• Research in Plant Disease
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• v.30 no.1
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• pp.26-33
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• 2024

In 2023, symptoms like damping-off disease were observed in 74 paprika growing in greenhouses in Cheorwon-gun, Gangwon-do, Korea. In this study, we tried to find the cause of the damping-off disease outbreak. We collected symptomatic seedlings and observed the typical crescent-shaped conidia of Fusarium oxysporum by microscope. To confirm the presence of F. oxysporum in the samples, polymerase chain reaction was performed using primers specific for F. oxysporum; the resulting sequence showed 99.11% identity with F. oxysporum. To confirm the pathogenicity of the F. oxysporum (CW) isolated from the samples, healthy paprika plants were inoculated with F. oxysporum CW and damping-off symptoms were observed 2 weeks later. To investigate whether the damping-off disease outbreak in Cheorwon-gun was caused by F. oxysporum-contaminated seeds, 100 paprika seeds were disinfected and placed in Murashige and Skoog medium. Typical pink F. oxysporum hyphae were found only in control non-disinfected seeds. An 18S rRNA-based and a TEF genebased phylogenetic analysis showed that the F. oxysporum CW isolate was not grouped with a F. oxysporum isolate reported from Cheorwon-gun in 2019. This study is the first report that an outbreak of damping-off disease in paprika in Cheorwon-gun, Gangwon-do, Korea, was caused by contamination of F. oxysporum seeds.

• Korean Journal of Heritage: History & Science
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• v.51 no.3
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• pp.4-31
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• 2018

It is difficult to ascertain exactly when the Hongjuupseong (Town Wall) was first constructed, due to it had undergone several times of repair and maintenance works since it was piled up newly in 1415, when the first year of the reign of King Munjong (the 5th King of the Joseon Dynasty). Parts of its walls were demolished during the Japanese occupation, leaving the wall as it is today. Hongseong region is also susceptible to historical earthquakes for geological reasons. There have been records of earthquakes, such as the ones in 1978 and 1979 having magnitudes of 5.0 and 4.0, respectively, which left part of the walls collapsed. Again, in 2010, heavy rainfall destroyed another part of the wall. The fortress walls of the Hongjuupseong comprise various rocks, types of facing, building methods, and filling materials, according to sections. Moreover, the remaining wall parts were reused in repair works, and characteristics of each period are reflected vertically in the wall. Therefore, based on the vertical distribution of the walls, the Hongjuupseong was divided into type I, type II, and type III, according to building types. The walls consist mainly of coarse-grained granites, but, clearly different types of rocks were used for varying types of walls. The bottom of the wall shows a mixed variety of rocks and natural and split stones, whereas the center is made up mostly of coarse-grained granites. For repairs, pink feldspar granites was used, but it was different from the rock variety utilized for Suguji and Joyangmun Gate. Deterioration types to the wall can be categorized into bulging, protrusion of stones, missing stones at the basement, separation of framework, fissure and fragmentation, basement instability, and structural deformation. Manually and light-wave measurements were used to check the amount and direction of behavior of the fortress walls. A manual measurement revealed the sections that were undergoing structural deformation. Compared with the result of the light-wave measurement, the two monitoring methods proved correlational. As a result, the two measuring methods can be used complementarily for the long-term conservation and management of the wall. Additionally, the measurement system must be maintained, managed, and improved for the stability of the Hongjuupseong. The measurement of Nammunji indicated continuing changes in behavior due to collapse and rainfall. It can be greatly presumed that accumulated changes over the long period reached the threshold due to concentrated rainfall and subsequent behavioral irregularities, leading to the walls' collapse. Based on the findings, suggestions of the six grades of management from 0 to 5 have been made, to manage the Hongjuupseong more effectively. The applied suggested grade system of 501.9 m (61.10%) was assessed to grade 1, 29.5 m (3.77%) to grade 2, 10.4 m (1.33%) to grade 3, 241.2 m (30.80%) and grade 4. The sections with grade 4 concentrated around the west of Honghwamun Gate and the east of the battlement, which must be monitored regularly in preparation for a potential emergency. The six-staged management grade system is cyclical, where after performing repair and maintenance works through a comprehensive stability review, the section returned to grade 0. It is necessary to monitor thoroughly and evaluate grades on a regular basis.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.5
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• pp.49-65
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• 2019

This study was carried out to investigate the domestic and international development of a new cultivar of the Rose of Sharon (Hibiscus syriacus L.), the Korean national flower, and the protection of the new plant variety. In addition, it will be used as basic data for the expansion of domestic distribution, promoting oversea export, and expanding the range of landscape architectural use. A total of 97 varieties received plant variety protection rights from the Korea Seed & Variety Service from 2004 to 2018. The selection criteria were plants having unique flowers, growth habits, and variegated leaves. Some cultivars with unique features, such as flower size, shape, and red eyes were available for focus planting. Plant varieties with tall and strong growth patterns have been highly valuable for street and focus planting. Cultivars with dwarf stems and compact branches are utilized for pot planting and bonsai. The protected cultivars were mostly single flower varieties, with two semi-double flowers. There were 57 cultivars of pink flowers with red eyes and 21 cultivars of white flowers with red eyes. There were 61 cultivars developed by crossing, 23 cultivars through interspecific hybridization and 7 cultivars developed through radiation treatment and mutation. The Hibiscus cultivars registered to the United States Patent and Trademark Office (USPTO) consisted of seven cultivars each from the United States, the United Kingdom, and the Netherlands, four from South Korea, and three from Belgium. The Hibiscus cultivars registered to the European Community Plant Variety Office (CPVO) consisted of 16 cultivars from France, 9 from the Netherlands, 5 from the UK and 1 from Belgium. The cultivars that received both plant patent and plant breeder rights in the United States and Canada were 'America Irene Scott', 'Antong Two', 'CARPA', 'DVPazurri', 'Gandini Santiago', 'Gandini van Aart', 'ILVO347', 'ILVOPS', 'JWNWOOD 4', 'Notwood3', 'RWOODS5', 'SHIMCR1', 'SHIMRR38', 'SHIMRV24', and 'THEISSHSSTL'. 'SHIMCR1' and 'SHIMRV24' acquired both domestic plant protection rights and overseas plant patents. The 14 cultivars that received both US plant patents and European protection rights were 'America Irene Scott', 'Bricutts', 'DVPAZURRI', 'Gandini Santiago', 'Gandini van Aart', 'JWNWOOD4', 'MINDOUB1', 'MINDOUR1', 'MINDOUV5', 'NOTWOOD3', 'RWOODS5', 'RWOODS6', 'Summer Holiday', and 'Summer Night'. The cultivars that obtained US patents consisted of 18 cultivars (52.9%) with double flowers, 4 cultivars (11.8%) with semi-double flowers, and 12 cultivars (35.3%) with single flowers. The cultivars that obtained European new variety protection rights, consisted of 11 cultivars (34.3%) with double flowers, 12 cultivars (21.9%) with semi-double flowers, and 14 cultivars (43.8%) with single flowers. In the future, new cultivars of H. syriacus need to be developed in order to expand domestic distribution and export abroad. In addition, when developing new cultivars, it is required to develop cultivars with shorter branches for use in flower beds, borders, hedges, and pot planting.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.