• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.681-682
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• 2018

There are many damages due to the unstable climate. Among them, agriculture will be the most affected by the climate. Agriculture has limited crops that can be grown along with climate and seasons. We will develop smart farms that integrate information technology(ICT) into agricultural technology and improve the productivity of existing agricultural technology. It uses Raspberry Pi and Arduino to control the hardware and software, and uses various sensors to recognize the environment necessary for crop cultivation and maintain optimal environment. In addition, it is possible to manipulate these Smart Farm as mobile or personal PC to implement a flexible Smart Farm.

• Journal of Korean Society of Rural Planning
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• v.26 no.3
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• pp.115-126
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• 2020

This study designed an extended technology acceptance model incorporating and combining TPB, TAM, UTAUT, and IDT, which are known to be useful in explaining technology acceptance intention, to analyze antecedents affecting smart farm acceptance intention from the perspective of policy handlers. In the model of this study, nine independent variables were set, including subjective norm, perceived behavioral control, attitude, perceived usefulness, performance expectation, effort expectation, social impact, promotion condition, and fitness. The effect of these variables on farm acceptance intention was analyzed. The study found that four factors, including perceived behavioral control, perceived usefulness, social impact, and fitness, had positive effects on the acceptance intention of smart farms. Of these, perceived usefulness had the highest impact. In conclusion, all the TPB, TAM, UTAUT, and IDT applied to the research hypothesis to explain the smart farm acceptance intention included on or more variables with significant effects. In other words, these theories were evaluated as useful to explain the acceptance intention of smart farms.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.211-218
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• 2022

Pandemic situations such as COVID-19 can occur supply chain crisis. Under the supply chain crisis, delivering farm products from the farm to the city is also very challenging. Therefore it is essential to prepare food sufficiency people who live in a city. We firmly insist on food self-production/consumption systems in each home. However, since it is impossible to grow high-quality crops without expertise knowledge. Therefore expert system is essential to grow high-quality crops in home. To address this problem, we propose a smart kitchen farm as a data-based monitoring system and platform with ICT convergence technology. Our proposed approach 1) collects data and makes judgments based on expert knowledge for home users, 2) increases product quality of the smart kitchen farms by predicting abnormal/normal crops, and 3) controls each personal home cultivation environment through data-based monitoring within the smart central server. We expect people can cultivate high-quality crops in thir kitchens through this system without expert knowledge about cultivation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.733-739
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• 2019

Water level measurement is highly demanding in IoT monitoring areas such as smart factory, smart farm, and smart fish farm. However, existing water level indicators are limited to be used in industrial fields as commercial products due to the high cost of sensors and the complexity of algorithms used. In order to solve these problems, our paper proposed methods using an infrared distance sensor as well as a hall sensor for the water level measurement, both of which are contactless smart sensors. Data errors caused by the inaccuracy of existing sensors were decreased by applying new simple structures so that versatility is enhanced. The performance of our method was validated using experiments based on simulations. We expect that our new water depth indicator can be extended to a general-purpose water level monitoring system based on IoT technology.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1121-1128
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• 2016

Our country of agriculture suffers problems such as aging, population decline, agricultural decline etc. To solve this problem, in the country, it is interest in Smart Farm System, a convenient and efficient system for the production through the convergence of ICT technology and agriculture. However, because of expensive construction costs and difficulty in securing human resources and training for Operating system, they are struggling to spread the actual farmers. Therefore, it is necessary to develop smart farm techniques suitable for such customized domestic environment. This study designed a system for collecting environment date in a greenhouse based on the low-cost embedded devices, and designed and implemented for the Web application that a user can easily use system. The implementation of the system lowers deployment costs and is expected to increase largely the spread of Smart Farm it can be easily accessed by using the smart phone.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.743-748
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• 2021

Amid the global population growth and climate change, high-tech smart farm technology that combines agriculture and ICT is actively being researched in Korea to solve sustainable crises such as declining population of agricultural and livestock industries. Existing smart farms are growing mainly on crops with low price competitiveness. Food consumption structures are becoming more sophisticated and diverse, and as agricultural consumption patterns change, the smart farm system also needs to be optimized for growing high-value special crops. To this end, an integrated ICT management system was designed and implemented by establishing a containerized smart farm environment specialized in growing sprout ginseng. Through this, it is possible to implement high-tech agricultural production and lead new future convergence industries through the convergence of ICT, agriculture, and the latest technologies and farming.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.15-20
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• 2022

With the arrival of the fourth industrial era, demand for agriculture is increasing day by day, and smart farm technology, in which computers manage agriculture in line with the current situation, is developing. However, agricultural workers who use it find it difficult to set up and use a management system for smart farms. This paper aims to establish a Lab-View smart farm management system to facilitate the use of a control program for ICT technology farms (hereinafter referred to as smart farms), one of the promising projects of the next industrial revolution. Based on Lab-View, users simply set the type of crops they want to grow, set appropriate temperature/humidity data for each set crop, and collect data in real time through sensors and store it in DB. This functionality maximizes convenience and usability in terms of users.

• 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.27-30
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• 2018

We design an expert system for tomato smart farm using decision trees and construct a control system with decision structure similar to that of farmers by using the data generated by factors that vary depending on the surrounding environment of each house. At present, Smart farm's control system does not control itself like the way farmers have done so far. Therefore, the dependency of smart farm control system is still not high. Direct intervention by farmers is indispensable for environmental control based on surrounding environment such as sensor value in smart farm. Therefore, we aimed to design a controller that incorporates decision trees into the expert system to make a system similar to the decision making of farmers. Prior to controlling the equipment in the house, it automatically selects the most direct effect among the various environmental factors, and then builds an expert system for complex control by including criteria for decision making by farmers. This study focused on deriving results using data without using heavy tools. Data is coming out of many smart farms at present. We expect this to be a standard for a methodology that allows farmers to access quickly and easily and reduce direct intervention.