• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2002.02a
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• pp.192-197
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• 2002

1. 실시간 모니터링 -온실 내부환경의 계측장치로 모듈화된 단일 칩 마이크로프로세서를 이용한 하우스 모니터를 개발하였다. 개발된 다수의 하우스 모니터는 RS-485통신을 이용하여 개발된 프로토콜을 통하여 그룹 모니터와 통신하면서 계측 데이터를 전송하였고 안정된 계측 성능을 보였다. 또한 그룹 모니터는 하우스모니터로부터 수신한 데이터를 인터넷 환경 TCP/IP 통신에 의해 서버에 정보를 전송하고 데이터베이스 서버에 저장할 수 있었다. 2. 클라이언트 서버 모델 -클라이언트 모니터를 통하여 허용된 사용자들은 해당 온실의 상황을 원격지에서 파악할 수 있는 있었다. 또한 분산환경 기술을 이용하여 서버를 경유하여 데이터베이스 서버에서 데이터 셋을 가져와 과거 재배 사례 등을 조회 및 이용 가능하였다. 이는 전문가에게 접근을 허용함으로써 재배에 관한 지원이 가능하도록 하였다. 데이터 베이스 시스템으로 연계하여 온실환경 정보를 분석하는 것이 가능하였다. 3. 기대효과 및 나아가야 할 방향 -개발된 시스템을 식물 공장 내 작물의 재배환경을 데이터베이스화하여 재배사례 데이터베이스를 형성하고 작물이 가장 잘 자라는 최적 재배 환경을 연구하여 고품질의 작물 재배에 이용될 수 있다. 또한 식물공장의 운전실적, 환경 조건, 환경 조절비용 등의 분석에 효율적으로 이용될 수 있을 것으로 예상되며 각 환경인자들과의 관계를 구명하는데 도움을 줄 것이다. 축적된 작물의 재배 사례 데이터베이스를 이용하여 작물 특성 및 재배 연구에 도움을 줄 수 있을 것이다. 제어 장치들의 운영실적을 분석함으로써 제어 시스템의 효율적이고 경제적인 제어가 가능하도록 할 수 있을 것이다. 이들이 모두 완성되면 전문가 및 전문가 시스템으로부터 지원을 받는 지능형 식물공장이 가능할 것이다. 본 연구에서 개발한 계측 모듈 및 데이터베이스 시스템은 실제 농가에 설치된 전용선을 이용하여 실증 실험을 통해 수정·보완하여야 할 것이다. 또한 시설원예분야에서 있어서 통신체계에 대한 표준화 연구가 수행되어 앞으로 개발될 다른 시스템들과의 호환성을 갖도록 해야 할 것이다. 앞으로 온실의 경영 및 관리 데이터베이스를 개발하여 첨단온실의 통합 관리 및 정보 시스템을 구축하여야 할 것이다. 또한, 시설원예의 환경 설계의 기준을 적용할 수 있도록 하여야 할 것이다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.185-199
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• 2019

Korea's facility horticulture has developed remarkably in a short period of time. However, in order to secure international competitiveness in response to unfavorable surrounding conditions such as high operating costs and market opening, it is necessary to diagnose the problems of facility horticulture and prepare countermeasures through analysis. The purpose of this study was to analyze the case of leading farmers by introducing information and communication technology (ICT) in hydroponic cultivation agriculture and horticulture, and to examine how agricultural technology utilizing smart farm and big data of facility horticulture contribute to farm productivity. Crop growth information gathering and analysis solutions were developed to analyze the productivity change factors calculated from hydroponics tomato farms and strawberry farms. The results of this study are as follows. The application range of the leaf temperature was verified to be variously utilized such as house ventilation in the facility, opening and closing of the insulation curtain, and determination of the initial watering point and the ending time point. Second, it is necessary to utilize water content information of crop growth. It was confirmed that the crop growth rate information can confirm whether the present state of crops is nutrition or reproduction, and can control the water content artificially according to photosynthesis ability. Third, utilize EC and pH information of crops. Depending on the crop, EC values should be different according to climatic conditions. It was confirmed that the current state of the crops can be confirmed by comparing EC and pH, which are measured from the supplied EC, pH and draining. Based on the results of this study, it can be confirmed that the productivity of smart farm can be affected by how to use the information of measurement growth.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.171-177
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• 2020

Under the influence of the Fourth Industrial Revolution, there are many tries to promote productivity enhancement and competitiveness by adapting smart farm technology that converges ICT technologies in agriculture. This smart farming technology is emerging as a new paradigm for future growth in agriculture. The development of real-time cultivation environment monitoring and automatic control system is needed to implement smart farm. Furthermore, the development of intelligent system that manages cultivation environment using monitoring data of the growth of crops is required. In this paper, a fast and efficient development method for implementing a cloud-based smart farm management system using a highly compatible and scalable web platform is proposed. It was verified that the proposed method using the web platform is effective and stable system implementation through the operation of the actual implementation system.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.63-68
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• 2018

Research on smart farms centering on greenhouse cultivation is actively under way due to the decrease in agriculture population and aging, but in the case of vegetables such as vegetables, outdoor cultivation is 70%. Therefore, there is a need to improve productivity and prevent soil contamination by automating, cultivating, and intelligentizing the outdoor cultivation of agriculture crops. In this paper, we show the case of establishing a outdoor production system using the Internet of things and define the environmental variables in the outdoor production system. By measuring soil temperature, water content, electrical conductivity and acidity through sensors, LoRa communication module transmits the information to the outdoor production system. The outdoor production system controls the amount of fertilizer and the volume of water based on this sensor data. We have developed a system that manages a wide range of crops using LoRa technology, which is a suitable communication method for cultivating crops, and manages production volume and sales performance.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1423-1431
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• 2018

IoT(Internet of Things) is applied to technologies such as agriculture and dairy farming, making it possible to cultivate crops easily and easily in cities.In particular, IoT technology that intelligently judge and control the growth environment of cultivated crops in the agricultural field is being developed. In this paper, we propose a method of predicting the growth environment of plants by learning the moisture supply cycle of plants using the intelligent object internet. The proposed system finds the moisture level of the soil moisture by mapping learning and finds the rules that require moisture supply based on the measured moisture level. Based on these rules, we predicted the moisture supply cycle and output it using media, so that it is convenient for users to use. In addition, in order to reduce the error of the value measured by the sensor, the information of each plant is exchanged with each other, so that the accuracy of the prediction is improved while compensating the value when there is an error. In order to evaluate the performance of the growth environment prediction system, the experiment was conducted in summer and winter and it was verified that the accuracy was high.

• Journal of IKEEE
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• v.22 no.1
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• pp.53-60
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• 2018

Smart farm dissemination by continuously developing IoT is one of the best solution for decreasing labor in Korea farming area because of ageing. For this reason, the number of Smart farm in Korea is being increased. The Smart farm can control farming environment such as temperature for human. Specially, The important thing is controlling proper temperature for farming. In order to control the temperature, legacy smart farms are usually using pans or air conditioners which can control the temperature. However, those devices result in increasing production cost because the electric power consumption is high. For this reason, we propose a smart farm which can predict the proper temperature after an hour by using Deep learning to minimize the electric power consumption by controlling window instead of pans or air conditioners. We can see the 83% of electric power saving by means of the proposed smart farm.

• Journal of Bio-Environment Control
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• v.27 no.2
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• pp.166-172
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• 2018

This study set out to conduct a field survey with smart greenhouse-based farms in seven types to figure out the actual state of smart greenhouses distributed across the nation before selecting a system to implement an optimal greenhouse environment and doing a research on higher productivity based on data related to crop growth, development, and environment. The findings show that the farms were close to an intelligent or advanced smart farm, given the main purposes of leading cases across the smart farm types found in the field. As for the age of farmers, those who were in their forties and sixties accounted for the biggest percentage, but those who were in their fifties or younger ran 21 farms that accounted for approximately 70.0%. The biggest number of farmers had a cultivation career of ten years or less. As for the greenhouse type, the 1-2W type accounted for 50.0%, and the multispan type accounted for 80.0% at 24 farms. As for crops they cultivated, only three farms cultivated flowers with the remaining farms growing only fruit vegetables, of which the tomato and paprika accounted for approximately 63.6%. As for control systems, approximately 77.4% (24 farms) used a domestic control system. As for the control method of a control system, three farms regulated temperature and humidity only with a control panel with the remaining farms adopting a digital control method to combine a panel with a computer. There were total nine environmental factors to measure and control including temperature. While all the surveyed farms measured temperature, the number of farms installing a ventilation or air flow fan or measuring the concentration of carbon dioxide was relatively small. As for a heating system, 46.7% of the farms used an electric boiler. In addition, hot water boilers, heat pumps, and lamp oil boilers were used. As for investment into a control system, there was a difference in the investment scale among the farms from 10 million won to 100 million won. As for difficulties with greenhouse management, the farmers complained about difficulties with using a smart phone and digital control system due to their old age and the utter absence of education and materials about smart greenhouse management. Those difficulties were followed by high fees paid to a consultant and system malfunction in the order.