• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.235-250
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• 2021

The overarching question of this study is how a typical rice cultivation system in Gimje, Korea was keeping up with the triple-win challenge of climate-smart agriculture (CSA). To answer this question, we have employed (1) quantitative data from direct measurement of energy, water, carbon and information flows in and out of a rice cultivation system and (2) appropriate metrics to assess production, efficiency, GHG fluxes, and resilience. The study site was one of the Korean Network of Flux measurement (KoFlux) sites (i.e., GRK) located at Gimje, Korea, managed by National Academy of Agricultural Science, Rural Development Administration. Fluxes of energy, water, carbon dioxide (CO₂) and methane (CH₄) were directly measured using eddy-covariance technique during the growing seasons of 2011, 2012 and 2014. The production indicators include gross primary productivity (GPP), grain yield, light use efficiency (LUE), water use efficiency (WUE), and carbon uptake efficiency (CUE). The GHG mitigation was assessed with indicators such as fluxes of carbon dioxide (F_CO2), methane (F_CH4), and nitrous oxide (F_N2O). Resilience was assessed in terms of self-organization (S), using information-theoretic approach. Overall, the results demonstrated that the rice cultivation system at GRK was climate-smart in 2011 in a relative sense but failed to maintain in the following years. Resilience was high and changed little for three year. However, the apparent competing goals or trade-offs between productivity and GHG mitigation were found within individual years as well as between the years, causing difficulties in achieving the triple-win scenario. The pursuit of CSA requires for stakeholders to prioritize their goals (i.e., governance) and to practice opportune interventions (i.e., management) based on the feedback from real-time assessment of the CSA indicators (i.e., monitoring) - i.e., a purpose-driven visioneering.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.27 no.3
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• pp.187-192
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• 2021

This study was conducted to compare the quality of baby leaves grown under several temperature conditions and the storage properties of MA storage for romaine lettuce. It was grown for 5 weeks under an artificial light source (200 µmol·m^-2·s^-1) in a chamber at 21℃, 28℃, and 35℃. The growth and quality of red romaine lettuce that grown in different temperatures were investigated at the end of cultivation, and the oxygen, carbon dioxide, and ethylene concentrations in the 20,000 cc OTR film and perforated film packed with lettuces were measured for 36 and 12 days, respectively. The red romaine lettuce baby leaf was examined for color, chlorophyll, and visual quality at the end of storage. The maximum quantum yield of baby leaf grown in different temperatures at 7days before the harvest was higher at 21℃ and 28℃ growth temperature treatments. On harvest day, the leaf length measured was longest at 28℃, and the leaf width was wider at 21℃ and 28℃, and the number of leaves was similar to 5-6 at all cultivation temperatures. Leaf weight, root weight, and dry weight were found to be higher at 21℃, and tended to decrease as the cultivation temperature increased. The concentration of ethylene in the film of the MA storage treatments was maintained at 1~2 µL·L^-1 until the end of storage in all treatments regardless of the cultivation temperature. Oxygen concentration in the MA treatment used 20,000 OTR film was maintained at around 19.5%, and carbon dioxide concentration around 1% that was satisfied the CA conditions. Both Hunter a^* and b^* values were generally higher in the MA storage treatment at the end of storage day. The chlorophyll content was decreased as the cultivation temperature increased, and was lower in the MA storage treatment than in the perforated film treatment. Visual quality was 3 points or higher in the MA storage treatment at 21℃ growth treatment, and it was maintained marketability. As the above results, the growth of baby leaves of romaine lettuce was the best at 21℃ treatment, and the lower the cultivation temperature, the longer the shelf life. And it was possible to extend the shelf life by 3 times by showing excellent visual quality at the MA storage treatment that satisfies the carbon dioxide concentration of CA condition until the end of storage day.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.203-215
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• 2022

The study is to investigate research trends and knowledge structures in the Smart Farm field. To achieve the research purpose, keywords and the relationship among keywords were analyzed targeting 104 Korean academic journals related to the Smart Farm in KCI(Korea Citation Index), and topics were analyzed using the LDA Topic Modeling technique. As a result of the analysis, the main keywords in the Korean Smart Farm-related research field were 'environment', 'system', 'use', 'technology', 'cultivation', etc. The results of Degree, Betweenness, and Eigenvector Centrality were presented. There were 7 topics, such as 'Introduction analysis of Smart Farm', 'Eco-friendly Smart Farm and economic efficiency of Smart Farm', 'Smart Farm platform design', 'Smart Farm production optimization', 'Smart Farm ecosystem', 'Smart Farm system implementation', and 'Government policy for Smart Farm' in the results of Topic Modeling. This study will be expected to serve as basic data for policy development necessary to advance Korean Smart Farm research in the future by examining research trends related to Korean Smart Farm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.794-797
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• 2016

This paper proposed the smart graden veranda system using the Arduino which is an open hardware controllers and sensors. It is designed automatically controls the watering and lighting. This system is not already massive cultivation systems, such as the existing plant factory that is a lot of leverage Considering that used in the interior, such as apartments and functionality were of course perform the design considering the aesthetic, recently spotlighted receive Internet of Things technology for this and it was carried out with the development of automated equipment using a smartphone app.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.8-13
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• 2019

Smart Farming has been regarded as an important application in information and communications technology (ICT) fields. Selecting crops for cultivation at the pre-production stage is critical for agricultural producers' final profits because over-production and under-production may result in uncountable losses, and it is necessary to predict crop production to prevent these losses. The ITU-T Recommendation for Smart Farming (Y.4450/Y.2238) defines plan/production consultation service at the pre-production stage; this type of service must trace crop production in a predictive way. Several research papers present that machine learning technology can be applied to predict crop production after related data are learned, but these technologies have little to do with standardized ICT services. This paper clarifies the relationship between agricultural consultation services and predicting crop production. A prediction scheme is proposed, and the results confirm the usability and superiority of machine learning for predicting crop production.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.655-656
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• 2022

4차산업혁명 등의 스마트 산업 발전으로 농업분야에도 점점 전문화 및 분업화된 ICT기반 기술이 수용되고 있으며, 이에 기반한 삶의 질 향상은 계절과 무관하게 언제나 신선한 과일이나 채소를 제공받고 싶어하는 소비자들의 요구를 증대시키고 있다. 본 논문에서는 신선한 과일이나 채소를 사계절 구분없이 가정에 제공할 수 있도록 단기 육묘가 가능하고, 육묘종에 따라 생육환경을 맞춤형으로 관리하여 별도의 시설 및 인적 자원을 최소화할 수 있는 스마트 육묘재배 시스템을 제안한다.

• 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.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.27-37
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• 2021

Conversion of rice paddy field to upland has been accelerated as the central government incentivizes more profitable upland crop cultivation. The objective of this study was to investigate the current status and conversion trend from paddy to upland for the reservoir irrigation districts. Total 605 of reservoir irrigation districts whose beneficiary area is greater than 200 ha were selected for paddy-to-upland conversion analysis using the land cover maps provided by the EGIS of the Ministry of Environment. The land cover data of 2019 was used to analyze up-to-date upland conversion status and its correlation with city proximity, while land cover change between 2007 and 2019 was used for paddy-to-upland conversion trend analysis. Overall 14.8% of the entire study reservoir irrigation area was converted to upland cultivation including greenhouse and orchard areas. Approximately the portion of paddy area was reduced by 17.8% on average, while upland area was increased by 4.9% over the 12 years from 2007 to 2019. This conversion from paddy to upland cultivation was more pronounced in the Gyoenggi and Gyeongsang regions compared to other the Jeolla and Chungcheong provinces. The increase of upland area was also more notable in proximity of the major city. This study findings may assist to identify some hot reservoir districts of the rapid conversion to upland cultivation and thus plan to transition toward upland irrigation system.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.703-718
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• 2021

An irrigation monitoring system is an efficient approach to save water and to provide effective irrigation scheduling for rice cultivation in desert soils. This research aimed to design, fabricate, and evaluate the basic performance of an irrigation monitoring system based on information and communication technology (ICT) for rice cultivation under drip and micro-sprinkler irrigation in desert soils using a Raspberry Pi. A data acquisition system was installed and tested inside a rice cultivating net house at the United Arab Emirates University, Al-Foah, Al-Ain. The Raspberry Pi operating system was used to control the irrigation and to monitor the soil water content, ambient temperature, humidity, and light intensity inside the net house. Soil water content sensors were placed in the desert soil at depths of 10, 20, 30, 40, and 50 cm. A sensor-based automatic irrigation logic circuit was used to control the actuators and to manage the crop irrigation operations depending on the soil water content requirements. A developed webserver was used to store the sensor data and update the actuator status by communicating via the Pi-embedded Wi-Fi network. The maximum and minimum average soil water contents, ambient temperatures, humidity levels, and light intensity values were monitored as 33.91 ± 2 to 26.95 ± 1%, 45 ± 3 to 24 ± 3℃, 58 ± 2 to 50 ± 4%, and 7160-90 lx, respectively, during the experimental period. The ICT-based monitoring system ensured precise irrigation scheduling and better performance to provide an adequate water supply and information about the ambient environment.