• Journal of Advanced Technology Convergence
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• v.1 no.1
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• pp.1-6
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• 2022

Since 2015, the government has been making efforts to distribute Korean smart farms. However, the supply is limited to large-scale facility vegetable farms due to the limitations of technology and current cultivation research data. In addition, the efficiency and reliability compared to the introduction cost are low due to the simple application of IT technology that does not consider the crop growth and cultivation environment. Therefore, in this paper, data analysis services was performed based on public and external data. To this end, a data-based target smart farm system was designed that is suitable for the situation of farms growing in facilities. To this end, a farm risk information notification service was developed. In addition, light environment maps were provided for proper fertilization. Finally, a disease prediction model for each cultivation crop was designed using temperature and humidity information of facility farms. Through this, it was possible to implement a smart farm data service by linking and utilizing existing smart farm sensor data. In addition, economic efficiency and data reliability can be secured for data utilization.

• Transactions of Materials Processing
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• v.32 no.3
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• pp.153-159
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• 2023

This paper presents the optimal design of a heating system using radiant heating elements for application in smart farms. Smart farming, an advanced agricultural technology, is based on artificial intelligence and the internet of things and promotes crop production. Temperature and humidity regulation is critical in smart farms, and thus, a heating system is essential. Radiant heating elements are devices that generate heat using electrical energy. Among other applications, radiant heating elements are used for environmental control and heating in smart farm greenhouses. The performance of these elements is directly related to their electrical energy consumption. Therefore, achieving a balance between efficient electrical energy consumption and maximum heating performance in smart farms is crucial for the optimal design of radiant heating elements. In this study, the size, electrical energy supply, heat generation efficiency, and heating performance of radiant heating elements used in these heating systems were investigated. The effects of the size and electrical energy supply of radiant heating elements on the heating performance were experimentally analyzed. As the radiant heating element size increased, the heat generation efficiency improved, but the electrical energy consumption also increased. In addition, increasing the electrical energy supply improved both the heat generation efficiency and heating performance of the radiant heating elements. Based on these results, a method for determining the optimal size and electrical energy supply of radiant heating elements was proposed, and it reduced the electrical energy consumption while maintaining an appropriate heating performance in smart farms. These research findings are expected to contribute to energy conservation and performance improvement in smart farming.

• Journal of People, Plants, and Environment
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• v.23 no.6
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• pp.655-665
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• 2020

Background and objective: This study was conducted to compare the cultivation environment, growth of cut flowers, and management performance of conventional farms and smart farms growing the standard cut chrysanthemum, 'Jinba'. Methods: Conventional and smart farms were selected, and facility information, cultivation environment, cut flower growth, and management performance were investigated. Results: The conventional and smart farms were located in Muan, Jeollanam-do, and conventional farming involved cultivating with soil culture in a plastic greenhouse, while the smart farm was cultivating with hydroponics in a plastic greenhouse. The conventional farm did not have sensors for environmental measurement such as light intensity and temperature and pH and EC sensors for fertigation, and all systems, including roof window, side window, thermal screen, and shading curtain, were operated manually. On the other hand, the smart farm was equipped with sensors for measuring the environment and nutrient solution, and was automatically controlled. The day and night mean temperatures, relative humidity, and solar radiation in the facilities of the conventional and the smart farm were managed similarly. But in the floral differentiation stage, the floral differentiation was delayed, as the night temperature of conventional farm was managed as low as 17.7℃ which was lower than smart farm. Accordingly, the harvest of cut flowers by the conventional farm was delayed to 35 days later than that of the smart farm. Also, soil moisture and EC of the conventional farm were unnecessarily kept higher than those of the smart farm in the early growth stage, and then were maintained relatively low during the period after floral differentiation, when a lot of water and nutrients were required. Therefore, growth of cut flower, cut flower length, number of leaves, flower diameter, and weight were poorer in the conventional farm than in the smart farm. In terms of management performance, yield and sales price were 10% and 38% higher for the smart farm than for the conventional farm, respectively. Also, the net income was 2,298 thousand won more for the smart farm than for the conventional farm. Conclusion: It was suggested that the improved growth of cut flowers and high management performance of the smart farm were due to precise environment management for growth by the automatic control and sensor.

• 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.

• Advanced Industrial SCIence
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• v.1 no.2
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• pp.24-29
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• 2022

Recently, smart farm research that applies IoT technology to various farms is being actively conducted to improve agricultural cooling power and minimize cost reduction. In particular, methods for automatically and remotely controlling environmental information data around smart farms through IoT devices are being studied. This paper proposes a processing model that can maintain an optimal growth environment by monitoring environmental information data collected from smart farms in real time based on machine learning. Since the proposed model uses machine learning technology, environmental information is grouped into multiple blockchains to enable continuous data collection through rich big data securing measures. In addition, the proposed model selectively (or binding) the collected environmental information data according to priority using weights and correlation indices. Finally, the proposed model allows us to extend the cost of processing environmental information to n-layer to a minimum so that we can process environmental information in real time.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.31-37
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• 2018

In this work, we propose an analytic framework for evaluating the quality of service and dimensioning the link capacity in the gateway router of a smart farm with a greenhouse eco-management system. Specifically, we focus on the gateway router of an IoT network that provides an access service for smart farms. We design the link capacity of a gateway router that is used for the remote management of the greenhouse eco-management system to accommodate both time-critical and delay-tolerant traffic in a greenhouse LAN. For this purpose, we first investigate the ecosystem for smart farm, and we define the specification and requirements of the greenhouse eco-management system. Second, we propose a system model for the link capacity of a gateway that is required to guarantee the delay performance of time-critical applications in the greenhouse LAN. Finally, the validity of the proposed system is demonstrated through a series of numerical experiments.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1519-1530
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• 2020

As livestock farms grow in size, the number of breeding individuals increases, making it difficult to manage livestock. Livestock farms require an integrated management system such as a monitoring system, an access control system, and an abnormal behavior detection system to manage livestock houses. In this paper, a smart korean cattle livestock management system using IoT and AI technology was proposed for livestock management in livestock farms. The smart korean cattle farm management system consists of a monitoring and control system, a vehicle access management system, and an abnormal cattle behavior detection system. It is expected that this will help manage large-scale livestock houses, and additional research is needed to improve the performance of abnormal behavior detection in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.285-286
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• 2021

With the recent development of Internet of Things (IoT) technology, smartization technology is expanding to the fields of agriculture, livestock, and fisheries, and smartization is in progress. In this smart technology, the most important thing is how to measure the data in the field and transmit it to the management system. Currently, the sensors used in the construction of smart farms and other livestock houses and farms are measuring and monitoring smart farms and other environmental conditions through various sensors such as temperature, humidity, CO gas, CO2, hydrogen, and O2. The communication method between these sensors and the HMI (Human Machine Interface) module that controls and manages the smart farm is still mainly using the RS-485-based modbus-RTU method. In this paper, we intend to design the MCU module for HMI so that various sensor modules can be connected to manage data through the RS-485-based Modbus method so that the sensor data required for smart farm construction can be managed by the HMI module.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.177-182
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• 2021

The fourth industrial revolution based on information and communication technology (ICT) becomes the center of society, and the overall industrial structure is also changing significantly. ICT refers to the hardware of information devices and the software technologies required for the operation and information management of these devices, and any means of collecting, producing, processing, preserving, communicating and utilizing them. ICT is integrated into industries and services or combined with new technologies in various fields such as robotics and nanotechnology to connect all products and services to the network. The development of ICT, which continuously creates new products and services, has spread to all sectors of the industry, affecting not only daily life but also the livestock sector recently. In agriculture, ICT technology can reduce production costs by efficiently managing labor and energy because it can improve quality and yield based on data on environmental and growth information such as temperature, humidity, light and soil. In particular, smart livestock is considered suitable for achieving livestock management goals because it can reduce labor force and improve productivity by remotely and automatically managing accurate information necessary for raising and breeding livestock with ICT devices. The purpose of this study is to propose the need for ICT technology by comparing farm productivity before and after ICT is introduced. The method of the study is to compare the productivity before and after the introduction of ICT in Korean beef farms, pig farms, and poultry farms. The effectiveness of the study proved the excellence of ICT technology through the production results before ICT introduction and the productivity improvement case of livestock farms that efficiently operated manpower management and reduced labor force after ICT introduction. The conclusion of this paper is to present the need for smart livestock through ICT adoption through case study results.