• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1305-1310
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• 2019

Modern agriculture is changing from resource-oriented agriculture to technology-oriented agriculture. Agriculture, which combines science and technology, is recognized as a new growth engine, and governments, local governments, research institutes, and industry are working together to develop and disseminate various devices necessary for smart farms to build intelligent smart farms. Recently, research is being conducted to build a more intelligent agricultural environment by building a cloud platform. In this paper, we propose a plan to build an urban energy - independent smart farm that can utilize leisure time and agricultural activities by utilizing the rooftop of a city. Also, by using IT technology, various data of smart farm can be managed on remote server, and HMI module for controlling internal environment of smart farm can be developed to manage smart farm automatically or semi-automatically. The service model suggests a model that can manage the internal environment of the smart farm based on mobile.

• 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 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.125-126
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• 2019

스마트농장과 스마트양식장으로 구분하여 첨단 정보통신기술(ICT), 사물인터넷(IoT), 인공지능(AI) 그리고 빅데이터 등이 적용된 국내외 스마트농장과 스마트양식장 사례와 해수를 이용한 해수온실의 사례 그리고 플로팅 팜과 스마트 플로팅 팜의 계획안 및 실제 사례를 분석했다. 사례분석을 통해 스마트 플로팅 팜에 적용되는 다양한 종류의 시스템을 분류하여 해수복합형 시스템 개념을 도출해냈다.

• 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.

• Water for future
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• v.47 no.2
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• pp.76-80
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• 2014

• KOREAN POULTRY JOURNAL
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• v.54 no.10
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• pp.174-178
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• 2022

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.649-655
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• 2020

This study determined effective educational strategies by investigating and analyzing the related educational demands for SMEs (small and medium-sized enterprises) in the 4th Industrial Revolution based area of smart farms. In order to derive the approprate educational strategies, Importance-Performance Analysis (IPA) and Borich's Needs Assessment Model were conducted based on the smart farm technological field. As a result, the education demand survey showed high demand for production systems and intelligent farm machinery. In detail, Borich's analysis showed the need for pest prevention and diagnosis technology (8.03), network and analysis SW linkage technology (7.83), and intelligent farm worker-agricultural power system-electric energy hybrid technology (7.43). In contrast, smart plant factories (4.09), lighting technology for growth control (4.46) and structure construction technology (4.62) showed low demands. Based on this, the IPA portfolio shows that the network and analysis SW linkage technology and the CAN-based complex center are urgently needed. However, the technology that has already been developed, such as smart factory platform development, growth control lighting technology and structure construction technology, was oversized. Based on these results, it is possible to strategically suggest the customized training programs for industrial sectors of SMEs that reflect the needs for efficiently operating smart farms. This study also provides effective ways to operate the relevant training programs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.180-186
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• 2020

ICT convergence using smart livestock is that in the first-generation dairy smart farm model, each device made by several manufacturers uses its own communication method, limiting the mutual operation of each device. This study uses a model based on open control technology to secure interoperability of existing ICT devices and to manage data efficiently. The open integrated control derived from this process is the software interface structure of Open API. It is an observer that serves as real-time data collection according to the communication method of ICT devices and sensors located at each end. It consists of a broker that connects and transmits to the upper integrated management server. As a result of the performance analysis through verification of two first-generation dairy smart farm model sites, the average daily milk production increased compared to the previous year (farm A 5.13%, farm B 1.33%, p<0.05). Cow days open (DO) was reduced by 17.5% on farm A and 13.3% for farm B(p<0.05). Cows require an adaptation period after the introduction of the ICT device, but if continuous effects are observed, the effect of production can be expected to increase gradually.

• Smart Media Journal
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• v.12 no.5
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• pp.65-72
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• 2023

Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.