• Journal of People, Plants, and Environment
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• v.24 no.4
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• pp.341-353
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• 2021

Background and objective: As the agricultural industry becomes a more convergent industry, it is believed that the demand for human resources by companies will change. Therefore, a survey was conducted to investigate the human resources required by agriculture companies. Methods: In the survey on 77 agriculture companies, 98.7% of respondents answered that new employees with a college degree needed additional training to adapt to practical affairs. Results: The first priority of education was "community spirit" (22.1%) and the second priority was "convergence capability" (15.6%). The most important educational goal desired by agricultural companies was "cultivating human resources with community spirit and ethical judgment", followed by "cultivating human resources with serious communication and problem-solving skills", and "cultivating human resources with scientific thinking and unique creative imagination." Sub-competencies that companies want agricultural colleges to strengthen were "community spirit" 4.32(SD=0.96), "desirable values" 4.30 (SD = 1.05), "sympathy" 4.28 (SD = 0.95), "convergence capability" 4.16 (SD = 0.88), "creativity" 4.11 (SD = 0.83), "civic spirit" 4.10 (SD = 0.91), and "rational/critical thinking" 3.94 (SD = 1.04). There was a significant difference in sub-competencies that require reinforcement depending on the number of full-time employees. "Creativity" was most necessary in companies with less than 3 employees (4.39), and 4~7 employees (4.33), and "aesthetics"" in companies with less than 3 employees (3.94), and 4-7 employees (3.61) "Civic spirit" was most necessary in companies with 31 employees or more (4.33). Conclusion: The most important educational goal desired by companies was "cultivating human resources with community spirit and ethical judgment".

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.743-748
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• 2021

Amid the global population growth and climate change, high-tech smart farm technology that combines agriculture and ICT is actively being researched in Korea to solve sustainable crises such as declining population of agricultural and livestock industries. Existing smart farms are growing mainly on crops with low price competitiveness. Food consumption structures are becoming more sophisticated and diverse, and as agricultural consumption patterns change, the smart farm system also needs to be optimized for growing high-value special crops. To this end, an integrated ICT management system was designed and implemented by establishing a containerized smart farm environment specialized in growing sprout ginseng. Through this, it is possible to implement high-tech agricultural production and lead new future convergence industries through the convergence of ICT, agriculture, and the latest technologies and farming.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.183-190
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• 2017

Recently the scale and area of greenhouse cultivation have been enlarged in Korea, and its importance in domestic agriculture is being increased. According to these situation, environment control systems are widely used in greenhouses. Even though development of greenhouse facilities and control devices, cultivation skill using them is in lower level more than european countries and Japan. In this study, we propose intelligent information system based on information-communication technology that supports environment control systems. Proposed system is able to support to maintain optimal environment for plant growth using data from environment control system, and also give useful knowledge for cultivation by active way. Furthermore, it estimates future status of plant growth, and suggest best strategy of environment control for current stage.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.207-213
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• 2019

Assistance for the quality assurance management and developers to provide high quality software products. Using a bug record keeping system is exceptionally important in software progress, and it is followed vastly by majority of software producing companies in modern era. Regular application of a bug record keeping system is very helpful in developing software systems. We developed this system which helps the software testing team to keep a complete record of their testing activities during the software testing, also increases your confidence in the software quality, class and worth. Our proposed system offers trouble-free and effortless approaches to acquire desired information about bug, also produces different kinds of reports like summary reports, detailed reports etc. It gives facility to create, delete and update any project. Our developed application system is designed by using visual c# at front end and sql server 2008 management studio express at back end.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.223-235
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• 2020

In this study, we propose an approach for calibrating the elevation of a DEM (Digital Elevation Model), one of the key data in realizing unmanned aerial vehicle image-based precision agriculture. First of all, radiometric correction is performed on the orthophoto, and then ExG (Excess Green) is generated. The non-vegetation area is extracted based on the threshold value estimated by applying the Otsu method to ExG. Subsequently, the elevation of the DEM corresponding to the location of the non-vegetation area is extracted as EIFs (Elevation Invariant Features), which is data for elevation correction. The normalized Z-score is estimated based on the difference between the extracted EIFs to eliminate the outliers. Then, by constructing a linear regression model and correcting the elevation of the DEM, high-quality DEM is produced without GCPs (Ground Control Points). To verify the proposed method using a total of 10 DEMs, the maximum/minimum value, average/standard deviation before and after elevation correction were compared and analyzed. In addition, as a result of estimating the RMSE (Root Mean Square Error) by selecting the checkpoints, an average RMSE was derivsed as 0.35m. Comprehensively, it was confirmed that a high-quality DEM could be produced without GCPs.

• Food Science of Animal Resources
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• v.43 no.3
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• pp.402-411
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• 2023

This study was conducted to investigate the bactericidal effect of nisin (Nisin) only, atmospheric pressure plasma (APP) only, and a combination of APP and nisin (APP+Nisin)(APP+Nisin) on beef jerky and sliced ham inoculated with Escherichia coli O157:H7, gram-negative bacteria. The bactericidal effect against E. coli O157:H7 and Listeria monocytogenes was confirmed using a nisin solution at a concentration of 0-100 ppm, and APP+Nisin was tested on beef jerky and sliced ham using 100 ppm nisin. Beef jerky and sliced ham were treated with APP for 5 min and 9 min, respectively. In the bacterial solution, 100 ppm nisin out of 0-100 ppm nisin exhibited the highest bactericidal activity against L. monocytogenes (gram-positive bacteria; p<0.05); however, it did not exhibit bactericidal effects against E. coli O157:H7 (gram-negative bacteria). The APP+Nisin APP+Nisin exhibited a 100% reduction rate in both E. coli O157:H7 and L. monocytogenes compared to the control group, and was more effective than the Nisin. The APP+Nisin decreased the number of colonies formed by 0.80 and 1.96 Log CFU/g for beef jerky and sliced ham, respectively, compared to the control, and exhibited a higher bactericidal effect compared to the Nisin (p<0.05). These results demonstrate the synergistic bactericidal effect of APP and nisin, providing a possible method to improve the limitations of nisin against gram-negative bacteria. In addition, this technology has the potential to be applied to various meats and meat products to control surface microorganisms.

• ALGAE
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• v.37 no.4
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• pp.331-347
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• 2022

Atopic dermatitis (AD) is one of major skin inflammatory diseases characterized by excessive Th2-mediated immune responses. Recent evidence provides that interlukin-13 (IL-13) plays the role of a key Th2 cytokine that drives the inflammation underlining AD. Due to adverse effects of commercially available synthetic drugs, the need for treatments based on natural products is gaining much attention. Sargassum horneri is an edible brown algae known for beneficial bioactivities including anti-inflammation. We investigated if polyphenol-rich S. horneri extracts (SHE) could suppress AD-like skin lesions in NC/Nga mice and if that involved inhibition of the infiltration of Th2-mediated cytokine IL-13. We observed markedly increased infiltration of IL-13 positive cells in AD-like skin lesions of mice but SHE treatments decreased it. Also, the dermal expression of IL-13 was sufficient to cause inflammatory responses in mice skin resembling human AD. SHE suppressed the dermal infiltration of inflammatory cells where IL-13 plays a crucial role in skin tissues and in the recruitment of inflammatory cells. Furthermore, it was confirmed that SHE reduced T cell, dendritic cell, and macrophage populations in spleen. Moreover, SHE decreased the collagen deposition in skin and ear dermis resulting in reduced fibrosis that occurs in AD due to excessive collagen. Taken together, our results reveal that SHE suppressed the infiltration of inflammatory cells into skin dermis by decreasing the infiltration of IL-13 positive cells. Therefore, SHE could be taken as a useful therapeutic agent to alleviate AD.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1254-1269
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• 2023

This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.145-151
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• 2004

According to the development of the aircraft industry, the fatigue strength of Aluminum Alloy becomes a great important material, but it seems that we don't understand an effect on the crack growth behaviour very well. This thesis is not only studied about the five kinds of 2017-T3, 2023-T3, 5083-0, 7075-T6, 7N01-T6 among the Aluminum Alloy which are the main materials of the aircraft, but also small or large relations against the fatigue strength of them. The consequence of the research was being progressed the accordance with the order. That is, The order is 2024-T3> 2017-T3> 7N01-T6> 7075-T6> 5083-0. These inclusion came out the acceleration phenomena in the crack growth behaviour among the high ${\Delta}K$ section Nevertheless I figured out their effects were being ignored at the 2b step.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.4
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• pp.21-30
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• 2022

Renewable energy systems aim to achieve carbon neutrality and replace fossil fuels. Photovoltaic technologies are the most widely used renewable energy. However, they require a large operating area, thereby decreasing available farmland. Accordingly, agrivoltaic systems (AVSs)-innovative smart farm technologies that utilize solar energy for crop growth and electricity production-are attracting attention. Although several empirical studies on these systems have been conducted, comprehensive research on their design is lacking, and no standard model suitable for South Korea has been developed. Therefore, this study created an integral design of AVS reflecting domestic crop cultivation conditions and conducted a structural analysis for safety assessment. The shading ratio, planting distance, and agricultural machinery work of the system were determined. In addition, national construction standards were applied to evaluate their structural safety using a finite element analysis. Through this, the safety of this system was ensured, and structural considerations were put forward. It is expected that the AVS model will allow for a stable utilization of renewable energy and smart farm technologies in rural areas.