• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.193-198
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• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• The Korea Journal of Herbology
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• v.34 no.2
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• pp.41-47
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• 2019

Objectives : A traditional herbal prescription, Kyung-Ok-Ko (KOK), has long been used in oriental medicine as an invigorant for age-related diseases, such as amnesia and stroke. However, the beneficial value of KOK for immune responses is largely unknown. Based on the above mentioned effects of KOK, other previous reports, and its use in traditional medicine, we hypothesized that KOK displays beneficial effects against methotrexate (MTX)-induced immune suppression. Methods : We investigated the effects of KOK (0.6 g/kg/day, oral (p.o.)) on deteriorated immunity caused by MTX (2 mg/kg/day, p.o.) in an immune suppression mouse model. MTX was fed to mice once a day for 7 days. After the immune responses of the mice deteriorated by MTX treatment, KOK in water was fed to the mice once a day for 14 days. We then measured the expression levels of various cytokines, such as T helper cell (Th1, Th2) cytokines, and the number of immune cells, such as spleen T cells, B cells, and macrophages. Results : The data showed that MTX decreased Th1 profiles (interferon $(IFN)-{\gamma}$, interleukin (IL)-2, IL-12) and the number of immune cells, and increased Th2 profiles (IL-4, IL-5, IL-13), which were normalized significantly by post-administration of KOK. However, there was no significant difference in body-weight gain between MTX- and KOK-treated mice. Conclusion : These results indicate that KOK has immune-enhancing functions and reduces immunotoxicity of MTX, suggesting that supplementation with KOK will improve immune responses clinically and be useful for the prevention of immune-related diseases.

• Nutrition Research and Practice
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• v.16 no.5
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• pp.589-603
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• 2022

BACKGROUND/OBJECTIVES: Previous studies have reported that protein supplementation contributes to the attenuation of inflammation. Serious trauma such as burn injury usually results in the excessive release of inflammatory factors and organs dysfunction. However, a few reports continued to focus on the function of protein ingestion in regulating burn-induced inflammation and organ dysfunction. MATERIALS/METHODS: This study established the rat model of 30% total body surface area burn injury, and evaluated the function of blended protein (mixture of whey and soybean proteins). Blood routine examination, inflammatory factors, blood biochemistry, and immunohistochemical assays were employed to analyze the samples from different treatment groups. RESULTS: Our results indicated a decrease in the numbers of white blood cells, monocytes, and neutrophils in the burn injury group administered with the blended protein nutritional support (Burn+BP), as compared to the burn injury group administered normal saline supplementation (Burn+S). Expressions of the pro-inflammatory factors (tumor necrosis factor-α and interleukin-6 [IL-6]) and chemokines (macrophage chemoattractant protein-1, regulated upon activation normal T cell expressed and secreted factor, and C-C motif chemokine 11) were dramatically decreased, whereas anti-inflammatory factors (IL-4, IL-10, and IL-13) were significantly increased in the Burn+BP group. Kidney function related markers blood urea nitrogen and serum creatinine, and the liver function related markers alanine transaminase, aspartate aminotransferase, alkaline phosphatase, and lactate dehydrogenase were remarkably reduced, whereas albumin levels were elevated in the Burn+BP group as compared to levels obtained in the Burn+S group. Furthermore, inflammatory cells infiltration of the kidney and liver was also attenuated after burn injury administered with blended protein supplementation. CONCLUSIONS: In summary, nutritional support with blended proteins dramatically attenuates the burn-induced inflammatory reaction and protects organ functions. We believe this is a new insight into a potential therapeutic strategy for nutritional support of burn patients.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.340-351
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• 2023

Landslides are major natural geological hazards that cause enormous property damage and human casualties annually. The vulnerability of mountainous areas to landslides is further exacerbated by the impacts of climate change. Soil depth is a crucial parameter in landslide and debris flow analysis, and plays an important role in the evaluation of watershed hydrological processes that affect slope stability. An accurate method of estimating soil depth is to directly investigate the soil strata in the field. However, this requires significant amounts of time and money; thus, numerous models for predicting soil depth have been proposed. However, they still have limitations in terms of practicality and accuracy. In this study, 71 seismic survey results were collected from domestic mountainous areas to estimate soil depth on hill slopes. Soil depth was estimated on the basis of a shear wave velocity of 700 m/s, and a database was established for slope angle, elevation, and soil depth. Consequently, the statistical characteristics of soil depth were analyzed, and the correlations between slope angle and soil depth, and between elevation and soil depth were investigated. Moreover, various soil depth prediction models based on slope angle were investigated, and corrected linear and exponential soil depth prediction models were proposed.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.145-156
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• 2023

In this study, Fourier-transform near-infrared (FT-NIR) spectra of Korean red pine seeds stored at -18℃ and 4℃ for 18 years were analyzed. To develop seed-germination prediction models, the performance of seven machine learning methods, namely XGBoost, Boosted Tree, Bootstrap Forest, Neural Networks, Decision Tree, Support Vector Machine, PLS-DA, were compared. The predictive performance, assessed by accuracy, misclassification, and area under the curve (0.9722, 0.0278, and 0.9735 for XGBoost, and 0.9653, 0.0347, and 0.9647 for Boosted Tree), was better for the XGBoost and decision tree models when compared with other models. The 54 wave-number variables of the two models were of high relative importance in seed-germination prediction and were grouped into six spectral ranges (811~1,088 nm, 1,137~1,273 nm, 1,336~1,453 nm, 1,666~1,671 nm, 1,879~2,045 nm, and 2,058~2,409 nm) for aromatic amino acids, cellulose, lignin, starch, fatty acids, and moisture, respectively. Use of the NIR spectral data and two machine learning models developed in this study gave >96% accuracy for the prediction of pine-seed germination after long-term storage, indicating this approach could be useful for non-destructive viability testing of stored seed genetic resources.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.248-257
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• 2023

Because of various societal changes, such as the recent improvement in income levels and extension of the flexible work system, the demand for forest recreation activities and their use patterns are undergoing a change. Accordingly, it is necessary to identify the characteristics of each type through the segmentation of the overall forest recreation and welfare markets and to plan differentiated policies for each market type. This study classifies the forest recreation and welfare activities according to four types of users (i.e., passive usage type, ordinary type, active lover type, and indifferent type) using the Latent Class Analysis and examines their demographic and socioeconomic characteristics to explain the differences between the groups. Three policy implications were derived from the results obtained: 1) the group experiencing forest recreation welfare is subdivided; 2) the socioeconomic characteristics that distinguish the groups undertaking forest recreation activities were identified; and 3) the policy targets and characteristics that can increase the experience of forest recreation welfare were identified. This study is insightful as it suggests differentiated policies for each group and proposes policy measures to move to the desirable group.

• Convergence Security Journal
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• v.22 no.5
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• pp.69-80
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• 2022

Now that climate change and food resource security are becoming issues around the world, smart farms are emerging as an alternative to solve them. In addition, changes in the production environment in the primary industry are a major concern for people engaged in all primary industries (agriculture, livestock, fishery), and the resulting food shortage problem is an important problem that we all need to solve. In order to solve this problem, in the primary industry, efforts are made to solve the food shortage problem through productivity improvement by introducing smart farms using the 4th industrial revolution such as ICT and BT and IoT big data and artificial intelligence technologies. This is done through the public and private sectors.This paper intends to consider the minimum requirements for the smart farm data collection system for the development and utilization of smart farms, the establishment of a sustainable agricultural management system, the sequential system construction method, and the purposeful, efficient and usable data collection system. In particular, we analyze and improve the problems of the data collection system for building a Korean smart farm standard model, which is facing limitations, based on in-depth investigations in the field of livestock and livestock (pig farming) and analysis of various cases, to establish an efficient and usable big data collection system. The goal is to propose a method for collecting big data.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1321-1330
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• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.67-74
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• 2024

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method's accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.