• The Plant Pathology Journal
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• v.37 no.6
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• pp.521-532
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• 2021

Knowledge and better understanding of functions of the microbial community are pivotal for crop management. This study was conducted to study bacterial structures including Acidovorax species community structures and diversity from the watermelon cultivated soils in different regions of South Korea. In this study, soil samples were collected from watermelon cultivation areas from various places of South Korea and microbiome analysis was performed to analyze bacterial communities including Acidovorax species community. Next generation sequencing (NGS) was performed by extracting genomic DNA from 92 soil samples from 8 different provinces using a fast genomic DNA extraction kit. NGS data analysis results revealed that, total, 39,367 operational taxonomic unit (OTU), were obtained. NGS data results revealed that, most dominant phylum in all the soil samples was Proteobacteria (37.3%). In addition, most abundant genus was Acidobacterium (1.8%) in all the samples. In order to analyze species diversity among the collected soil samples, OTUs, community diversity, and Shannon index were measured. Shannon (9.297) and inverse Simpson (0.996) were found to have the highest diversity scores in the greenhouse soil sample of Gyeonggi-do province (GG4). Results from NGS sequencing suggest that, most of the soil samples consists of similar trend of bacterial community and diversity. Environmental factors play a key role in shaping the bacterial community and diversity. In order to address this statement, further correlation analysis between soil physical and chemical parameters with dominant bacterial community will be carried out to observe their interactions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.416-417
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• 2019

최근 도시농업의 중요성이 부각되면서 정책적 육성과 지원에 힘입어 다양한 형태의 도시농업 조성이 확대되고 있다. 현대 농업은 스마트팜 보급이 일반화되고 있지만, 대부분의 도시농업은 전통적인 작물재배 방법을 사용하고 있다. 따라서 본 논문에서는 도시농업의 다양한 유형을 고려한 도시형 스마트팜 시스템을 제안한다. 제안하는 도시형 스마트팜 시스템은 스마트팜 통합 컨트롤러와 스마트팜 서비스 플랫폼 서버로 구성되며, 스마트온실 표준 인터페이스 기반의 센서 및 구동기 연동과 IoT 서비스 플랫폼을 이용하여 도시농업의 유형에 따라 유동적으로 시스템을 구성 할 수 있는 환경을 제공한다.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.171-177
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• 2020

Under the influence of the Fourth Industrial Revolution, there are many tries to promote productivity enhancement and competitiveness by adapting smart farm technology that converges ICT technologies in agriculture. This smart farming technology is emerging as a new paradigm for future growth in agriculture. The development of real-time cultivation environment monitoring and automatic control system is needed to implement smart farm. Furthermore, the development of intelligent system that manages cultivation environment using monitoring data of the growth of crops is required. In this paper, a fast and efficient development method for implementing a cloud-based smart farm management system using a highly compatible and scalable web platform is proposed. It was verified that the proposed method using the web platform is effective and stable system implementation through the operation of the actual implementation system.

• Korean journal of applied entomology
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• v.62 no.4
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• pp.345-346
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• 2023

Natural Enemy in First (NEF) method is an ecological engineering application technology for natural enemies and was applied to strawberry in a smart farm-type greenhouse to evaluate its effect on the density of thrips and aphids. The control group was treated with pesticide and compared with the NEF treatment group, in which Orius minutus and Portulaca sp. were used as a natural enemy and habitats for thrips and aphids. The density of pests in the NEF group was effectively managed and similar to that in the control group.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.359-366
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• 2021

This study aimed to develop and verify the smart human resource management (HRM) program in a large scale greenhouse. HRM program delivers detailed work orders to workers and gathers work results by mobile phone application. Greenhouse managers can monitor the workload, work speed, quality of employee by HRM program and can analyse performance easily. Greenhouse Managers can set the work speed including 'twisting', 'trimming' and 'harvesting' in a greenhouse. It makes planning work schedule and assigns resources to each specific job easier. Therefore, the manager can arrange the number of employees to promote work performance and also easy to estimate the labor shortage. Greenhouse managers can evaluate the adequacy of the number of employees through job performance analysis by period and adjusts the supply/demand ratio of regular and non-regular employees. The HRM program can improve work efficiency by announcing the real-time work performance of all employees on a monitor screen to induce competition among workers and re-educate unripe employees who accomplish behind average to improving work skills.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.131-137
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• 2021

This study estimated acreage response functions for greenhouse paprika, greenhouse strawberry, open-land garlic, and open-land spinach by using Gyeongsangnamdo agricultural income data. The results show that the cultivation area for greenhouse paprika increased because the agricultural management costs decreased, and the risk of price volatility was relatively low. On the other hand, the cultivation area for greenhouse strawberries decreased due to increasing agricultural management costs and the greater risk of price volatility. In the case of open-land garlic and spinach, the cultivation area remained stagnant due to the greater risk of price volatility, despite increasing agricultural revenue. We derived several policy implications from our results. The risk of price volatility in agricultural products is greater for crops grown on land rather than crops grown in greenhouses. Therefore, the local government needs to adopt the "agricultural revenue guarantee insurance" in preference to crops grown on land rather than crops grown in greenhouses. On the other hand, in the case of greenhouse crops, agricultural management costs are very high. Thus, local government should focus on replacing old facilities and supplying smart-farm facilities that reduce agricultural management costs such as heating costs.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.320-326
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• 2016

From the perspective of climate-smart agriculture, it is becoming more critical to accurately estimate the amount of greenhouse gas emissions in the agricultural sector. In order to accurately ascertain the methane emissions from rice paddies, which account for a significant portion of the emission from the agricultural sector, we used the data from the 2010 Agriculture, Forestry and Fisheries Census, the revised water management scaling factors and their calculation program. In order to facilitate the analyses and understanding, the results were mapped using the ArcGIS software. The fact that the validation of the mapped values against the actual field measurements at one site showed little difference encourages the necessity to further this study. The administrative districts-based map of methane emission can help clearly identify the regional differences. Furthermore, the analysis of their major controlling factors will provide important scientific basis for the practical policy makings for methane mitigation.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.249-254
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• 2023

Recently, the distribution of drones is being activated by saving farmers' working time and protecting them from harmful human bodies from pesticides due to the mission of spraying pesticides using drones. It is possible to compensate for various shortcomings derived from the existing pesticide spraying method, wide-area control and helicopter control. Recently, the smart farm expansion policy has actively used it to generate profits for farmers by increasing harvests by monitoring growth information of various crops based on IoT in real time and collecting big data on key variables, and related drone industry technologies are also being developed. In this study, drones were applied to the work of shading greenhouses to secure diversity in agricultural application fields, and basic research on the greenhouse environment was conducted to materialize the technology related to shading. In order to provide high-quality light in consideration of the internal and external environment of the green house, basic research was conducted to enable light-shielding missions using drones through nozzle design for uniform spraying of nozzles of drones, light-transmitting rate analysis of green houses, and light-shielding agent application experiments.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.87-94
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• 2018

Intelligent transportation systems are one of the most important new forms of infrastructure on domestic roads, and is a system that makes possible the most efficient movement of vehicles on a road. The High Pass system, which is a domestic intelligent transportation system, started a little later than in other countries but developed at a rapid pace. With the recent introduction of smart tolling technology, it provided an opportunity to stop and review the tolling system. This study aims to investigate the driving method and results of LDCC for domestic smart towing through case study. Unlike other companies, Lotte Data Communication Company has long invested in payment systems. It has little experience investing in infrastructure, but participated in the Smart Toll System at the Gwangan Bridge in cooperation with the Busan City government, to lead the development of intelligent transportation systems. LDCC, which has made new investments, not only exceeded its existing core competencies, but also upgraded Korea's tolling system's ability to reduce greenhouse gas emissions and improved its financial performance.