mining Techniques, Springer, 1st edition (February 1, 2008), page 138, ISBN 3-540-76916-1
  15. Z. Shi(2017), Pesticide Pollution in China, Thesis Centria University of Applied Sciences.
  16. Zhang et al.(2003). Modified Logistic Regression: An Approximation to SVM and its Applications in Large-Scale Text Categorization, ICML.