• Journal of Korea Society of Industrial Information Systems
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• v.20 no.4
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• pp.47-54
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• 2015

With the spread of smart technology, in the personal and office equipment, many of the products are equipped with smart features. These changes have opened the era that can enforce the user's operation from a remote location. In this study, we design and implement a smart multi-plug system to make a multi-plug installed in the home can be operated anytime and anywhere via wireless network technologies add smart feature to a multi-plug that can be seen easily around us, it is intended to complement the limitations of traditional multi-plugs. The smart multi-plug proposed in this paper, by operating the plug remotely from smart devices at anytime and from anywhere, is possible to provide services required for the weak users such as elderly and children, it is expected that the proposed system are widely used in the various areas requiring remote control of the power supply including u-Farming, terrarium, and smart home, and so on.

• 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 Korean Society for Quality Management
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• v.52 no.2
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• pp.201-220
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• 2024

Purpose: Smart farming is related to the low carbon certification system as it provides many opportunities to cultivate and manage crops in an eco-friendly, thereby reducing carbon footprint. However, there is a significant lack of consumer perception research on low carbon labels for smart farms vegetables. Therefore, this study aims to investigate consumer perceptions of smart farm vegetable and low carbon labels. Methods: This study manipulated cultivation type(general vs. smart farm) and low carbon labels (yes vs. no) as experimental stimuli. Measurement questions and the research model were validated through confirmatory factor analysis and reliability analysis. Hypotheses testing were conducted using SPSS 29.0, AMOS 28.0. Results: The results of the study showed no significant difference in consumers perceived naturalness based on cultivation types, and there was also no moderating effect of the low carbon label. There was no difference between environmental benefits and health benefits according to the cultivation type. Perceived naturalness had a significant effect on both environmental and health benefits, and environmental benefits showed a higher impact relationship. These benefits positively affected attitudes and willingness to pay a premium, Environmental benefits had a higher impact on attitudes, while health benefits had a higher impact on willingness to pay a premium. Lastly, attitudes were found to have a significant impact on the willingness to pay a premium. Conclusion: This study is valuable in that it investigated consumer perceptions of smart farms and low carbon labels that have not been previously studied. It compares the environmental and health benefits, confirming their influence on attitudes and willingness to pay a premium. The results suggest a potential expansion in academic research on smart farming and low carbon labels, offering practical insights for marketing strategies and policies for relevant companies.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.388-393
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• 2024

The convergence of machine learning and smart farm is becoming more and more important. The purpose of this research is to quantitatively analyze machine learning and smart farm with bibliographic data from 2013 to 2023. This study analyzed the 251 articles, filtered from the Web of Science, with regard to the article publication trend, the article citation trend, the top 10 research area, and the top 10 keywords representing the articles. The quantitative analysis results reveal the four points: First, the number of article publications in machine learning and smart farm continued growing from 2016. Second, the article citations in machine learning and smart farm drastically increased since 2018. Third, Computer Science, Engineering, Agriculture, Telecommunications, Chemistry, Environmental Sciences Ecology, Material Science, Instruments Instrumentation, Science Technology Other Topics, and Physics are top 10 research areas. Fourth, it is 'machine learning', 'smart farming', 'internet of things', 'precision agriculture', 'deep learning', 'agriculture', 'big data', 'machine', 'smart' and 'smart agriculture' that are the top 10 keywords composing authors' keywords in the articles in machine learning and smart farm from 2013 to 2023.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.603-606
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• 2015

The post-production of smart farming defines the stage that the final products are delivered from producer to consumers via market on ICT network. It deals with the process of product packaging and distribution from producer to consumer with marketing strategy. This focus on reference model for post-production service including specialization, centralization of product delivery, and just-in-time delivery, and marketing system on the network. It defines a significant function component on post-production stage. The producer plays a significant role in economy being one of the main contributors to the many customers. This articles suggest the effective product distribution service which requires delivering the right product, in the right quantity, in the right condition, to the right place, at the right time, for the right cost, and encompassing global marketing based on ICT network, will be provided[1].

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.405-407
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• 2017

The environmental change is affecting the farm products like tomato, and pepper, etc. This affects to lead smart farming yield. What is more, this inconstant conditions cause the farms to be infected by variety diseases. Therefore ICT technology is needed to detect and prevent the crops from being effected by diseases. This article suggests the procedure to help producer for identifying farms disease based on the detected image. This detects the kind of diseases with comparing the trained image data before and after disease emergence. First step monitors an image of farms and resize it. Its features are extracted on parameters such as color, and morphology, etc. The next steps are used for classification to classify the image as infected or non-infected. on the bassis of detection algorithm.

• 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.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.104-119
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• 2019

• Smart Media Journal
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• v.5 no.3
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• pp.17-24
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• 2016

Korean agriculture is currently experiencing difficulties as a cause of rural depopulation, aging of rural population, grain self-sufficiency rate decline, and deepening of climate change. It is necessary to ensure our country's agriculture industrial competitiveness in accordance with opening of FTA imports expanded. To ensure the underdeveloped competitive, Korean government defines the 3rd generation model from 1st generation model to extend the smart farms of Korean types. The agriculture smarting overcomes the growth limitations of agriculture, and efforts to develop 6th + ${\alpha}$ industry. In this paper, We define and verify the IoF(Internet of Farming)-Cloud based substantial services about 2rd generation model, and propose a greenhouse of IoF-Cloud testbed.

• Smart Media Journal
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• v.3 no.1
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• pp.16-21
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• 2014

The demand for food rises proportionally as population grows. To be able to achieve sustainable supply of livestock products, efficient farm management is a necessity. With the advancement in technology it also brought innovations that could be harness in order to achieve better productivity in animal production and agriculture. Precision Livestock Farming (PLF) is a budding concept of making use of smart sensors or available devices to automatically and continuously monitor and manage livestock production. With this concept, this paper introduces a swine management system that integrates image processing technique for weight monitoring. This system captures pig images using camera, evaluate and estimate the weight base on the captured image. It is comprised of Pig Module, Breeding Module, Health and Medication Module, Weighr Module, Data Analysis Module and Report Module to help swine farm administrators better understand the performance and situation of the swine farm. This paper aims to improve the management in both small and big livestock raisers.