Journal of Bio-Environment Control (생물환경조절학회지)

The Korean Society for Bio-Environment Control (한국생물환경조절학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Greenhouse Climate Management Model for Educational Simulation Design

교육용 시뮬레이션 설계를 위한 온실 환경 제어 모델의 활용

  • Yoon, Seungri (Protected Horticulture Researcher Institute, NIHHS) ;
  • Kim, Dongpil (Protected Horticulture Researcher Institute, NIHHS) ;
  • Hwang, Inha (Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University) ;
  • Kim, Jin Hyun (Protected Horticulture Researcher Institute, NIHHS) ;
  • Shin, Minju (Protected Horticulture Researcher Institute, NIHHS) ;
  • Bang, Ji Wong (Protected Horticulture Researcher Institute, NIHHS) ;
  • Jeong, Ho Jeong (Protected Horticulture Researcher Institute, NIHHS)
  • 윤승리 (농촌진흥청 국립원예특작과학원) ;
  • 김동필 (농촌진흥청 국립원예특작과학원) ;
  • 황인하 (서울대학교 농림생물자원학부 원예생명공학전공) ;
  • 김진현 (농촌진흥청 국립원예특작과학원) ;
  • 신민주 (농촌진흥청 국립원예특작과학원) ;
  • 방지웅 (농촌진흥청 국립원예특작과학원) ;
  • 정호정 (농촌진흥청 국립원예특작과학원)
  • Received : 2022.09.30
  • Accepted : 2022.10.31
  • Published : 2022.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Modern agriculture is being transformed into smart agriculture to maximize production efficiency along with changes in the 4th industrial revolution. However, rural areas in Korea are facing challenges of aging, low fertility, and population outflow, making it difficult to transition to smart agriculture. Among ICT technologies, simulation allows users to observe or experience the results of their choices through imitation or reproduction of reality. The combination of the three-dimension (3D) model and the greenhouse simulator enable a 3D experience by virtual greenhouse for fruits and vegetable cultivation. At the same time, it is possible to visualize the greenhouse under various cultivation or climate conditions. The objective of this study is to apply the greenhouse climate management model for simulation development that can visually see the state of the greenhouse environment under various micrometeorological properties. The numerical solution with the mathematical model provided a dynamic change in the greenhouse environment for a particular greenhouse design. Light intensity, crop transpiration, heating load, ventilation rate, the optimal amount of CO2 enrichment, and daily light integral were calculated with the simulation. The results of this study are being built so that users can be linked through a web page, and software will be designed to reflect the characteristics of cladding materials and greenhouses, cultivation types, and the condition of environmental control facilities for customized environmental control. In addition, environmental information obtained from external meteorological data, as well as recommended standards and set points for each growth stage based on experiments and research, will be provided as optimal environmental factors. This simulation can help growers, students, and researchers to understand the ICT technologies and the changes in the greenhouse microclimate according to the growing conditions.

국내외로 첨단 ICT 융합기술이 농업 분야에 적용되기 시작하면서, 시설원예 설비들이 고도화되고, 스마트팜 구축 기술 및 인력이 축적되기 시작하였다. 그러나 우리나라 농촌의 경우, 농업생산 연령의 고령화, 국내 농촌 인구의 지속적인 유출, 저출산 등으로 인하여 스마트팜 확대 및 적용에 어려움이 많은 실정이다. 따라서 공간 및 시간에 구속을 받지 않는 간편한 농업인 교육 프로그램이 필요하며, 최근 부상하고 있는 시뮬레이션 기술을 활용한다면 농업 교육용 시뮬레이션 툴 개발도 가능할 것으로 판단된다. 온실 환경 제어 모델을 이용한 시뮬레이션은 다양한 지역과 기상 조건 하에서 대상 온실의 열과 물질에너지의 상호작용을 합리적으로 예측할 수 있게 해준다. 본 연구에서는 온실 환경 제어 모델을 활용하여 외부 기상 데이터를 통해 온실의 환경 변화를 예측하고 가상의 환경 제어시스템을 통해 환경 제어 시 필요한 에너지값들을 시뮬레이션 할 수 있었다. 이러한 결과를 통해 이용자가 직접 맞춤형 환경 제어를 할 수 있도록 편의성을 고려한 사용자 인터페이스를 구축할 것이며, 실제 파프리카 재배 온실의 제어 요소들을 반영할 수 있도록 설계될 것이다. 농업용 교육 시뮬레이션 툴을 최근 활발하게 연구가 이루어지고 있는 작물 생육 모델링 기술 및 전산유체역학 기술과 융합하면 더욱타당한 결과를 보일 것이다.

Keywords

Acknowledgement

이 연구는 농촌진흥청 연구사업(세부과제번호: 421001-03)의 지원에 의해 이루어진 것임.

References

  1. ASAE 2003, Heating, Ventilating and Cooling Greenhouses. ANSI/ASAE Standard EP406.4:741-749.
  2. Barton K., and P. Maharg 2007, E-Simulations in the wild: interdisciplinary research, design, and implementation. Games and simulations in online learning: Research and development frameworks. Information Science Publishing, PA, USA, pp 115-148.
  3. Beny A., and K. Leah 2002, Effects of CO2 enrichment on yield, carbohydrate accumulation and changes in the activity of antioxidative enzymes in bell pepper (Capsicum annuum L.). J Hortic Sci Biotechnol 77:534-540. doi:10.1080/14620316.2002.11511534
  4. Cunha J.B. 2007, SIMGreen: A simulation tool for the greenhouse climate. Eur Fed Inf Technol Agric.
  5. De Zwart H.F. 1996, Analyzing energy-saving options in greenhouse cultivation using simulation model. PhD Thesis, Wageningen Agricultural University, Wageningen, the Netherlands, p 236.
  6. Dorais M. 2003, The use of supplemental lighting for vegetable crop production: Light intensity, crop response, nutrition, crop management, cultural practices. In Proc Can Greenhouse Conf Oct 9, 2003.
  7. Filipczak B., J. Gordon, M. Hequet, C. Lee, and M. Picard 1994, Can computer simulation stoke competitive fires? Training 31:12-13.
  8. Fitz-Rodriguez E., C. Kubota, G.A. Giacomelli, M.E. Tignor, S.B. Wilson, and M. McMahon 2010, Dynamic modeling and simulation of greenhouse environments under several scenarios: A web-based application. Comput Electron Agric 70:105-116. doi:10.1016/j.compag.2009.09.010
  9. Jeong I.S., C.G. Lee, L.H. Cho, S.Y. Park, M.J. Kim, S.J. Kim, and D.H. Kim 2020, Development and validation of inner environment prediction model for glass greenhouse using CFD. Protected Hort Plant Fac 29:285-292. (in Korean) doi:10.12791/KSBEC.2020.29.3.285
  10. Jeong W.J., D.J. Myoung, and J.H. Lee 2009, Comparison of climate conditions of sweet pepper's greenhouse between Korea and the Netherlands. J Bio-Env Con 18:244-252. (in Korean)
  11. Jolliet O., and B.J. Bailey 1992, The effect of climate on tomato transpiration in greenhouses-measurements and models comparison. Agric For Meteorol 58:43-62. doi:10.1016/0168-1923(92)90110-P
  12. Kang D.B., B.G. Chung, and C.M. Heo 2020, Factors affecting acceptance of smart farm technology: Focusing on mediating effect of trust and moderating effect of IT level. Korean J Org Agric 28:315-334. (in Korean) doi:10.11625/KJOA.2020.28.3.315
  13. Kang W.H., I. Hwang, D.H. Jeong, D. Kim, J. Kim, J.H. Kim, K.S. Park, and J.E. Son 2019, Time change in spatial distributions of light interception and photosynthetic rate of paprika estimated by ray-tracing simulation. Protected Hort Plant Fac 28:279-285. (in Korean) doi:10.12791/KSBEC.2019.28.4.279
  14. Kim J.G., I.B. Lee, K.S. Yoon, T.H. Ha, R.W. Kim, U.H. Yeo, and S.Y. Lee 2018, A study on the trends of virtual reality application technology for agricultural education. Protected Hort Plant Fac 27:147-157. (in Korean) doi:10.12791/KSBEC.2018.27.2.147
  15. Lee C.G., and J.S. Lee, H.J. Jeong, J.H. Lee, J.H. Kim, J.E. Son, I. Hwang, J.H. Shin, K.S. Park, and J.W. Lee 2021, Greenhouse Supplemental Lighting Technology. National Institute of Horticultural & Herbal Science, RDA, Wanju, Korea, p 82. (in Korean)
  16. Lee S.N., S.J. Park, I.B. Lee, T.H. Ha, K.S. Kwon, R.W. Kim, U.H. Yeo, and S.Y. Lee 2016, Design of energy model of greenhouse including plant and estimation of heating and cooling loads for a multi-span plastic-film greenhouse by building energy simulation. Protected Hort Plant Fac 25: 123-132. (in Korean) doi:10.12791/KSBEC.2016.25.2.123
  17. Lopez R., and E.S. Runkle (Eds.) 2017, Light Management in Controlled Environments. Meister Media Worldwide, OH, USA.
  18. Luo W., C. Stanghellini, J. Dai, X. Wang, H.F. de Zwart, and C. Bu 2005, Simulation of greenhouse management in the subtropics, Part II: Scenario study for the summer season. Biosyst Eng 90:433-441. doi: 10.1016/j.biosystemseng.2004.12.002
  19. Martzopoulou A., D. Vafiadis, and V.P. Fragos 2020, Energy gain in passive solar greenhouses due to CO2 enrichment. Energies 13:1242. doi:10.3390/en13051242
  20. Medrano E., P. Lorenzo, M.C. Sanchez-Guerrero, and J.I. Montero 2005, Evaluation and modelling of greenhouse cucumber-crop transpiration under high and low radiation conditions. Sci Hortic 105:163-175. doi:10.1016/j.scienta.2005.01.024
  21. Nam S.W., and H.H. Shin 2015, Development of a method to estimate the seasonal heating load for plastic greenhouses. J Korean Soc Agric Eng 57:37-42. (in Korean) doi:10.5389/KSAE.2015.57.5.037
  22. Navas L.M., S. Plaza, J.L. Garcia, L. Luna, R. Benavente, J.M. Duran, and N. Retamal 1998, Formulation and sensitivity analysis of a dynamic model of the greenhouse climate validation for a mild Mediterranean climate. Acta Hortic 456:305-312. doi:10.17660/ActaHortic.1998.456.36
  23. Runkle E.S. 2007, Maximizing supplemental lighting. Greenhouse Product News, November 2007, p 66.
  24. Sabeh N.C., G.A. Giacomelli, and C. Kubota 2006, Water use for pad and fan evaporative cooling of a greenhouse in a semi-arid climate. Acta Hortic 719:409-416. doi:10.17660/ActaHortic.2006.719.46
  25. Shin J., I. Hwang, D. Kim, J.Kim, J.H. Kim, and J.E. Son 2022, Waning advantages of CO2 enrichment on photosynthesis and productivity due to accelerated phase transition and source-sink imbalance in sweet pepper. Sci Hortic 301:111130. doi:10.1016/j.scienta.2022.111130
  26. Shin J.H., and J.E. Son 2015, Irrigation criteria based on estimated transpiration and seasonal light environmental condition for greenhouse cultivation of paprika. Protected Hort Plant Fac 24:1-7. (in Korean) doi:10.12791/KSBEC.2015.24.1.001
  27. Shin J.H., J.S. Park, and J.E. Son 2014, Estimating the actual transpiration rate with compensated levels of accumulated radiation for the efficient irrigation of soilless cultures of paprika plants. Agric Water Manag 135:9-18. doi:10.1016/j.agwat.2013.12.009
  28. Son J.E., I.S. Kim, J.M. Choi, and J.H. Bae 2021, Greenhouse Horticulture. Hyangmunsa, Seoul, Korea. (in Korean)
  29. Statistics Korea (KOSTAT) 2020, Census of Agriculture, Forestry and Fisheries in 2020. KOSTAT, Daejeon, Korea. (in Korean)
  30. Ta T.H., J.H. Shin, T.I. Ahn, and J.E. Son 2011, Modeling of transpiration of paprika (Capsicum annuum L.) plants based on radiation and leaf area index in soilless culture. Hortic Environ Biotechnol 52:265-269. doi:10.1007/s13580-011-0216-3
  31. Tadj N., T.D. Bartzanas, Fidaros, B. Draoui, and C. Kittas 2010, Influence of heating system on greenhouse microclimate distribution. Transact ASABE 53:225-238. doi:10.13031/2013.29498
  32. Takakura T., and J.E. Son 2004, Simulation of Biological and Environmental Processes. Kyushu University Press, Kyushu, Japan, p 139.
  33. Tignor M.E., S.B. Wilson, G.A. Giacomelli, C. Kubota, E. Fitz-Rodriguez, T.A. Irani, E.B. Rhoades, and M.J. McMahon 2007, Multi-institutional cooperation to develop digital media for interactive greenhouse education. HortTechnology 17: 397-399. doi:10.21273/HORTTECH.17.3.397
  34. Yang Y.X., D.H. Zhu, and T.L. Tan 1999, The development of IT and the application trend of which in agriculture. Comput Agric 4:1-6.
  35. Yoon S., J.H. Kim, I. Hwang, D. Kim, J. Shin, and J.E. Son 2021, Effect of stem number on growth, fruit quality, and yield of sweet peppers grown in greenhouses under supplemental lighting with high pressure sodium lamps in winter. J Bio-Env Con 30:237-243. (in Korean) doi:10.12791/KSBEC.2021.30.3.237
  36. Yu I.H., N.K. Yun, M.W. Cho, H.R. Ryu, and D.G. Moon 2014, Development of CFD model for analyzing the air flow and temperature distribution in greenhouse with air-circulation fans. CNU J Agric Sci 41:461-472. (in Korean) doi:10.7744/cnujas.2014.41.4.461
  37. Zeng C.Z., Z.L. Bie, and B.Z. Yuan 2009, Determination of optimum irrigation water amount for drip-irrigated muskmelon (Cucumis melo L.) in plastic greenhouse. Agric Water Manag 96:595-602. doi:10.1016/j.agwat.2008.09.019
  38. Zhu Y. 2004, Effects of information technology in agriculture modernization. Agric Mod 5:43-44.