• Journal of Digital Convergence
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• v.20 no.2
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• pp.203-215
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• 2022

The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.413-422
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• 2022

Smart farms are receiving a lot of attention as a way to solve the chronic labor shortage and aging problems in agriculture. The smart farm industry, called the 6th industrial revolution, needs to strengthen its competitiveness. In order to apply innovative IT technology to agriculture, it is important to collect and analyze information about prior research or patents. This paper examines smart farm patent trends through 5,789 patent data related to smart farm using the domestic patent information search service(KIPRIS). This paper examines the domestic patent trends of smart farm information through keyword network, ego network, simultaneous appearance network, and bigram network analysis. As a result of network analysis related to smart farm patents, patents related to smart farm systems and control technologies were the most common. This paper can provide help in setting the direction of future smart farm-related patent research.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.35-41
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• 2022

Heavy snow damage can be prevented in advance with an appropriate security system. To develop the security system, we developed a model that predicts snow depth after a few hours when the snow depth is observed, and utilized it to calculate a failure probability with various types of greenhouses and observed snow depth data. We compared the Markov chain model and Bayesian long short-term memory models with varying input data. Markov chain model showed the worst performance, and the models that used only past snow depth data outperformed the models that used other weather data with snow depth (temperature, humidity, wind speed). Also, the models that utilized 1-hour past data outperformed the models that utilized 3-hour data and 6-hour data. Finally, the Bayesian LSTM model that uses 1-hour snow depth data was selected to predict snow depth. We compared the selected model and the shifting method, which uses present data as future data without prediction, and the model outperformed the shifting method when predicting data after 11-24 hours.

• Journal of Convergence for Information Technology
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• v.10 no.2
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• pp.15-20
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• 2020

In this paper, we propose an ICT-based smart farm design. At present, the decrease in rural population is naturally inevitable due to the decrease of the total population. The economic burden on each farm grows with increasing labor costs. As a solution to this, the necessity of spreading smart farms using computing resources is emerging. The proposed system utilizes the ICT technology emerging from the Fourth Industrial Revolution. We will use big data analysis to collect a large amount of data and propose a platform for managing collected data and providing efficient services. The proposed platform consists of SOA service layer, middleware layer, resource pool layer and physical resource layer. ICT-based smart farm service can reduce costs and be easy to install and manage because ICT-based smart farm service provides only necessary functions from the user's point of view.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.104-107
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• 2021

Unity programs are largely recognized as game development tools. However, it has many functions built in, so it can be applied to various fields as well as game development. Therefore, in this paper, we propose a smart farm environmental control program using Unity and Photon. The proposed program has vast compatibility that is not limited to specific devices, and it is very easy to build network systems for remote control. In addition, the proposed programs were installed on various devices such as pc and smartphones, making it easy to control the smart farm environment system. Through experiments, it was confirmed that data transmission and reception between Windows and Android, other operating system environments, and that smart farm systems were operating normally.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.313-318
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• 2016

Recently, the smart farm development using a PC and smart phone to manag the farm for improving competitiveness is in progress. In the smart farm, by using the various ICT technology including RFID, Wi-Fi, ZigBee, Wireless LAN, and etc., the growing environment of the crop and animals can be managed with the remote. By using the network including not only the TCP/IP based wired network but also ZigBee, Wireless LAN, and etc., each of the devices installed in the smart farm transmits the growing environment data to the server. So, smart farms have information and network security vulnerability. Therefore, we propose the method that analyzes the security vulnerability which can begenerated in the smart farm and user authentication method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.3
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• pp.19-32
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• 2020

This study analyzed the effects of ICT technical characteristics of agricultural industry on smart farm entrepreneurial intention by using smart farm effort expectation and smart farm acceptance intention as mediators for smart farm pre-founders. Sub-variables of the technical characteristics of agricultural industry ICT were classified into availability, economics, data convergence and scalability. 349 questionnaires collected from pre-founders living in the country were used for empirical analysis. SPSS v22.0 and Process macro v3.4 were used to analyze the data based on serial multiple mediation model. First, economics and scalability had a positive (+) effect on start-up intention. Second, availability, economics and scalability had a significant effect on effort expectation. Third, effort expectation had a significant positive effect on acceptance intention. Fourth, acceptance intention had a significant positive effect on start-up intention. Fifth, the indirect effects of economics on start-up intention were all significant through effort expectation, through acceptance intention and through both effort expectation and acceptance intention. Sixth, the indirect effect of data convergence on start-up intention was significant through acceptance intention. Seventh, the indirect effect of scalability on start-up intention was significant through effort expectation and through both effort expectation and acceptance intention. As a follow-up study, it is necessary to study for the mediating variables other than mediators introduced in the study or the moderated mediation analysis through the conditional process model in which the moderating variable is introduced.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.81-97
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• 2024

This study examines the effects of adopting Information and Communication Technology (ICT) in livestock farm management on farm sales revenue. Using the 2020 Census of Agriculture, Forestry, and Fisheries, a nationally representative data set constructed by Statistics Korea, this study focuses on a sample of 9,020 livestock farms in South Korea. We employ Propensity Score Matching (PSM) methods to address the potential selection bias between 2,076 farms that used ICT for livestock farm management and 6,944 farms that did not. The findings consistently show that the use of ICT significantly increases farm revenue, taking into account the selection bias. The utilization of ICT in livestock farms leads to a higher increase in sales revenue, particularly for farms with greater sales.

• 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.

• Convergence Security Journal
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• v.22 no.5
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• pp.69-80
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• 2022

Now that climate change and food resource security are becoming issues around the world, smart farms are emerging as an alternative to solve them. In addition, changes in the production environment in the primary industry are a major concern for people engaged in all primary industries (agriculture, livestock, fishery), and the resulting food shortage problem is an important problem that we all need to solve. In order to solve this problem, in the primary industry, efforts are made to solve the food shortage problem through productivity improvement by introducing smart farms using the 4th industrial revolution such as ICT and BT and IoT big data and artificial intelligence technologies. This is done through the public and private sectors.This paper intends to consider the minimum requirements for the smart farm data collection system for the development and utilization of smart farms, the establishment of a sustainable agricultural management system, the sequential system construction method, and the purposeful, efficient and usable data collection system. In particular, we analyze and improve the problems of the data collection system for building a Korean smart farm standard model, which is facing limitations, based on in-depth investigations in the field of livestock and livestock (pig farming) and analysis of various cases, to establish an efficient and usable big data collection system. The goal is to propose a method for collecting big data.