• 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.

• Journal of Advanced Technology Convergence
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• v.2 no.1
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• pp.1-7
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• 2023

Recently, there have been increasing interests in researches to apply wireless communication technologies for smart farm. This paper introduces the problems in the smart farm for the outdoor culture and technical considerations to solve the problems. As candidate technologies, this paper selects LoRa, Sigfox, NB-IoT, and Wi-Fi and then determines that LoRa is a suitable technology based on the CAPEX, coverage, transmission rate, battery, and the price. To provide technical analysis, this paper introduces technologies related to the physical and medium access control (MAC) layers as well as the security. Specifically, this paper includes the modulation technology in the physical layer, Class (Class A, B, and C) based protocol operations in MAC layer, and security architecture based on the LoRa version.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.317-323
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• 2022

The purpose of this study was to analyze how innovative facility investment and innovative research manpower capabilities of venture companies related to the 4th industrial smart farm affect the technological performance of patents and design registrations, and the financial performance of sales and operating profit. As a research method, a total of 47 venture companies were selected as a sample and regression analysis was performed. Research Results This study analyzes the technological commercialization factors of venture companies related to the 4th industrial smart farm and proposes to expand the budget for R&D government tasks for financial and technological success. In the future research direction, I believe that more discussion is needed on the contribution of companies to quantitative and qualitative growth.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.