Research-platform Design for the Korean Smart Greenhouse Based on Cloud Computing

Jeong-Hyun Baek; Jeong-Wook Heo; Hyun-Hwan Kim; Youngsin Hong; Jae-Su Lee

doi:10.12791/KSBEC.2018.27.1.27

All Issue

2018 Vol.27, Issue 1 Preview Page Next Page

Research-platform Design for the Korean Smart Greenhouse Based on Cloud Computing 클라우드 기반 한국형 스마트 온실 연구 플랫폼 설계 방안

31 January 2018. pp. 27-33

PDF XML

Abstract

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

Keywords

agricultural information & communication technology(ICT)

artificial intelligence(AI)

big data

decision-making support service

smart farm

본 연구는 농업 및 정보 통신 기술의 융합을 기반으로 국내외 스마트 농장 서비스 모델을 검토하고 한국의 스 마트 온실을 개선하기 위해 필요한 다양한 요인을 조사 하기 위해 수행되었다. 국내 스마트 온실의 작물 생육모 델 및 환경모델에 관한 연구는 제한적이었고, 연구를 위 한 인프라를 구축하는 데는 많은 시간이 필요하다. 이러한 문제의 대안으로 클라우드 기반 연구 플랫폼이 필요하다. 제안된 클라우드 기반 연구 플랫폼은 통합 데이터, 생육 환경모델, 구동기 제어 모델, 스마트 온실 관리, 지식 기 반 전문가 시스템 및 농가 대시보드 모듈을 통해 통합 적 데이터 저장 및 분석을 위한 연구 인프라를 제공한 다. 또한 클라우드 기반 연구 플랫폼은 작물 생육환경, 생산성 및 액추에이터 제어와 같은 다양한 요인들 간의 관계를 정량화하는 기능을 제공하며, 연구자는 빅데이터, 기계 학습 및 인공지능을 활용하여 작물 생육 및 생장 환경 모델을 분석할 수 있다.

키워드

농업 ICT

인공지능

빅데이터

의사결정지 원서비스

스마트팜

References

A. Atole, A. Asmar, A. Biradar, N. Kothawade, S. Sarode and R.G. Khope, International Journal of Emerging Technologies and Innovative Research, IoT based smart farming system., 4(4); 29-31 (2017)www.jetir.org

J.H. Baek and H.L. Lee, Database Society Journal, Design and implementation of crop-environmental control cloud systems based on growth patterns. The Korean Institute of Information Scientists and Engineers., 30(2); 57-66 (2014)

Beecham Research, (2014)https://www.beechamresearch.com/files/BRL%20Smart%20Farming%20Executive%20Summary.pdf

J. CaA adas, J.A. SA nchez-Molina, F. RodrA-guez and I.M. del A?guila, Inf. Process. Agric., Improving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoes., 4(1); 50-63 (2017)

Gartner, Artificial intelligence, machine learning, and smart things promise an intelligent future. (2017)www.gartner.com/smarterwithgartner/gartners-top-10-technologytrends-2017/

F. Guerrini, (2015)http://www.forbes.com/sites/federicoguerrini/2015/18/the-future-of-agriculture-smart-farming/#5708f01a337c

I. Hameed, E.I. El-Madbouly and M.I. Abdo, International Journal of Automation and Control, Reconfigurable adaptive fuzzy fault-hiding control for greenhouse climate control system., 11(2); 164-187 (2017)

10.1504/IJAAC.2017.10002732

IRS Global Research, IoT-based smart agriculture and smart farm market forecasts and core technology development trends. (2016)http://www.irsglobal.com/

D. Kapnias and P. Ilias, 22nd CPA/IACS conference, Automated classification of land cover for the needs of CAP using Sentinel data.Lisbon(Portugal) (2016)

J.S. Lee, Y.G. Hong, G.H. Kim, D.H. Lee, S.R. Han and D.H. Im, 2016 Fall Conference, A study on development of cloud system for the smart greenhouse automatic control.; 559-560 (2016)

G. Li, W. Zhang and Y. Zhang, Sensors & Transducers, A design of the IOT gateway for agricultural greenhouse., 172(6); 75 (2014)

Y. Liu and C. Bi, Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), 2017 IEEE International Conference, The design of greenhouse monitoring system based on ZigBee WSNs., l(2); 430 (2017)

J. Michael, ISSN 2214-3173, Modelling the smart farm, information, processing in agriculture. (2017)

RDA, Big-data utilization for smart farm proper management.; 66-72, Tomato. (2016)

J.N. Xin and F. Zazueta, Agric. Eng. Int. CIGR J., Technology trends in ICT – towards data-driven, farmer-centered and knowledge-based hybrid cloud architectures for smart farming., 18(4); 275-279 (2016)

U.H. Yeo, I.B. Lee, K.S. Kwon, T.H. Ha, S.J. Park, R.W. Kim and S.Y. Lee, Protected Horticulture and Plant Factory, Analysis of research trend and core technologies based on ICT to materialize smart-farm., 25(1); 30-41 (2016)

10.12791/KSBEC.2016.25.1.30

Information

Publisher :The Korean Society for Bio-Environment Control
Publisher(Ko) :(사)한국생물환경조절학회
Journal Title :Protected Horticulture and Plant Factory
Journal Title(Ko) :시설원예ㆍ식물공장
Volume : 27
No :1
Pages :27-33
Received Date : 2017-09-26
Revised Date : 2018-01-17
Accepted Date : 2018-01-23
DOI :https://doi.org/10.12791/KSBEC.2018.27.1.27

Journal of Bio-Environment Control ISSN:1229-4675(Print) 2765-3641(Online) 생물환경조절학회지

All Issue