• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.389-396
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• 2020

In this paper, a cultivation traceability system to implement smart agriculture developed and implemented, and in particular, devised a system that manages the cultivation traceability of grains that are difficult to grow in smart farms. Mobile and web programs based on smart devices are designed, and the collected information is stored in a DB server and can be used as big data. In addition, real-time location information and agricultural activity information can be matched using an electronic map(Vworld) based on GIS/LBS applying GPS of a mobile device. By designing the cultivation traceability information DB required in the field, the farmhouse, farmers, and cultivation information were developed to make it easy for managers to use, and implemented mobile and web programs in the field. The system is expected to raise the quality and safety management capabilities to the next level in response to variables such as labor saving effect and climate change.

• 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 Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.173-181
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• 2014

The Economist was implied most of the major fisheries are procured by aquaculture in 2030 affected by the Aquaculture Revolution. William Hallal was also predicted that amount of aquatic products will be about 50% of the total fishery in 2015. Various organizations had been conducted various u-farm researches and demonstration projects due to changing environment. This study aims to propose an ICT-based technologies and policies for the ICT-based smart system by identifying results and problems.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.12
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• pp.1231-1236
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• 2016

Paris Agreement signed in January 2015 is a new rule that will replace the existing Kyoto Protocol. The new agreement needs new demands and challenges to minimize carbon emissions. Especially, even though agricultural sector occupies only 1.8% in the national energy consumption, the portion of the energy being occupied in agricultural production costs very high. Although renewable energy and energy-saving facilities is being developed and disseminated for replacing fossil fuel energy and saving energy, the installation-rate is not enough high. Thus, this paper developed Korean-style smart farm system, and carried out the experiment to show the performance of energy savings through analyzing proper environment in domestic situation.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.299-305
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• 2021

Dissolved oxygen, pH, and temperature are the most important factors for fish farming because they affect fish growth and mass mortality of the fish. Therefore, fish farm workers must always check all pools on the farm, but this is very difficult in reality. That's why we developed a control system for smart fish farms. This system includes a gateway, sensor gatherers, and a PC program using LabVIEW. One sensor gatherer can cover up to four pools. The sensor gatherers are connected to the gateway in the form of a bus. For the gateway, the ATmega2560 is used as the main processor for communication and the STM32F429 is used as a sub-processor for displaying LCD. For the sensor gatherer, ATmega2560 is used as the main processor for communication. MQTT (Message Queuing Telemetry Transport), RS-485, and Zigbee are used as the communication protocols in the control system. The users can control the temperature and the dissolved oxygen using the PC program. The commands are transferred from the PC program to the gateway through the MQTT protocol. When the gateway gets the commands, it transfers the commands to the appropriate sensor gatherer through RS-485 and Zigbee.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.185-199
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• 2019

Korea's facility horticulture has developed remarkably in a short period of time. However, in order to secure international competitiveness in response to unfavorable surrounding conditions such as high operating costs and market opening, it is necessary to diagnose the problems of facility horticulture and prepare countermeasures through analysis. The purpose of this study was to analyze the case of leading farmers by introducing information and communication technology (ICT) in hydroponic cultivation agriculture and horticulture, and to examine how agricultural technology utilizing smart farm and big data of facility horticulture contribute to farm productivity. Crop growth information gathering and analysis solutions were developed to analyze the productivity change factors calculated from hydroponics tomato farms and strawberry farms. The results of this study are as follows. The application range of the leaf temperature was verified to be variously utilized such as house ventilation in the facility, opening and closing of the insulation curtain, and determination of the initial watering point and the ending time point. Second, it is necessary to utilize water content information of crop growth. It was confirmed that the crop growth rate information can confirm whether the present state of crops is nutrition or reproduction, and can control the water content artificially according to photosynthesis ability. Third, utilize EC and pH information of crops. Depending on the crop, EC values should be different according to climatic conditions. It was confirmed that the current state of the crops can be confirmed by comparing EC and pH, which are measured from the supplied EC, pH and draining. Based on the results of this study, it can be confirmed that the productivity of smart farm can be affected by how to use the information of measurement growth.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.130-137
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• 2023

Smart farms, which have been receiving attention as a solution to recent rural problems, refer to technologies that optimize the growing environment of crops and increase the productivity and quality of crops through efficient management. If the relationships between environmental data in smart farms are analyzed, additional productivity enhancement and crop management will be possible. In this paper, we propose a method for acquiring and analyzing nine environmental data, including temperature, humidity, CO2, soil temperature, soil moisture, insolation, soil EC, EC, and pH. Data acquisition is done through RS-485 communication between the main board and the sensor board and stored in the database after acquisition. The stored data is downloaded in Excel sheet format and analyzed through histograms, data charts, and correlation heatmaps. First, we analyze the distribution of total, day, and night data through histogram analysis, and identifiy the average, median, minimum, and maximum values by month through data chart analysis separating day and night to see how the data changes by month. Finally, we analyze the correlation of the data through a correlation heatmap analysis separating day and night. The results show a very strong positive correlation between temperature and soil temperature and soil EC and EC during the day, and a very strong positive correlation between temperature and soil temperature and soil EC and EC at night, and a strong negative correlation between temperature and soil EC.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.959-966
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• 2020

With the growing importance of the fishery industry and the rapid growth of the aquaculture industry, research on smart farms through ICT convergence in the aquaculture field is in progress. To enable monitoring of the growing environment at the farm site, an underwater drone drive unit, an image collection device, an integrated controller for posture stabilization, and a remote control device capable of controlling and controlling drones through real-time underwater images were proposed, and design, development, and tests were conducted. By utilizing underwater drones, it is possible to replace the supply and demand of manpower and high-cost work in the aquaculture industry, and to manage fish farms in a stable manner by reducing the probability of farming deaths.

• Korean Journal of Organic Agriculture
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• v.28 no.3
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• pp.315-334
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• 2020

This study was conducted to analyze factors affecting acceptance of smart farm technology. Smart farm technology is rapidly being introduced to agriculture in accordance with the progress of the 4th Industrial Revolution, but research on this is still little. Therefore, in this study, based on the unified theory of acceptance and use of technology (UTAUT), a research model reflecting the characteristics of smart farm technology was constructed. To test this, empirical analysis was performed. A survey was conducted for students in smart farm technology education and adult male and female farmers who are currently planning to operate smart farms. Valid 204 sample were used for analysis. The hypothesis test was based on multiple regression analysis using SPSS 24 statistical package. For the mediating effect and moderating effect, Process Macro 3.4 based on the regression equation was used. The results of testing the hypothesis are as follows. First, in the causal hypothesis test, it was shown that performance expectancy, social influence and price value have a significant positive effect on the intention to use smart farm technology. On the other hand, effort expectancy, facilitating conditions were not tested for a significant influence on the use of smart farm technology. As a result of analyzing the mediating effect of trust, it was found that trust plays a mediating role between performance expectancy, effort expectancy, social influence, facilitating conditions, price value and intention to use smart farm technology. In particular, the effort expectancy has not been tested for a direct significant effect on intention to use smart farm technology, but it has been shown to have an impact through trust. Trust was found to be a full mediating between the effort expectancy and the intention to use the smart farm technology. The current IT level of prospective users has been shown to play a moderating role between performance expectancy, facilitating conditions and intention to use smart farm technology. In particular, the IT level was found to strengthen the relationship between performance expectancy and intention to use smart farm technology. Based on the results of these studies, academic and practical implications were suggested.