• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.17-26
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• 2021

Greenhouses have been damaged due to the uplift pressure from strong wind, for which rebar piles are often installed near the greenhouse to resist the pressure. For the effective design of rebar piles, it is necessary to access the shear strength of soil on which the greenhouse is constructed. This study experimentally evaluates the shear strength of the soil beneath the greenhouse. Four soil samples were collected from four agricultural sites, and prepared for testing with 75, 80, 85, and 90% compaction rates. One-dimensional unconfined compression test (UC), consolidated-undrained triaxial test (CU), and resonant column test (RC) were performed for the evaluation of shear strength and shear modulus. Generally, the higher shear strength and modulus were observed with the higher compaction rates. In particular, the UC shear strength increases with the increase of #200 sieve passing rate. Resulting from the CU test, the sample with the most of coarse soil had the highest friction angle, but the variation is small among samples. Resulting from the CU and RC tests, the ratio of maximum shear modulus with the major principle stress at failure was the higher at the finer soil. The ratio was two to three times greater than the ratio from the standard sand. This indicates that the shear strength is lower for the fine soil than the coarse soil at the same shear modulus. The results of this study will be a useful resource for the estimation of the pull-out strength of the rebar pile against the uplift pressure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1254-1262
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• 2021

Recently, the agricultural environment in Korea is rapidly changing due to the aging and decline of the agricultural population, and in order to solve these problems, it is urgently required to improve the agricultural productivity and reduce the labor force. To solve this problem, a smart farm fused with ICT technology is being proposed as an alternative. In Korea, smart farms are currently mainly used in greenhouses. In this paper, this smart farm technology is to be applied to the cultivation of sprouted ginseng. To this end, we use seedlings (about 1.0g) to grow a solid medium and bed for cultivating sprouted ginseng, a fresh ginseng that is produced in a short period of time (2~3 months) with a clean environment management technology that does not use chemical pesticides and hydroponics in a greenhouse developed. In addition, an IoT-based growth environment management system was developed to monitor the growth process of sprouted ginseng in such an environment and to control driving devices.

• Journal of Energy Engineering
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• v.27 no.4
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• pp.70-79
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• 2018

A heat pump system using the thermal effluent from the Jeju thermal power plant of KOMIPO was constructed with the capacity of 300 RT to supply cool or hot water to greenhouse facilities located 3 km from the power station. The way of transporting heat from the thermal effluent to greenhouses at a long distance was optimized, and a monitoring system to measure the water temperature and detect a leakage in a pipe conduit was also installed. This paper presents the system configuration of the constructed heat pump system for air conditioning and heating of greenhouse facilities in Jeju, and the characteristics of major components deployed in the system. The preoperational tests of the heat pump system were conducted during the summer season in 2018 for evaluation of its cooling performance. The operational stability and cooling performance of the heat pump system were confirmed by investigating the measured fluid temperature and flow rate, and COP of the heat pump in a cooling mode.

• The Plant Pathology Journal
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• v.35 no.5
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• pp.538-542
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• 2019

In 2017, two new tomato mosaic virus (ToMV) isolates were collected from greenhouses in Buyeo, Chungcheongnam-do, South Korea. Full-length cDNAs of the new ToMV isolates were cloned into dual cauliflower mosaic virus 35S and T7 promoter-driven vectors, sequenced and their pathogenicities investigated. The nucleotide sequences of isolates GW1 (MH507165) and GW2 (MH507166) were 99% identical, resulting in only two amino acid differences in nonconserved region II and the helicase domain, Ile668Thr and Val834Ile. The two isolates were most closely related to a ToMV isolate from Taiwan (KJ207374). Isolate GW1 (Ile668, Val834) induced a systemic hypersensitive response in Nicotiana benthamiana compared with the isolate GW2, which a single residue substitution showed was due to Val834.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.101-108
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• 2019

The domestic duck industry is the sixth-largest among the livestock industries. However, 34.3% of duck houses were the duck houses arbitrarily converted from plastic greenhouses. This type of duck house was difficult to properly manage internal air temperature and humidity environment. Humidity environment inside duck houses is an important factor that directly affects the productivity and disease occurrence of the duck. Although the humidity environments of litters (bedding materials) affect directly the inside environment of duck houses, there are only few studies related to humidity environment of litters. In this study, evaporation rates from litters were evaluated according to air temperature, relative humidity, water contents of litters, and wind speed. The experimental chamber was made to measure evaporation rates from litters. Temperature and humidity controlled chamber was utilized during the conduct of the laboratory experiments. Using the measured data, a multi linear regression analysis was carried out to derive the calculation formula of evaporation rates from litters. In order to improve the accuracy of the multi linear regression model, the partial vapor pressure directly related to evaporation was also considered. Variance inflation factors of air temperature, relative humidity, partial vapor pressure, water contents of litters, and wind speed were calculated to identify multicollinearity problem. The Multiple $R^2$ and adjusted-$R^2$ of regression model were calculated at 0.76 and 0.71, respectively. Therefore, the regression models were developed in this study can be used to estimate evaporation rates from the litter of duck houses.

• Journal of Apiculture
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• v.33 no.4
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• pp.239-250
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• 2018

We investigated the pollination activity of honeybees (Apis mellifera L.) in terms of different climatic conditions in the greenhouse cultivation of watermelons (Citrullus lanatus L.) during winter. The aim of the study was to search a climatic condition which effectively can be use honeybees as pollinators during the flowering season of watermelons in winter or early spring. The average climatic conditions inside the greenhouse during the bee activity time (BAT)-between 10:00 and 16:00 in mid-Februarywere a temperature of $30.4^{\circ}C$, relative humidity of 53.7%, illuminance level of 22,728.4lx, and UV level of $0.233mW/cm^2$. Bee traffic and foraging activity were at their greatest at 10:00 and tended to decrease with time. Male watermelon flowers typically dehisced between 10:00 and 12:00. Climatic conditions were significantly correlated with bee activities, including bee traffic and foraging activity. Bee activities were positively correlated with temperature, illuminance level, and UV level but negatively correlated with relative humidity. Temperature had the greatest effect on honeybee behavior. Among the foraging honeybees, the number of high-flying bees that did not pollinate flowers showed a strong positive correlation with temperature, and the number of bees landing on the flowers showed a positive correlation with the UV level. The temperature range inside greenhouses at which the pollination activities of honeybees can be maintained efficiently during winter watermelon pollination was found to be $21{\sim}25^{\circ}C$.

• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.213-231
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• 2021

Purpose: This study intends to provide decision-making information to improve efficiency by analyzing the management efficiency of smart greenhouse business entities and identifying factors that affect the efficiency based on input and output. Methods: The subjects of analysis were business entities for cultivating strawberries in smart greenhouses in Jeolla region (northern and southern Jeolla provinces), and the analysis focused on the management performance of the 2019-2020 crop period (year). Data Envelopment Analysis(DEA) was applied as an analysis method for efficiency analysis, Quantile Regression(QR) analysis was applied as a factor affecting the efficiency. Results: The reason for the efficiency gap between business entities was that there were many business entities that did not minimize the input cost at the current level of output, and the area where the variance among business entities was large was the fixed cost per 10a. In the results of the affecting factor analysis, it was found that the seed-seedlings cost, fertilizer cost, other material cost, and employment and labor cost had a negative (-) effect on the efficiency, and that the repair and maintenance cost had a positive (+) effect. Conclusion: Therefore, to achieve the efficiency of scale, it is necessary to reduce the input scale to an appropriate level. In the case of business entities with low efficiency by quartile, the seed-seedlings, fertilizer, and other material costs reduce expenditures, and repair maintenance costs can improve efficiency by increasing expenditures.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.51-63
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• 2022

Up-to-date statistics of crop cultivation status is essential for farm land management planning and the advancement in remote sensing technology allows for rapid update of farming information. The objective of this study was to develop a classification model of rice paddy or winter crop fields based on NDWI, NDVI, and HSV indices using Sentinel-2 satellite images. The 18 locations in central Korea were selected as target areas and photographed once for each during summer and winter with a eBee drone to identify ground truth crop cultivation. The NDWI was used to classify summer paddy fields, while the NDVI and HSV were used and compared in identification of winter crop cultivation areas. The summer paddy field classification with the criteria of -0.195

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.93-100
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• 2023

In this paper, the purpose of this study is to melt the process of returning to farming through games and settle down in a stable manner to ensure that there are no more prospective young farmers who wish to return to farming but cannot proceed with their dreams due to various barriers of reality. The game was designed to develop in the order of fields, greenhouses, automation systems, and smart farms, and to grow the crops they want at the early level, and added a community system to highlight that rural areas are community life, not individualistic life. Support benefits or information provided by local governments or governments were inserted into the community system so that prospective farmers could naturally access the information.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.35-41
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• 2022

Heavy snow damage can be prevented in advance with an appropriate security system. To develop the security system, we developed a model that predicts snow depth after a few hours when the snow depth is observed, and utilized it to calculate a failure probability with various types of greenhouses and observed snow depth data. We compared the Markov chain model and Bayesian long short-term memory models with varying input data. Markov chain model showed the worst performance, and the models that used only past snow depth data outperformed the models that used other weather data with snow depth (temperature, humidity, wind speed). Also, the models that utilized 1-hour past data outperformed the models that utilized 3-hour data and 6-hour data. Finally, the Bayesian LSTM model that uses 1-hour snow depth data was selected to predict snow depth. We compared the selected model and the shifting method, which uses present data as future data without prediction, and the model outperformed the shifting method when predicting data after 11-24 hours.