• Title/Summary/Keyword: indoor smart farm

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Types of Vertical Smart Farms and Awareness of their use in Korean Cities Types and Feasibility Analysis of Vertical Smart Farms in Korean Cities

  • Heo, Han Kyul;Lee, Eunseok
    • Journal of People, Plants, and Environment
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    • v.24 no.3
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    • pp.257-266
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    • 2021
  • Background and objective: Vertical smart farm (VSF) is an alternative that contributes to solving various problems such as climate change and food shortage. This study focused on the types and awareness of VSF to introduce and diffuse VSF. We aimed to investigate the types of VSF and citizens' awareness on VSF. We analyzed 1) where the smart farm technology could be implemented on a building; 2) what citizens think about VSF; and 3) suggested what is most necessary for the introduction and diffusion of VSF in the future based on citizens' perception. Methods: VSF types were investigated through case studies on VSF in Korea and overseas. Citizens' perception on VSF was investigated through a questionnaire survey. A statistical analysis was conducted with the survey results for implications of the introduction and diffusion of VSF. Results: Four types of VSF were derived: rooftop farms, facade farms, indoor farms, and farms using the whole building. The survey showed that 29.2%, 27.8%, and 22.2% of respondents knew well about urban agriculture, smart farms, and vertical smart farms, respectively. Respondents answered that improving awareness is the most important factor to introduce VSF. According to the statistical analysis, it was determined that education and promotion of the necessity of VSF would be important to diffuse the VSF. Conclusion: VSF can be a solution to a variety of problems we face. The results of this study suggest a direction for the introduction and diffusion of VSF. In order to introduce VSF in the future, additional studies must be conducted on the legal system.

Data-Based Model Approach to Predict Internal Air Temperature in a Mechanically-Ventilated Broiler House (데이터 기반 모델에 의한 강제환기식 육계사 내 기온 변화 예측)

  • Choi, Lak-yeong;Chae, Yeonghyun;Lee, Se-yeon;Park, Jinseon;Hong, Se-woon
    • Journal of The Korean Society of Agricultural Engineers
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    • v.64 no.5
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    • pp.27-39
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    • 2022
  • The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

Building a Smart Farm in the House using Artificial Intelligence and IoT Technology (인공지능과 IoT 기술을 활용한 댁내 스마트팜 구축)

  • Moon, Ji-Ye;Gwon, Ga-Eun;Kim, Ha-Young;Moon, Jae-Hyun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2020.11a
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    • pp.818-821
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    • 2020
  • The artificial intelligence software market is developing in various fields world widely. In particular, there is a wide variety of applications for image recognition technology using deep learning. This study intends to apply image recognition technology to the 'Home Gardening' market growing rapidly due to COVID-19, and aims to build a small-scale smart farm in the house using artificial intelligence and IoT technology for convenient crop cultivation for busy people living in cities. This intelligent farm system includes an automatic image recognition function and recommendation function based on temperature and humidity sensor-based indoor environment analysis.

Indoor Temperature Analysis by Point According to Facility Operation of IoT-based Vertical Smart Farm (IoT 기반 수직형 스마트 팜의 설비운영에 따른 지점별 실내온도분석)

  • Kim, Handon;Jung, Mincheol;Oh, Donggeun;Cho, Hyunsang;Choi, Seun;Jang, Hyounseung;Kim, Jimin
    • Korean Journal of Construction Engineering and Management
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    • v.23 no.1
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    • pp.98-105
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    • 2022
  • It is essential for vertical smart farms that artificially grow crops in an enclosed space to properly utilize air environment facilities to create an appropriate growth environment. However, domestic vertical smart farm companies are creating a growing environment by relying on empirical data rather than systematic methods. Using IoT to create a growing environment based on systematic and precise monitoring can increase crop production yield and maximize profitability. This study aims to construct a monitoring system using IoT and to analyze the cause by demonstrating the imbalance of temperature environment, which is a significant factor in crop cultivation. 1) The horizontal temperature distribution of the multi-layer shelf was measured with different operating methods of LED and air conditioner. As a result, there was a temperature difference of "up to 1.7℃" between the sensors. 2) As a result of measuring the vertical temperature distribution, the temperature difference was "up to 6.3℃". In order to reduce this temperature gap, a strategy for proper arrangement and operation of air conditioning equipment is required.

A Research about Time Domain Estimation Method for Greenhouse Environmental Factors based on Artificial Intelligence (인공지능 기반 온실 환경인자의 시간영역 추정)

  • Lee, JungKyu;Oh, JongWoo;Cho, YongJin;Lee, Donghoon
    • Journal of Bio-Environment Control
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    • v.29 no.3
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    • pp.277-284
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    • 2020
  • To increase the utilization of the intelligent methodology of smart farm management, estimation modeling techniques are required to assess prior examination of crops and environment changes in realtime. A mandatory environmental factor such as CO2 is challenging to establish a reliable estimation model in time domain accounted for indoor agricultural facilities where various correlated variables are highly coupled. Thus, this study was conducted to develop an artificial neural network for reducing time complexity by using environmental information distributed in adjacent areas from a time perspective as input and output variables as CO2. The environmental factors in the smart farm were continuously measured using measuring devices that integrated sensors through experiments. Modeling 1 predicted by the mean data of the experiment period and modeling 2 predicted by the day-to-day data were constructed to predict the correlation of CO2. Modeling 2 predicted by the previous day's data learning performed better than Modeling 1 predicted by the 60-day average value. Until 30 days, most of them showed a coefficient of determination between 0.70 and 0.88, and Model 2 was about 0.05 higher. However, after 30 days, the modeling coefficients of both models showed low values below 0.50. According to the modeling approach, comparing and analyzing the values of the determinants showed that data from adjacent time zones were relatively high performance at points requiring prediction rather than a fixed neural network model.

Application of Internet of Things Based Monitoring System for indoor Ganoderma Lucidum Cultivation

  • Quoc Cuong Nguyen;Hoang Tan Huynh;Tuong So Dao;HyukDong Kwon
    • International journal of advanced smart convergence
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    • v.12 no.2
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    • pp.153-158
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    • 2023
  • Most agriculture plantings are based on traditional farming and demand a lot of human work processes. In order to improve the efficiency as well as the productivity of their farms, modern agricultural technology was proven to be better than traditional practices. Internet of Things (IoT) is usually related in modern agriculture which provides the farmer with a real-time monitoring condition of their farm from anywhere and anytime. Therefore, the application of IoT with a sensor to measure and monitors the humidity and the temperature in the mushroom farm that can overcome this problem. This paper proposes an IoT based monitoring system forindoor Ganoderma lucidum cultivation at a minimal cost in terms of hardware resources and practicality. The results show that the data of temperature and humidity are changing depending on the weather and the preliminary experimental results demonstrated that all parameters of the system were optimized and successful to achieve the objective. In addition, the analysis results show that the quality of Ganoderma lucidum produced on the research method conforms to regulations in Vietnam.

Effects of Lettuce Cultivation Using Optical Fiber in Closed Plant Factory (폐쇄형 식물공장내 태양광 파이버를 이용한 상추 재배효과)

  • Lee, Sanggyu;Lee, Jaesu;Won, Jinho
    • Journal of Bio-Environment Control
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    • v.29 no.2
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    • pp.105-109
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    • 2020
  • This study was conducted to the improvement of solar light-based artificial light supply system and effect of lettuce cultivation. The artificial light supply system was consisted of units such as light source, power, system measurement and controller. The light source supply was composed of a solar transmitter and an LED lamp. The power supply consisted of an leakage breaker, SMPS, LED controller and relay. The solar transmitter was made of a quartz optical fiber with optimal light transmission. Artificial light used white lamp among LEDs. System measurement and control consisted of touch screen, Zigbee communication module and light quantity sensor. The results of test confirmed that the LED light is automatically activated when the intensity measured by the light intensity sensor is 200 μmolm-2s-1 or less. Moreover, the leaf length, root length, chlorophyll content and root fresh weight of optical fiber treatment was hight than LED lamp treatment. Therefore, it can be inferred that the energy-saving solar light collector device can be effective in the indoor lettuce production. However, the use of LED lamp is also recommended to assure the availability of sufficient sunlight in cloudy and rainy days.

Membrane-based Direct Air Capture Technologies (분리막을 이용한 공기 중 이산화탄소 제거 기술)

  • Yoo, Seung Yeon;Park, Ho Bum
    • Membrane Journal
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    • v.30 no.3
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    • pp.173-180
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    • 2020
  • As the demand for fossil fuels continues to increase worldwide, carbon dioxide (CO2) concentration in the air has increased over the centuries. The way to reduce CO2 emissions to the atmosphere, carbon capture and sequestration (CCS) technology have been developed that can be applied to power plants and factories, which are primary emission sources. According to the climate change mitigation policy, direct air capture (DAC) in air, referred to as "negative emission" technology, has a low CO2 concentration of 0.04%, so it is focused on adsorbent research, unlike conventional CCS technology. In the DAC field, chemical adsorbents using CO2 absorption, solid absorbents, amine-functionalized materials, and ion exchange resins have been studied. Since the absorbent-based technology requires a high-temperature heat treatment process according to the absorbent regeneration, the membrane-based CO2 capture system has a great potential Membrane-based system is also expected for indoor CO2 ventilation systems and immediate CO2 supply to smart farming systems. CO2 capture efficiency should be improved through efficient process design and material performance improvement.