• KIPS Transactions on Computer and Communication Systems
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• v.9 no.7
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• pp.157-164
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• 2020

As the consumption of fishery products continues to increase, aquaculture industry has emerged instead of fishing industry facing limitations of fish stock resources. Recently, smart fish farming industry has rapidly developed through convergence with 4th Industrial Revolution technology. Accordingly, it is important to derive a future model of smart fish farming platforms in order to secure the superiority of the aquaculture industry and the technology standard hegemony. In this study, the future direction of smart fish farm platform was derived through the analysis of environment related to politics, economy, society, and technology related to smart fish farming by applying PEST methodology of macro-environment analysis. It is expected that it will help the public and industrial circles in planning and implementing related projects by including the entire process of value chain of aquaculture industry of breeding, production, management and distribution, and by presenting advanced models based on artificial intelligence and digital twin.

• Korean Journal of Organic Agriculture
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• v.32 no.2
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• pp.203-231
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• 2024

Smart farm farmers invest a lot of facility costs, time and effort. Product differentiation using storytelling marketing is important for the profitability of smart farms, since it is not easy to differentiate products between fruits and vegetables grown in smart farms and conventional facilities. This study empirically analyzes the effect of storytelling marketing on consumers' value perception and willingness to pay for fruit and vegetables grown in smart farms. For this purpose, the survey is conducted on 1,050 consumers and the main results are as follows. First, as a result of evaluating consumers' value perception, consumers perceive the value of comparative products (product 2 and 3) to be higher than the base product (product 1). Product 2 and 3 provide richer stories than product 1. Second, the willingness to pay for product 3, which provides the richest story, was the highest, followed by product 2, and then product 1. This means storytelling marketing could be an effective strategy that increases the value of fruits and vegetables grown in smart farms. Third, more than half of the respondents are willing to use QR codes when purchasing fresh agricultural products. Farmers could use QR codes to provide rich stories for effective storytelling marketing.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.780-790
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• 2015

Studies for a plants factory is progressing for cultivating various plants by the needs of the times and industry around world. However most studies is carried out only in lab sized plants factory. It does not consider an economic feasibility. The study for a large scale plants factory is very required to get an economic gain. In this paper we has been studying a smart farm factory based on ICT using the hydroponics ginseng. The smart farm factory is to extend a concept of the general plants factory to full automated factory. The factory can collect the information about growing of plants and automate operating and management of factory like the existing plants factory. Also it is the total plants factory management system, which analyzes the collected information for optimized growth and development of plants and applies the result to the system back.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.496-499
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• 2016

The smartfarm is a leader in the Field of environmental monitoring in agriculture. By the use of wireless remote systems, monitoring applications of the smartfarm are able to provide vital information to the farmer wherever he may be. Absentee farmers are finding the ease of viewing the application graphs on their mobile phone is providing them with peace of mind. We design system and technical requirements of service for managing and operating smart-farm based on cloud technology. It describes requirements of cloud technology for monitoring, controlling, managing, and operating cloud-based smart farm. Smart farm system and service with cloud platform contains 3 interfaces and 3 services. In addition, smart-farm using cloud platform could have several cases so it should be established and managed in varying way depending on cultivars, its size and type. This paper will focus the industry's attention on the importance of Open/Standard Cloud platform thereby stimulating the smartfarm in agriculture.

• Korean Journal of Ecology and Environment
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• v.52 no.2
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• pp.171-177
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• 2019

Smart farm is a breakthrough technology that can maximize crop productivity and economy through efficient utilization of space regardless of external environmental factors. This study was conducted to investigate the optimal growth and physiological conditions of Chinese matrimony vine (Lycium chinense) with LED light sources in a smart farm. The light source was composed of red+blue and red+blue+white mixed light using a LED system. In the red+blue mixed light, red and blue colored LEDs were mixed at ratios of 1:1, 2:1, 5:1, and 10:1, with duty ratios varied to 100%, 99%, and 97%. The experimental results showed that the photosynthetic rate according to the types of light sources did not show statistically significant differences. Meanwhile, the photosynthetic rate according to the mixed ratio of the red and the blue light was highest with the red light and blue LED ratio of 1:1 while the water use efficiency was highest with the red and blue LED ratio of 2:1. The photosynthetic rate according to duty ratio was highest with the duty ratio of 99% under the mixed light condition of red+blue+white whereas the water use efficiency was highest with the duty ratio of 97% under the mixed light of red+blue LED. The results indicate that the light source and light quality for the optimal growth of Lycium chinense in the smart farm using the LED system are the mixed light of red+blue (1:1) and the duty ratio of 97%.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.75-84
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• 2021

As the weather changes become frequent, weather disasters are increasing, causing more damage to plastic greenhouses. Among the damage caused by various disasters, damage by snow to the greenhouse takes a relatively long time, so if an alarm system is properly prepared, the damage can be reduced. Existing greenhouse design standards and snow warning systems are based on snow depth. However, even in the same depth, the load on the greenhouse varies depending on meteorological characteristics and snow density. Therefore, this study aims to secure the structural safety of greenhouses by developing sensors that can directly measure snow loads, and analysing the warning criteria for load using a stochastic model. Markov chain was applied to estimate the failure probability of various types of greenhouses in various regions, which let users actively cope with heavy snowfall by selecting an appropriate time to respond. Although it was hard to predict the precise snow depth or amounts, it could successfully assess the risk of structures by directly detecting the snow load using the developed sensor.

• Journal of Ecology and Environment
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• v.46 no.3
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• pp.161-171
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• 2022

Background: Epilobium hirsutum L. is designated as an endangered plant in South Korea located in Asia, due to the destruction of its habitats through the development of wetlands. Therefore, in this study, in order to find a light condition suitable for the growth and ecophysiological responses of Epilobium hirsutum L., those of this plant under treatment with various light qualities in a smart farm were measured. Results: In order to examine the changes in the physiological and growth responses of Epilobium hirsutum L. according to the light qualities, the treatment with light qualities of the smart farm was carried out using the red light: blue light irradiation time ratios of 1:1, 1:1/2, and 1:1/5 and a red light: blue light: white light irradiation time ratio of 1:1:1. As a result, the ecophysiological responses (difference between leaf temperature and atmospheric temperature, transpiration rate, net photosynthetic rate, intercellular CO₂ partial pressure, photosynthetic quantum efficiency) to light qualities appeared differently according to the treatments with light qualities. The increase in the blue light ratio increased the difference between the leaf temperature and the atmospheric temperature and the photosynthetic quantum efficiency and decreased the transpiration rate and the intercellular CO₂ partial pressure. On the other hand, the white light treatment increased the transpiration rate and intercellular CO₂ partial pressure and decreased the temperature difference between the leaf temperature and the ambient temperature and photosynthetic quantum efficiency. Conclusions: The light condition suitable for the propagation by the stolons, which are the propagules of Epilobium hirsutum L., in the smart farm, is red, blue and white mixed light with high net photosynthetic rates and low difference between leaf temperature and atmospheric temperature.