• Journal of the Korean Society of Food Culture
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• v.13 no.5
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• pp.431-460
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• 1998

After the Japanese Invasion of Korea in 1592, correspondents of Chosun called on Japan twelve times, as a mission, from 1607 to 1811. The suite of Chosun Communication Facilities (correspondents) was composed of nearly 500 people including a senior envoy, a junior envoy, other envoyes, and cooks. After preparing traveling expenses, Sifts, foods, medicinal stuffs, and ginseng, they shipped people and freight in three passenger ships and three freighters. They departed from Pusan for Edo(Tokyo). There were 28 stop-overs on their way to Edo and the banquet was given for them at every stop. The arrangements of the table for each banquet were made up of 753 Seon(tables), 3Jeup(soups) and 15Che(dishes). 753Seon(tables) is Ganban(the table for decoration) and a substantial food reception was composed of 3Jeup(soups) and 15Che(dishes). 753Seon(tables) was called Dadopoong(the food of refreshment) style and this was the standard arrangement of the banquet table in Japan. It was comprised of 3Jeup(soups) and 15Che(dishes). On their way from Edo to Thusima Island, food was afforded to correspondents for later preparation and dining. The banquet that the master of Thusima Island gave was composed of the first and second style banquets. The first one was Chusun style and the second one was Japanese style.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.287-288
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• 2021

There is a growing interest in plant factories around the world, and in recent years, research on an automated management system that can grow crops even in an urban environment is in progress. The development of this management system is based on the development of ICT (Information Communications Technology) technology, and research is being conducted focusing on facilities, light sources, temperature, humidity and automation to increase the productivity of plants. Research on standardization is ongoing. In this paper, by constructing a test bed that can grow hydroponic ginseng in a container environment, we propose a model that can monitor and manage the environment for the growth process on mobile.

• Journal of the Korean Society of Food Science and Nutrition
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• v.11 no.1
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• pp.31-36
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• 1982

Sugars and nitrogen contents and physico-chemical properties of ethanol extracts of fresh, dried, and tail ginsengs with different concentrations of the solvent were investigated. The transmittance at 550nm of fresh, dried and tail ginseng extracts (1% D.Wsoln) respectively, and all the extracts were slightly viscous-sticky, brown and pH of 4.8-6.2 Total sugars, sucrose and starch contents of the extracts were decreased with increasing of enthanol concentration as a solvent. Total sugar content of the extracts were decreased in the order of dried, tail and fresh ginseng and sucrose content were decreased in the order of fresh, driedcand tail ginsengs and starch content were decreased in the order of tail, dried and fresh ginsengs. The reducing sugar contents of the extracts were 4.9-3.8 %, 8.6-12.8 % and 7.6-9.1% in fresh, dried and tail ginsengs, respectively. Total nitrogen contents of the extracts were 2.3-4.6% in average and decreased in the order of dried, fresh and tail ginsengs.

• The Korean Journal of Mycology
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• v.37 no.2
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• pp.144-149
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• 2009

The occurrences of the major diseases and the densities of air-born microbes were surveyed in the cultivation facilities for oyster mushroom (Pleurotus ostreatus), king oyster mushroom (Pleurotus eryngii), and enoki mushroom (Flammulina velutipes) in different areas of Korea. Green mold disease was most often developed in oyster mushroom bed cultivation with the disease incidence rate of approximate 10% while the disease incidences from bottle and plastic envelop cultivation were less than 1~2%. In the bed cultivation, the major air-born microbes in the growth room were Aspergillus, Penicillium, Trichoderma, and Curvularia with the total fungal population density of 567~1,297 CFU/$m^3$ . However, only Trichoderma and Penicillium were detected in the growth rooms and innoculation rooms of bottle and plastic envelop cultivation with the densities of 350~700 CFU/$m^3$ and 160~260 CFU/$m^3$, respectively. The bacterial diseases become evident in the growth rooms of bottle and plastic envelop cultivation with the approximate incidence rate of 10%. The identified bacterial species were Brevibacillus levelkil, Rhizobium radiobacter, Brevundimonas vesicularis, Pseudomonas mosselii, Microbacterium testaceum. Sphingomonas panmi, Sphingomonas yabuuchiae, Paracocus dinitrificans, Curtobacterium flaccumfaciens pv. flaccumfaciens and some unidentified bacteria with the densities of 40~6,359 CFU/$m^3$ in the growth rooms and 9 CFU/$m^3$ in the inoculation room. This study indicated that the green mold disease by fungal strains was the major mushroom disease in the bed cultivation and suggested that the contamination of bacteria and fungi together in the growth media could result in severe production loss. The plastic envelope and bottle cultivation were evidenced to be less susceptible to such contaminations.

• 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.