• Agribusiness and Information Management
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• v.6 no.2
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• pp.13-23
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• 2014

The world is in the process of a structural change related to ICT convergence knowledge industries. ICT is leading to the creation of new products and services, and is making our lives more convenient, safer, and more efficient. In advanced countries, many studies have been conducted with the goal of developing new business models converged with ICT, and this is also the case in the food industry. Korea possesses world-leading ICT, and if this ICT is applied to the food industry, a world-class new business model can be developed. The u-Food System, which is in the process of development in Korea, is a next-generation food system that can allow food providers, consumers, and distributors to access various types of information about food products, including traceability, distribution, safety, quality, and freshness, and manage this information. It is a future food system that converges ICT, biotechnology and sensing technology with food. Based on the u-Food System, this paper will introduce the status of current smart quality distribution technologies that converge ICT (such as sensor tag, sensor network, LBS, GIS, and CDMA) with food technologies (such as traceability, quality, distribution management) to manage the safety and quality of fresh food in the distribution process.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.339-345
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• 2017

Recently, agriculture and the environment has changed dramatically due to global warming and abnormal weather. In particular, it is necessary to develop new agricultural techniques according to transforming the growing environment of agricultural crops. Therefore, "Smart Farm" building technology for controlling agricultural environment and improving efficiency for ICT technology development has recently been introduced. However, in reality, systematic and objective evaluation items are absent at various levels and management levels that affect the management environment of the smart farm. In this research, it derived the importance index among the factors associated with Smart Farm technology by AHP method. As a result, in order to evaluate comprehensive operation and management of the smart farm, the two evaluation fields(sensor device and control/information management system) were selected as the top evaluation items. These results mean that system that can detect the growth environment information of agricultural crops and control the growing environment is more important than anything, when smart farm is applied. It is judged that the results of this research can be used as basic data for making evaluation indicators associated with the introduction of smart palm technology in the future.

• Journal of Korean Society of Rural Planning
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• v.26 no.2
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• pp.25-37
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• 2020

The aim of this study is to assess the potential for fusion and convergence in industrial agriculture. First, we analyzed the types of industrial agriculture and applied the agri-business and ICT agriculture. After then, we analyzed the Project Potential Index (PPI) of the agri-business and ICT agriculture using the agricultural enterprise database provided from Rural Research Institute in Korea. The results revealed that Haenam have a high potential for agri-business project because of large number of farmers and annual sales. Wanju was considered as the suitable place for ICT agriculture project because of large area of greenhouse. This study was applied only 7 study area but the methodology suggested in this study could be widely used for assessing potential project various types of industrial agriculture.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.215-221
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• 2020

The paradigm of agriculture is also changing to address the problem of food shortages due to the increase of the world population, climate conditions that are increasingly subtropical, and labor shortages in rural areas due to aging population. With the development of Information Communication Technology (ICT), our daily lives are changing rapidly and heralds a major change in agricultural management. In a hyper-connected society, the introduction of high-tech into traditional Agriculture of the past is absolutely necessary. In the development process of Agriculture, the first generation produced by hand, the second generation applied mechanization, and the third generation introduced automation. The fourth generation is the current ICT operation and the fifth generation is artificial intelligence. This paper investigated Smart Farm that increases productivity through convergence of Agriculture and ICT, such as smart greenhouse, smart orchard and smart Livestock. With the development of sustainable food production methods in full swing to meet growing food demand, Smart Farming is emerging as the solution. In overseas cases, the Netherlands Smart Farm, the world's second-largest exporter of agricultural products, was surveyed. Agricultural automation using Smart Farms allows producers to harvest agricultural products in an accurate and predictable manner. It is time for the development of technology in Agriculture, which benchmarked cases of excellence abroad. Because ICT requires an understanding of Internet of Things (IoT), big data and artificial intelligence as predicting the future, we want to address the status of theory and actual Agriculture and propose future development measures. We hope that the study of the paper will solve the growing food problem of the world population and help the high productivity of Agriculture and smart strategies of sustainable Agriculture.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.523-529
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• 2020

Future information and communication technology (ICT) is constantly evolving and converging in diverse fields depending on the wireless environment, and the trend is being further developed to increase the speed of wireless networks. Future ICT is needed in many areas such as active senior & solo-economy, hyper-connected society, intelligent machine, industrial boundary collapse, secured self, and the sharing economy. However, a lot of research is needed to solve problems such as machine learning, security, prediction, unmanned technology, etc. Therefore, this paper describes some technologies developed in the areas of blockchain, fault diagnosis, security, agricultural ICT, cloud, life safety and care, and climate monitoring in order to provide insights into the future paradigm.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.428-436
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• 2014

The convergence technology of ICT(Information & Communication Technology) in agriculture is the main key of the future agricultural industry. Recently, many that by using big data it can improve crop growth-circumstance and agricultural Industry. However, the data of crop growth-circumstance has been not shared and operated separately by individual farm. Therefore, it is necessary to build the LOK(Linked Open Knowledge) system for Quality of Farming & Farm product. We research previous studies for big data and development of the corp growth-circumstance using big data system case. Also, we suggest to build LOK system for improving the domestic agricultural industry.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.271-272
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• 2016

Owing to the development of information communication technology(ICT), there are many changes taking place in the agriculture sector. As an example, an effective agricultural activities with ICT have brought an increase in agricultural production. On the other hand, these technology investment activities are very costly. Until recently, the technology development has been made in agricultural technology development perspective. This view, however, has been changed to production increase in accordance with the crop growth conditions. In this paper, we propose a device which can maintain an optimal crop growing environment. It is economic by utilizing the existing facility of farmhouse and can reduce the cost of crop growth environment improvement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.591-594
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• 2014

IT Convergence with agriculture is expected to bring more efficiency and quality improvement in producing, distributing, consuming of agricultural products with the aid of information processing and autonomous control technologies of the IT area. However, there exist many difficulties to establish services and systems to actualize the IT convergence service in the agricultural field to cope with various objects such as time-varying weather changes, growth condition of farm products, and continual diseases. In this paper, we suggest standard technology service requirements for ubiquitous environments agriculture based on network.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.147-157
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• 2022

This study tried to propose a smart farm technology strategy suitable for the domestic situation, focusing on the differentiation suitable for the domestic situation of ICT technology. In the case of advanced countries in the overseas agricultural industry, it was confirmed that they focused on the development of a specific stage that reflected the geographical characteristics of each country, the characteristics of the agricultural industry, and the characteristics of the people's demand. Confirmed that no enemy development is being performed. Therefore, in response to problems such as a rapid decrease in the domestic rural population, aging population, loss of agricultural price competitiveness, increase in fallow land, and decrease in use rate of arable land, this study aims to develop smart farm ICT technology in the future to create quality agricultural products and have price competitiveness. It was suggested that the smart farm should be promoted by paying attention to the excellent performance, ease of use due to the aging of the labor force, and economic feasibility suitable for a small business scale. First, in terms of economic feasibility, the ICT technology is configured by selecting only the functions necessary for the small farm household (primary) business environment, and the smooth communication system with these is applied to the ICT technology to gradually update the functions required by the actual farmhouse. suggested that it may contribute to the reduction. Second, in terms of performance, it is suggested that the operation accuracy can be increased if attention is paid to improving the communication function of ICT, such as adjusting the difficulty of big data suitable for the aging population in Korea, using a language suitable for them, and setting an algorithm that reflects their prediction tendencies. Third, the level of ease of use. Smart farms based on ICT technology for the development of the Industry6.0 (1.0(Agriculture, Forestry) + 2.0(Agricultural and Water & Water Processing) + 3.0 (Service, Rural Experience, SCM)) perform operations according to specific commands, finally suggested that ease of use can be promoted by presetting and standardizing devices based on big data configuration customized for each regional environment.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.8-13
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• 2019

Smart Farming has been regarded as an important application in information and communications technology (ICT) fields. Selecting crops for cultivation at the pre-production stage is critical for agricultural producers' final profits because over-production and under-production may result in uncountable losses, and it is necessary to predict crop production to prevent these losses. The ITU-T Recommendation for Smart Farming (Y.4450/Y.2238) defines plan/production consultation service at the pre-production stage; this type of service must trace crop production in a predictive way. Several research papers present that machine learning technology can be applied to predict crop production after related data are learned, but these technologies have little to do with standardized ICT services. This paper clarifies the relationship between agricultural consultation services and predicting crop production. A prediction scheme is proposed, and the results confirm the usability and superiority of machine learning for predicting crop production.