• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1391-1396
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• 2011

A way to increase productivity in agriculture that is labor-centered industry is to graft IT technology. Today, many technologies in ubiquitous computing are deployed in all areas of society such as traffic control, automotive manufacturing, construction, defence, healthcare and clinical services. These IT technologies is gaining more attention as a fusion technology among traditional industries. To successfully build ubiquitous agriculture environment, it needs optimized core technology development for agriculture that includes sensor node H/W, middleware platform, routing protocol and agriculture environment application services. To achieve accurate botany growth environment management, we propose a green growth environment management system using environmental factor monitoring sensor and biological information sensors in greenhouse. By using our proposed system, it is expected to realize fusion complex agriculture technology with low cost.

• Journal of Korean Society of Forest Science
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• v.111 no.1
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• pp.165-176
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• 2022

To raise awareness of carbon reduction in climate change, the Korea Forest Service has developed and adopted a forest carbon offset program, which aims to reduce carbon levels based on forest management. However, to maintain the forest carbon offset program, challenges such as the lack of a forest monitoring system to manage and maintain the program, must be faced. In this context, we investigated the limitations of conducting forest carbon offset programs using a number of interview techniques, including in-depth interview and questionnaire survey methods. The questionnaire surveys were developed based on the results of a literature review along with a preinterview and in-depth survey of the people in charge of the forest carbon offset program. The Irving Seidman technique was adopted for the in-depth interviews. Additionally, descriptive and frequency analyses were conducted to identify the characteristics of perception. Lastly, logistic regression was used to identify the limiting factors that affect the willingness to perform forest carbon offset monitoring activity. Results showed that the project managers or people in charge of the forest carbon offset program lacked expertise in forest carbon offset programs, which negatively affected their willingness to perform monitoring activity. Additionally, the study revealed a number of limiting factors that hindered the monitoring of forest carbon offset projects. Improving understanding using the approaches presented in this study may contribute to increasing the benefits associated with the forest carbon offset program in South Korea.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.290-298
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• 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
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• v.31 no.4
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• pp.452-459
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• 2022

One of the important factors among the smart farm factors is environmental measurement. This study tried to design an environmental measurement monitoring system through Bluetooth wireless communication with LoRa using the open source programs Arduino, App Inventor, and Node Red. This system consists of Arduino, LoRa shield, temperature and humidity sensor (SHT10), and carbon dioxide sensor (K30). The environmental measurement system is configured as a system that allows the sensor to collect environmental data and transmit it to the user through wireless communication to conveniently monitor the farm environment. As libraries used in the Arduino program, LoRa.h, Sensirion.h, LiquidCrystal_I2C.h and K30_I2C.h were used. When receiving environmental data from the sensor at regular intervals, coding using average value was used for data stabilization. An Android-based app was developed using Node Red and App Inventor program as the user interface. It can be seen that the environmental data for the sensor is well collected with the screen output to the serial screen of Arduino, the screen of the smartphone, and the user interface of Node Red. Through these open source-based platforms and programs will be applied to various agricultural applications.

• Ocean and Polar Research
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• v.31 no.1
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• pp.97-110
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• 2009

Two of the most important topics of the 21st century are ensuring harmony between man and his environment and the emerging long-tail economy in which niche markets are becoming increasingly more important. Since the Industrial Revolution in 17th century, human beings have increasingly exploited the world's natural capital, such as the natural environment and its ecosystems. Now the world is facing limits to sustainable economic growth because of limits to this natural capital. Thus, most countries are beginning to adopt a new development paradigm, the so-called"Green Development Paradigm" which pursues environmental conservation in parallel with economic growth. Recently, the Korean government announced an ambitious national policy of Low Carbon & Green Growth for the next six decades. This is an important step that transforms the existing national policy into a new future-oriented one. The fisheries sector in particular has great potential for making a substantial contribution to this national policy initiative. For example, the ocean itself with its sea plants and phytoplankton has an enormous capacity for fixing carbon, and its vast areas of tidal flats have a tremendous potential for cleaning up pollutants from both the sea and the land. Furthermore, the fishing industry has great potential for the development of fuel-saving biodegradable technologies, and a long-tail economy based on digital technologies can do much to promote the production and consumption of green goods and services derived from the oceans and the fisheries. In order for this potential to be realized, the fisheries authority needs to develop a new green-growth strategy that is practical and widely supported by fishing communities and the markets, taking into account the need for greenhouse gas reduction, conservation of the ocean environment and ecosystems, an improved system for seafood safety, the establishment of strengthened MCS (monitoring control surveillance) system, and the development of coastal ecotourism. In addition, fisheries green policies need to be implemented through a well-organized system of government aids, regulations and compensation, and spontaneous (voluntary) orders in fishing communities should be promoted to encourage far more responsible fisheries.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.378-388
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• 2016

International societies are currently working together to achieve the Climate-Smart Agriculture (CSA) initiative which aims the triple wins: (1) sustainably increasing agricultural productivity and incomes; (2) adapting and building resilience to climate change; and (3) mitigating greenhouse gases emissions. In terms of its scope and context, CSA follows the '3Nong (三農)' vision cast about 200 years ago by Dasan Jeong Yak-Yong who emphasized the triad of governance, management and monitoring towards comfortable, profitable and noble agriculture. Yet, the CSA provides the practical aims that facilitate the development of holistic indicators for quantitative evaluation and monitoring, on which decision-making support system is based. In this study, we introduce an agent-based model, i.e. Mathematical Programming Multi-Agent Systems (MP-MAS), as a tool for supporting the decision-making toward CSA. We have established the initial version of MP-MAS adapted for domestic use and present the preliminary results from an application to the rice farming case in Haenam, Korea. MP-MAS can support both farmers and policy-makers to consider diverse management options from multiple perspectives. When the modules for system resilience and carbon footprint are added, MP-MAS will serve as a robust tool that fulfills not only CSA but also Dasan's '3Nong' vision of sustainable agricultural-societal systems.

• Journal of Environmental Impact Assessment
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• v.28 no.6
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• pp.592-603
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• 2019

This study is about the implementation of low energy sewage management technology through effective control of blower which consumes the most energy in sewage treatment. In calculating the amount of oxygen required for microorganisms, unlike the existing method using the operating index in the bioreactor or TMS data in the discharge port, the CODcr and NH₄⁺-N concentration changes in sewage flowing into the sewage treatment plant were detected in advance before entering the bioreactor and the amount of air was controlled based on this. The pre-sensing was found to have a high correlation compared with conventional products. As a result of blower control, it was possible to save about 9.9% energy more than the manual control. Consequently, this study suggested the possibility of blower's real-time control combined with pre-sensing technology. Also, it is expected that the low energy sewage treatment can be applied to sewage treatment facilities dependent on operation by manpower, and it will contribute to the reduction of greenhouse gas emissions.

• Journal of Animal Science and Technology
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• v.60 no.11
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• pp.27.1-27.8
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• 2018

Background: Enteric methane ($CH_4$) accounts for about 70% of total $CH_4$ emissions from the ruminant animals. Researchers are exploring ways to mitigate enteric $CH_4$ emissions from ruminants. Recently, nano zinc oxide (nZnO) has shown potential in reducing $CH_4$ and hydrogen sulfide ($H_2S$) production from the liquid manure under anaerobic storage conditions. Four different levels of nZnO and two types of feed were mixed with rumen fluid to investigate the efficacy of nZnO in mitigating gaseous production. Methods: All experiments with four replicates were conducted in batches in 250 mL glass bottles paired with the ANKOM$^{RF}$ wireless gas production monitoring system. Gas production was monitored continuously for 72 h at a constant temperature of $39{\pm}1^{\circ}C$ in a water bath. Headspace gas samples were collected using gas-tight syringes from the Tedlar bags connected to the glass bottles and analyzed for greenhouse gases ($CH_4$ and carbon dioxide-$CO_2$) and $H_2S$ concentrations. $CH_4$ and $CO_2$ gas concentrations were analyzed using an SRI-8610 Gas Chromatograph and $H_2S$ concentrations were measured using a Jerome 631X meter. At the same time, substrate (i.e. mixed rumen fluid+ NP treatment+ feed composite) samples were collected from the glass bottles at the beginning and at the end of an experiment for bacterial counts, and volatile fatty acids (VFAs) analysis. Results: Compared to the control treatment the $H_2S$ and GHGs concentration reduction after 72 h of the tested nZnO levels varied between 4.89 to 53.65%. Additionally, 0.47 to 22.21% microbial population reduction was observed from the applied nZnO treatments. Application of nZnO at a rate of $1000{\mu}g\;g^{-1}$ have exhibited the highest amount of concentration reductions for all three gases and microbial population. Conclusion: Results suggest that both 500 and $1000{\mu}g\;g^{-1}$ nZnO application levels have the potential to reduce GHG and $H_2S$ concentrations.

• 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 Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 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.