• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.49-54
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• 2016

The IoT has attracted attention as one of the technologies that are applied to various industries and create new services. The IoT can utilize existing network technologies to create services by providing Internet connection between objects. Objects Personalized services can be created by collecting various data using the IoT. In the field of agriculture, we are promoting sustainable agriculture and enhancing competitiveness through the use of the IoT, and the convergence of IoT in agriculture is pushing for smart agriculture. In Korea, the Ministry of Agriculture, Food and Rural Affairs is preparing measures to spread smart farms to improve agricultural competitiveness using IoT technology. Therefore, we propose the development model of smart agriculture in the future through the case study on the IoT based on agriculture.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.256-264
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• 2023

Smart farm technology using IoT is one of the technologies that can increase productivity and improve the quality of agricultural products in agriculture, which is facing difficulties due to the decline in rural population, lack of rural manpower due to aging, and increase in diseases and pests due to climate change. . Smart farms using existing IoT simply monitor farms, implement smart plant growers, and have automatic greenhouse opening and closing systems. This paper implements a smart farm system based on MQTT, an industry standard protocol for the Internet of Things, and Node-RED, a representative development middleware for the Internet of Things. First, data is extracted from Arduino sensors, and data is collected and transmitted from IoT devices using the MQTT protocol. Then, Node-RED is used to process MQTT messages and store the sensing data in real time in MongoDB, a representative NoSQL, to store the data. Through this smart farm system, farm managers can use a computer or mobile phone to check sensing information on the smart farm in real time, anytime, anywhere, without restrictions on time and space.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1254-1262
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• 2021

Recently, the agricultural environment in Korea is rapidly changing due to the aging and decline of the agricultural population, and in order to solve these problems, it is urgently required to improve the agricultural productivity and reduce the labor force. To solve this problem, a smart farm fused with ICT technology is being proposed as an alternative. In Korea, smart farms are currently mainly used in greenhouses. In this paper, this smart farm technology is to be applied to the cultivation of sprouted ginseng. To this end, we use seedlings (about 1.0g) to grow a solid medium and bed for cultivating sprouted ginseng, a fresh ginseng that is produced in a short period of time (2~3 months) with a clean environment management technology that does not use chemical pesticides and hydroponics in a greenhouse developed. In addition, an IoT-based growth environment management system was developed to monitor the growth process of sprouted ginseng in such an environment and to control driving devices.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.71-72
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• 2020

본 논문에서는 시간과 공간의 제약없이 작물의 생육환경을 관측하고 자동 및 원격으로 제어할 수 있는 스마트팜 플랫폼을 설계 및 구현하였다. 스마트팜 플랫폼은 환경 데이터 수집을 위한 다양한 아두이노 센서 모듈, 웹과 데이터베이스 서버, 애플리케이션을 이용한 자동 및 원격 제어, 총 3가지 기술로 구성된다. 사용자가 앱을 통하여 언제 어디에서나 농장 주변의 환경 정보를 조회하고 원격으로 제어하면 농사에 대한 노동력 절감 뿐만 아니라 시간적·공간적 구속으로부터 자유로워져 여유시간도 늘고 삶의 질도 개선될 수 있을 것으로 기대된다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1427-1435
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• 2017

The smart farm is a remarkable system because it utilizes information and communication technologies in agriculture to bring high productivity and excellent qualities of crops. It automatically measures the growth environment of the crops and accumulates huge amounts of environmental information in real time growing in smart farms using multi-variable control of environmental factors. The statistical model using the collected big data will be helpful for decision making in order to control optimal growth environment of crops in smart farms. Using data collected from a smart farm of tomato, we carried out multiple regression analysis to determine the relationship between yield and environmental factors and to predict yield of tomato. In this study, appropriate parameter modification was made for environmental factors considering tomato growth. Using these new factors, we fit the model and derived the optimal environmental factors that affect the yields of tomato. Based on this, we could predict the yields of tomato. It is expected that growth environment can be controlled to improve tomato productivities by using statistical model.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.61-67
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• 2024

In order to monitor the growth environment of new varieties of crops, it is necessary to build the agricultural production infrastructure and strengthen the agricultural resource management system using popular smart sensor tag technology. In addition, the infrastructure for improving high-quality new varieties of crops using IoT technology and the monitoring system must be strengthened. In other words, widespread smart sensor (RFID UHF Sensor Tag) technology for environmental monitoring required for improving new crop varieties is desperately needed in the smart farm environment. Therefore, in this paper, we implemented an integrated sensor that can implement smart tag functions based on heterogeneous integrated sensors. In addition, we developed a technology that can manage crops in real time through the implemented smart integrated tag and smartphone linkage. For this purpose, an integrated antenna capable of RFID and Bluetooth communication was constructed. In addition, a communication method that allows information to be collected directly from the smartphone through the Bluetooth function was used.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.366-375
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• 2022

The monitoring of electricity consumption using Internet of Things (IoT) technology is attracting attention as a technology to reduce operation costs of smart farms. In this study, we propose a method to apply a real-time electrical consumption monitoring system (the e-Gauge system) and utilization of the collected data real-time while a melon-producing smart farm is in operation. For this purpose, the electrical consumption data for the individual smart-farm facilities such as boilers, nutrient distribution systems, automatic controllers, circulation fans, boiler controllers, and other IoT-related utilities were collected during three months of melon cultivation period. By using the monitoring results, the electrical energy consumption pattern was analyzed as an example, and necessary considerations needed to optimally utilize the measurement data were suggested. This paper will be useful in lowering the technological implementation barriers for new researchers to build a electrical consumption monitoring system and reducing trial and errors in the usage of the generated data.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.53-58
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• 2024

IoT, Big-data, AI, and Cloud technologies, which are core technologies of the 4th Industrial Revolution, have recently been applied to various fields and are being used as core technologies for new growth engines. Accordingly, these core technologies are applied to the agricultural field without exception, contributing to solving the problem of labor shortage, reducing production costs, and reducing environmental burden through remote and automated production without time and space constraints. However, as these core technologies are utilized, security incidents are occurring in the agricultural field as well. Accordingly, this study divides smart farms into three stages(Basic, Middle, and High) and presents the characteristics and security threats of each stage. In particular, as the number of container-based services and research increases under cloud platforms, we would like to suggest countermeasures focusing on security threats.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.424-433
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• 2021

The aim of this study is to explores the possibility of applying new aquaponics using guppies, a tropical fish breeding as companion fish at home, different from the aquaponics system using fish species such as loach, carp, and catfish for commercial purposes. To facilitate the application of Aquaponics at home, a system was established by connecting a water tank, water plants, hydroponic pots, plant growth LEDs, and Arduino sensors using Internet of Things(IoT) technology. As a hydroponic crops, lettuce that can be easily obtained and consumed at home was selected. In order to confirm the applicability of aquaponics using tropical fish, the growth rates of hydroponic crops in the same environment were compared as a control. The growth rate of aquaponics crops using tropical fish was about 77.4% of that of hydroponic crops. This will produce the same effect as hydroponic cultivation if conditions correspond with enough fish quantity to feed plant and appropriate pH control for growth are met. It can be seen that, and in the future, it can be used to develop an Aquaphonics standard system applicable at home.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.83-95
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• 2020

Being a dense and complex task of precisely detecting the weeds in practical crop field environment, previous approaches lack in terms of speed of processing image frames with accuracy. Although much of the attention has been given to classify the plants diseases but detecting crop weed issue remained in limelight. Previous approaches report to use fast algorithms but inference time is not even closer to real time, making them impractical solutions to be used in uncontrolled conditions. Therefore, we propose a detection model for the complex rice weed detection task. Experimental results show that inference time in our approach is reduced with a significant margin in weed detection task, making it practically deployable application in real conditions. The samples are collected at two different growth stages of rice and annotated manually