• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.5
• /
• pp.47-53
• /
• 2023

With the advent of the 4th Industrial Revolution, diverse and a large amount of data has been accumulated now. The agricultural community has also collected environmental data that affects the growth of crops in smart farms or open fields with sensors. Environmental data has different features depending on where and when they are measured. Studies have been conducted using collected agricultural data to predict growth and yield with statistics and artificial intelligence. The results of these studies vary greatly depending on the data on which they are based. So, studies to enhance data quality have also been continuously conducted for performance improvement. A lot of data is required for high performance, but if there are outlier or missing values in the data, it can greatly affect the results even if the amount is sufficient. So, adjustment of outlier and missing values is essential in the data preprocessing. Therefore, this paper integrates data collected from actual farms and proposes a adjustment system for outlier and missing values based on it.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.433-434
• /
• 2018

Smart farm technology that combines 4th industrial technology and agriculture is expected to be a solution to agriculture in Korea which is getting worse due to aging and population decrease. The development of smart farm technology is considered to be important, and the method of designing and accessing and controlling data is described.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.3
• /
• pp.290-298
• /
• 2019

The proportion of farm households in the total population is decreasing every year. The aging of rural areas is expected to deepen. The aging of agriculture is continuing due to the aging of the aged population and the decline of the young population, and agricultural manpower shortage is emerging as a threat to agriculture and rural areas. The existing facility cultivation was concentrated on the production / yield per unit area. However, nowadays, not only production but also crop quality should be good so that the quality of crops must be improved because they can secure competitiveness in the market. Therefore, the government plans to increase the productivity by hi-techization of ICT infrastructure horticulture and to plan the dissemination of energy saving smart greenhouse. Therefore, it is necessary to develop a Smart Farm convergence service system based on a hybrid algorithm to enhance diversity and connectivity. Therefore, this study aims to develop smart farm convergence service system which collects data of growth environment of the rhizosphere environment of crops by wireless and monitor smartphone.

• Journal of Bio-Environment Control
• /
• v.31 no.1
• /
• pp.1-7
• /
• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.2B
• /
• pp.252-262
• /
• 2010

Recently, the USN (Ubiquitous Sensor Network) technology is emerging as an aspect of digital convergence trends which is being rapidly evolving in the whole society. The technological feasibility for the various application services using the USN is researched in numerous industries, but, in the agricultural field, the market of USN application service, technology adoption and commercialization have been delayed. In the agricultural field, the ubiquitous technologies could lead to huge change in the conventional surroundings such as growth environment of livestock, crop cultivation and harvest. In this paper, to offer a integrated management, we construct a u-swinery(ubiquitous swinery) system which is consisted with USN environmental sensors to collect information from physical phenomenon such as luminance, relative humidity, temperature and ammonia gas. Numbers of CCTV were also installed to monitor inside and outside of the swinery. The u-swinery integrated management system can monitor and control the condition of swinery from remote sites. Furthermore, by gathering the cumulative environmental data from the system, the optimal growth condition for the livestock could be created.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.11a
• /
• pp.1166-1168
• /
• 2017

환경 데이터는 현재까지 농업, 해양, 주거 분야에서 다양하게 연구 및 활용되고 있다. 본 논문에서는 단순히 환경 모니터링과 제어에 국한되는 것이 아닌 환경 데이터를 수집하는 저비용 센서 모듈로 각각 다른 실내 공간을 식별하는 모델을 제안하고자 한다. 이를 통해 스마트홈 및 스마트빌딩 분야에서 환경 변화에 따른 맞춤형 서비스를 제공할 수 있을 것으로 기대된다.

• Journal of Digital Convergence
• /
• v.15 no.3
• /
• pp.65-75
• /
• 2017

Agricultural sector in Korea faces the threat of aging farmers and many other difficulties. Because agriculture is a very less-competitive industry in Korea and many solutions to improve the competitiveness of Korean agriculture should be studied. The advent of Internet of things(IoT) technology makes possible many new industries and business models in the current society. The adoption of this new technology in agriculture can bring about innovations in agricultural production and distribution as $6^{th}$ industry. This paper summarizes the opportunities in IoT and smart farm. The major benefits and obstacles in introducing smart farms are reviewed and the cases of two successful smart farms in Korea are analyzed. Through these case studies, we can recognize the current status and future strategies in Korean smart farms.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.128-128
• /
• 2019

증발산량은 수표면이나 토양면에서 수증기의 형태로, 대기중으로 방출되는 증발량과 식물의 엽면을 통해 지중의 물이 대기 중으로 방출되는 증산량의 합으로, 기상학과 수문학에 사용되는 중요한 농업기상 매개 변수이다. 증발산량을 관측하는 방법으로는 라이시미터 (Lysimeter)와 같은 관측장비를 통해 실제 증발산량을 측정하는 방법과, FAO-56 Penman-Monteith (PM)과 같은 증발산량 추정 알고리즘을 이용하여 산출하는 방법이 있다. 국내의 경우 기상관측소에서 수집한 데이터를 이용하여 증발산량 추정공식을 통해 증발산량을 산정하는 연구가 이루어졌으며, 위성영상에 기반하여 증발산을 추정하려는 연구가 진행되고 있다. 본 연구에서는 미국 항공우주국 (National Aeronautics and Space Administration, NASA)에서 추진하는 위성을 이용한 지구 전역의 장기관측 계획 EOS (Earth Observing System)에 의해 발사된 지구 관측 위성인 MODIS Terra 위성에서 제공되는 MOD16A2 위성영상을 사용하였다. MOD16A2 위성영상은 2001년부터 현재까지 500m의 픽셀 단위로 제공되는 8일 간격의 전지구 규모의 위성영상으로, 본 연구에서는 우리나라 관측소에서 관측된 기상인자를 PM 공식에 입력하여 산정된 증발산량 값과 MOD16A2 위성영상 데이터를 비교하여 우리나라 MOD16A2 위성영상 적용성 및 밭작물 가뭄분석에 적용하였다.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.6 no.4
• /
• pp.595-601
• /
• 2011

This study proposed the system to automatically control the optimized raising environment for vegetation, raised in an equipment house, in which u-IT agricultural technology concept was applied. The system consists of environment sensors such as temperature, humidity, etc., biosensors such as EC, PH, etc., and requested automatic control devices, in which they were automatically controlled by the software system. The system is established in client systems, installed in each equipment house and the server system, collecting data from each client system as well. In addition, the system collects each farmer's data through the Internet and 3G network. In this phase, collected raising environment data comes to be analyzed in order to find out the optimized vegetation raising environment, finally, which is visualized and used for consulting each farmer.

• Korean Journal of Environmental Agriculture
• /
• v.42 no.4
• /
• pp.358-370
• /
• 2023

Composting has been proposed for the management of organic waste, and the resulting products can be used as soil amendments and fertilizer. However, the emissions of greenhouse gases (GHGs) such as CO₂, CH₄, and N₂O produced in composting are of considerable concern. Hence, various additives have been developed and adopted to control the emissions of GHGs. This review presents the different additives used during composting and summarizes the effects of additives on GHGs during composting. Thirty-four studies were reviewed, and their results showed that the additives can reduce cumulative CO₂, CH₄, and N₂O emission by 10.5%, 39.0%, and 28.6%, respectively, during composting. Especially, physical additives (e.g., biochar and zeolite) have a greater effect on mitigating N₂O emissions during composting than do chemical additives (e.g., phosphogypsum and dicyandiamide). In addition, superphosphate had a high CO₂ reduction effect, whereas biochar and dicyandiamide had a high N₂O reduction effect. This implies that the addition of superphosphate, biochar, and dicyandiamide during composting can contribute to mitigating GHG emissions. Further research is needed to find novel additives that can effectively reduce GHG emissions during composting.