• Journal of IKEEE
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• v.23 no.1
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• pp.322-325
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• 2019

The non-intrusive load monitoring(NILM) algorithm can infer the power usage of the individual electric devices by the total electric power consumption of the main line. To develop such an algorithm, power usage pattern data of individual devices as well as those of various combinations of these devices are required. In this paper, we propose a method to develop a power usage pattern data acquisition system for developing a NILM algorithm for a smart farm. The data acquisition system is capable of simultaneously measuring the power usage of individual electrical devices and the power usage according to various combinations of scenarios every second. The measured data can be remotely monitored from the outside of the smart farm through the LTE network, and the measured data is stored in an external server.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.373-376
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• 2018

Traditional paprika smart farm systems are often harmful to paprika growth because they are set to follow the values of several sensors to the reference value, so the system is often unable to make optimal judgement. Using decision tree techniques, the expert system for the paprika smart farm is designed to create a control system with a decision-making structure similar to that of farmers using data generated by factors that depend on their surroundings. With the current smart farm control system, it is essential for farmers to intervene in the surrounding environment because it is designed to follow sensor values to the reference values set by the farmer. To solve this problem even slightly, it is going to obtain environmental data and design controllers that apply decision tree method. The expert system is established for complex control by selecting the most influential environmental factors before controlling the paprika smart farm equipment, including criteria for selecting decisions by farmers. The study predicts that each environmental element will be a standard when creating smart farms for professionals because of the interrelationships of data, and more surrounding environmental factors affecting growth.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.127-135
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• 2023

This study examined the impact of changes in agricultural production methods on society, the economy, and the environment. While traditional open-field farming relied heavily on natural conditions, modern approaches, including greenhouse and smart farming, have emerged to mitigate the effects of climate and seasonal variations. Facility horticulture has been on the rise since the 1990s, and recently, there has been a growing interest in smart farms due to reasons such as climate change adaptation and food security. We compared open-field spinach and greenhouse spinach using agricultural income survey data, and we also compared greenhouse tomato cultivation with smart farming tomato cultivation, utilizing data from the smart farm survey reports. The economic results showed that greenhouse spinach increased yield by 25.8% but experienced a 29% decrease in income due to equipment depreciation. In the case of tomato production in smart farms, both yield and income increased by 36-39% and 34-46%, respectively. In terms of environmental impact, we also compared fertilizer and energy usage. It was found that greenhouse spinach used 29% less fertilizer but 14% more energy compared to open-field spinach. Smart farming for tomatoes saw a negligible decrease in electricity and fuel costs. Regarding the social impact, greenhouse spinach reduced labor hours by 31%, and the introduction of smart farming for tomatoes led to an average 11% reduction in labor hours. This reduction is expected to have a positive effect on sustainable farming. In conclusion, the transition from open-field to greenhouse cultivation and from greenhouse cultivation to smart farming appears to yield positive effects on the economy, environment, and society. Particularly, the reduction in labor hours is beneficial and could potentially contribute to an increase in rural populations.

• Journal of Practical Agriculture & Fisheries Research
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• v.25 no.4
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• pp.67-77
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• 2024

This study analyzed the profitability and diagnosed business performance of fruit and vegetable (cherry tomatoes, tomatoes, strawberries, cucumbers) businesses targeting young farmers participating in the youth business incubation center of A Smart Farm Innovation Valley. The purpose of this is to provide basic data for decision-making by prospective young entrepreneurs. As a result of the analysis, Smart Farm Innovation Valley had the advantage of having a fixed rental fee. As a result, it was analyzed that various costs such as depreciation of large farm equipment, depreciation of farming facilities, repair and maintenance costs, land rent, floating capital service cost, fixed capital service cost, and land capital service cost are being reduced. However, excessive input of labor, water, electricity, other materials, and fertilizer costs was being made. Guidance to reduce these costs is expected to make a significant contribution to expanding the influx of young farmers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2348-2354
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• 2016

In recent days, IoT (Internet of Things) technology has been widely used in the field of agriculture, which enables the collection of environmental data and biometric data into the database. The availability of big data on agriculture results in the increase of the machine learning based analysis. Through the analysis, it is possible to forecast agricultural production and the diseases of livestock, thus helping the efficient decision making in the management of smart farm. Herein, we use the environmental and biometric data of Smart Pig farm to derive the accurate relationship model between the environmental information and the daily weight increase of swine and verify the accuracy of the derived model. To this end, we applied the M5P tree algorithm of machine learning which reveals that the wind speed is the major factor which affects the daily weight increase of swine.

• Journal of Platform Technology
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• v.9 no.4
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• pp.42-50
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• 2021

Recently, the need to introduce smart farms is increasing in order to solve the problems of intensifying competition such as a decrease in rural population due to aging, a decrease in production, and the inflow of foreign agricultural products, and accordingly, the need for education is increasing. This paper is a study on the implementation of an Android-based IoT smart farm for education so that it can be used in a real environment by reducing the farm's smart farm system. To confirm that Android-based education can be applied in a real environment using the IoT smart farm for education, experiments were performed in automatic mode and manual mode using Bluetooth, Wi-Fi, and server/client communication methods. In the automatic mode, the current status can be checked in real time by receiving all data, and in the manual mode, commands are transmitted in real time using the received sensor data and remote control is performed. As a result of the experiment, it was possible to understand the characteristics of each communication method, and it was confirmed that remote monitoring and remote control of the smart farm using the Android App was possible.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.171-177
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• 2020

Under the influence of the Fourth Industrial Revolution, there are many tries to promote productivity enhancement and competitiveness by adapting smart farm technology that converges ICT technologies in agriculture. This smart farming technology is emerging as a new paradigm for future growth in agriculture. The development of real-time cultivation environment monitoring and automatic control system is needed to implement smart farm. Furthermore, the development of intelligent system that manages cultivation environment using monitoring data of the growth of crops is required. In this paper, a fast and efficient development method for implementing a cloud-based smart farm management system using a highly compatible and scalable web platform is proposed. It was verified that the proposed method using the web platform is effective and stable system implementation through the operation of the actual implementation system.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.49-57
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• 2022

Currently, the problem of food shortage is emerging in our society due to climate problems and an increase population in the world. As a solution to this problem, we propose a multi-remote control smart farm that combines artificial intelligence (AI) and information and communication technology (ICT) technologies. The proposed smart farm integrates ICT technology to remotely control and manage crops without restrictions in space and time, and to multi-control the growing environment of crops. In addition, using Arduino and deep-learning technology, a smart farm capable of multiple control through a smart-phone application (APP) was proposed, and Ai technology with various data securing and diagnosis functions while observing crop growth in real-time was included. Various sensors in the smart farm are controlled by using the Arduino, and the data values of the sensors are stored in the built database, so that the user can check the stored data with the APP. For multiple control for multiple crops, each LED, COOLING FAN, and WATER PUMP for two or more growing environments were applied so that the user could control it conveniently. And by implementing an APP that diagnoses the growth stage through the Tensor-Flow framework using deep-learning technology, we developed an application that helps users to easily diagnose the growth status of the current crop.

• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.