• Journal of Korean Society for Quality Management
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• v.46 no.3
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• pp.707-716
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• 2018

Purpose: This study aims to analyze the management performance and production efficiency of strawberry farmers who introduced smart farming, one of the primary symbols of the fourth industrial revolution in the agricultural sector. Methods: We conducted an empirical survey of strawberry farms using smart farming and analyzed production efficiency using DEA method. Results: First, difficulties for strawberry farmers introducing smart farming included time and money spent on parts replacement and additional costs due to compatibility problems with existing facilities after the adoption. Second, strawberry farmers using smart farming increased their total income by producing higher yield and improving quality thanks to the competent growth management. Third, the analysis of production efficiencies before and after smart farming found improvement in technical efficiency, pure technical efficiency, and scale efficiency. But, the gaps in technical and scale efficiencies among the farms widened. Conclusion: Based on the results above, following policy suggestions are offered. First, an environment control technology suitable for strawberry farming needs to be developed. Second, the smart farming technology needs to be standardized by the government. Third, new smart farm models need to be developed to accommodate to the facilities and environment in Korea through collecting big data including high-quality data on the environment, growth, and yield. Fourth, continuing education needs to be provided to narrow the gap in smart farming technology among strawberry farmers.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.98-103
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• 2021

Recently, as the need for food resources has increased both domestically and internationally, support for the agricultural sector for stable food supply and demand is expanding in Korea. However, according to recent media articles, the biggest problem in rural communities is the unstable profit structure. In addition, in order to confirm the profit structure, profit forecast data must be clearly prepared, but there is a lack of auxiliary data for farmers or future returnees to predict farm income. Therefore, in this paper we analyzed data over the past 15 years through time series analysis and proposes an artificial intelligence farm income prediction algorithm that can predict farm household income in the future. If the proposed algorithm is used, it is expected that it can be used as auxiliary data to predict farm profits.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.377-389
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• 2021

This paper presents a framework design of a digital twin system for a vertical smart farm. In this paper, a framework of digital twin systems establishes three factors: 1) Client 2) IoT gateway, and 3) Server. Especially, IoT gateway was developed using the Eclipse Ditto, which has been commonly used as the standard open hardware platform for digital twin. In particular, each factor is communicating with the client, IoT gateway, and server by defining the message sequence such as initialization and data transmission. In this paper, we describe the digital twin technology trend and major platform. The proposed design has been tested in a testbed of the lab-scale vertical smart-farm. The sensor data is received from 1 Jan to 31 Dec 2020. In this paper, a prototype digital twin system that collects environment and control data through a raspberry pi in a plant factory and visualizes it in a virtual environment was developed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.180-186
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• 2020

ICT convergence using smart livestock is that in the first-generation dairy smart farm model, each device made by several manufacturers uses its own communication method, limiting the mutual operation of each device. This study uses a model based on open control technology to secure interoperability of existing ICT devices and to manage data efficiently. The open integrated control derived from this process is the software interface structure of Open API. It is an observer that serves as real-time data collection according to the communication method of ICT devices and sensors located at each end. It consists of a broker that connects and transmits to the upper integrated management server. As a result of the performance analysis through verification of two first-generation dairy smart farm model sites, the average daily milk production increased compared to the previous year (farm A 5.13%, farm B 1.33%, p<0.05). Cow days open (DO) was reduced by 17.5% on farm A and 13.3% for farm B(p<0.05). Cows require an adaptation period after the introduction of the ICT device, but if continuous effects are observed, the effect of production can be expected to increase gradually.

• 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 Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.117-122
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• 2018

This paper suggests smart agricultural diffusion strategy through advanced ICT technology. Today, the world is threatened by environmental pollution and traditional warming due to global warming, and the decrease in agricultural workers due to low fertility and aging is expected to bring social problems to future food resources. The convergence of ICT technology and agriculture is not a labor-intensive primary industry, but a new paradigm that includes cultivation, manufacturing and services. It is urgent to spread smart farm technology that can supply stable food with low labor force. In this paper, we review the current state of smart farm technology, analyze the impediments to diffusion, and present the direction of smart agricultural development in the future by upgrading ICT technology.

• Journal of Korean Society of Rural Planning
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• v.25 no.2
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• pp.15-21
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• 2019

The agricultural environment is changing and becoming more advanced due to the influence of the 4th Industrial Revolution. From the basic plan of Rural Informatics to the current level of 2nd generation smart farms aimed at improving productivity using Big data, cloud network and more IoT technology. We are continuing to provide support and research and development. However, many problems remain to be solved in order to supply and settle smart farms in Korea. The purpose of this study is to provide a method of collecting and sharing data on farming environment and to help improve the income and productivity of farmers based on collected data. In the case of hog farm, the multiple sensors for environmental data like temperature, humidity and gases and the network environment for connecting the internet were established. The environment sensor was made using the ESP8266 Node MCU board as micro-controller, DHT22 sensor for temperature and humidity, and MQ series sensors for various gases in the hog pens. The network sensor was applied experimentally for one month and the environmental data of the hog farm was stored on a web database. This study is expected to raise the importance of collecting and managing the agricultural and environmental data, for the next generation farmers to understand the smart farm more easily and to try it by themselves.

• Smart Media Journal
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• v.12 no.1
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• pp.47-53
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• 2023

Recently, research on a smart farm that creates new values by combining information and communication technology(ICT) with agriculture has been actively done. In order for domestic smart farm technology to have productivity at the same level of advanced agricultural countries, automated decision-making using machine learning is necessary. However, current smart greenhouse data collection technologies in our country are not enough to perform big data analysis or machine learning. In this paper, we design and implement a smart greenhouse data preprocessing system for autonomous machine learning. The proposed system applies target data to various preprocessing techniques. And the proposed system evaluate the performance of each preprocessing technique and store optimal preprocessing technique for each data. Stored optimal preprocessing techniques are used to perform preprocessing on newly collected data

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.60-71
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• 2019

As IoT systems such as smart farms and smart cities become popular, a large amount of data collected from many sensor nodes is sent to a server in the Internet, which causes network traffic explosion, delay in delivery, and increase of server's workload. To solve these problems, the concept of fog computing has been proposed to store data between IoT systems and servers. In this study, we implemented a software platform of the fog node and applied it to the prototype smart farm system in order to check whether the problems listed above can be solved when using the fog node. When the fog node is used, the time taken to control an IoT device is lower than the response time of the existing IoT device-server case. We confirmed that it can also solve the problem of the Internet traffic explosion and the workload increase in the server. We also showed that the intelligent control of IoT system is feasible by having the data visualization in the server and real time remote control, emergency notification in the fog node as well as data storage which is the basic capability of the fog node.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.17-26
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• 2024

This study aimed to design and build a web-based decision support system for wheat cultivation management. The system is designed to collect and measure the weather environment at the growth stage on a daily basis and predict the soil moisture content. Based on this, APSIM, one of the process-based crop models, was used to predict the potential yield of wheat cultivation in real time by making decisions at each stage. The decision-making system for wheat crop management was designed to provide information through a web-based dashboard in consideration of user convenience and to comprehensively evaluate wheat yield potential according to past, present, and future weather conditions. Based on the APSIM model, the system estimates the current yield using past and present weather data and predicts future weather using the past 40 years of weather data to estimate the potential yield at harvest. This system is expected to be developed into a decision support system for farmers to prescribe irrigation and fertilizer in order to increase domestic wheat production and quality by enhancing the yield estimation model by adding influence factors that can contribute to improving wheat yield.