• The Plant Pathology Journal
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• v.24 no.2
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• pp.118-124
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• 2008

Skin sooty dapple disease, a fungal disease that lowers Asian pear fruit quality, has emerged recently in Korea but has not yet been thoroughly characterized. This disease affects the surface of fruit, leaves, and young shoots of the Asian pear, typically appearing as a dark or pale black dapple on the fruit surface. The disease initiates on the fruit with small circular lesions that become bigger, eventually spreading to form large circular or indefinite lesions. Sparse dark or flourishing white-greyish aerial mycelia and appearance of a dark or pale black dapple on the fruit surface are typical signs of this disease. The disease was severe during cold storage of the Niitaka and Chuhwangbae varieties, but more limited on the Gamcheonbae and Hwangkeumbae varieties. To identify causal pathogens, 123 fungal isolates were obtained from lesions. The fungi that caused typical skin sooty dapple disease symptoms in our bioassay were identified. Based on their morphological characteristics, 74% of the isolates were Cladosporium sp. and 5-7 % of the isolates were Leptosphaerulina sp., Tripospermum sp., or Tilletiopsis sp. None of the isolates caused severe soft rot by injection to a wound plug, but some of the Cladosporium sp. isolates caused mild maceration. Therefore this microbiol complex cannot account for the soft rot also observed in stored fruits. The high frequency of isolation of Cladosporium sp. from disease tissues and bioassay on pear fruit surface suggest that Cladosporium sp. could be a major pathogen in the microbial complex associated with skin sooty dapple disease of the Asian pear in Korea.

• Korean Journal of Veterinary Service
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• v.42 no.1
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• pp.39-42
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• 2019

Tetanus is an acute, often fatal, and infectious disease of all species of domestic animals caused by the neurotoxin of Clostridium tetani (C. tetani). This disease is usually known to develop after microbial contamination in the deep or penetrating wound sites. In February 2017, a farmer who was raising 76 cows injected foot and mouth disease vaccine to three or more cows with one syringe. Their clinical symptoms were observed 2 to 16 days after the vaccination. The initial symptoms were stiffness, rigidity of the neck and limbs, pricked ears, and prolapse of the third eyelid. Subsequently, there was recumbency with extension of the limbs, convulsions and opistotonus and the affected 20 cows were all died. Two dead cows were submitted to Animal and Plant Quarantine Agency for disease diagnosis. At necropsy, a focal edematous abscess of 15 to 20 cm in diameter was grossly observed in the subcutaneous and intramuscular tissue of scapular region and filled with a large amount of greenish pus. The feed was full in oral cavity and slightly observed in the trachea and lobes of lung. Histopathologically, focal granulomatous nodules with eosinophilic materials in the tissue were observed. In the lung, aspiration pneumonia and gram (+) bacteria were seen. The C. tetani was isolated in samples anaerobically cultured using reinforced clostridial medium and identified by PCR. To our knowledge, no previous outbreak of tetanus in cattle has affected such a high number of animals; neither has it been associated with misuse of the same syringe and needle to administer multiple individuals.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• Korean Journal of Agricultural Science
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• v.40 no.2
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• pp.93-99
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• 2013

The disease and insect were surveyed locally in greenhouse, fruit packing house and store house of 51 farms in 13 towns having purpose of paprika exportation. By analysis, various disease and insect were not only founded locally but more ones detected in farms having old facilities and no natural enemy. We found 15 pathogens such as Fusarium spp., Alternaria solani, Leveilluila taurica, PepMV (Pepino mosaic virus) and TMV (Tobacco mosaic virus) in greenhouse, Fusarium spp. in fruit packing house and Penicillium spp. in store house. We found 15 insects in greenhouse such as Bemisia tabaci, rialeurodes vaporariorum and Myzus persicae in greenhouse, Hylobitelus haroldi in fruit packing house. However, the problem quarantine disease and insect for importation and exportation were not detected in inspection time.

• The Plant Pathology Journal
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• v.21 no.2
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• pp.99-101
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• 2005

Host cell death occurs during many, but not all, interactions between plants and the pathogens that infect them. This cell death can be associated with disease resistance or susceptibility, depending on the nature of the pathogen. The most well-known cell death response in plants is the hypersensitive response (HR) associated with a resistance response. HR is commonly regulated by direct or indirect interactions between avirulence proteins from pathogen and resistance proteins from plant and it can be the result of multiple signaling pathways. Ion fluxes and the generation of reactive oxygen species commonly precede cell death, but a direct involvement of the latter seems to vary with the plant-pathogen combination. Exciting advances have been made in the identification of cellular protective components and cell death suppressors that might operate in HR. In this review, recent progress in the mechanisms by which plant programmed cell death (PCD) occurs during disease resistance will be discussed.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.233-238
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• 2017

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.

• Plant Disease and Agriculture
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• v.5 no.2
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• pp.111-114
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• 1999

A bacterial disease of rice plant that rotted the sheath to brown was found in rice plants at Tanbuk Uisong Kyungbuk in June 1999, When the bacterial isolates from the diseased rice plants were inoculated to health plant by the artificial needle prick method the same symptoms were examined. According to its characteristics and pathogenicity on the his plant the causal bacterium was identified as Burkholderia glumae which is known as the pathogen of bacterial grain rot of rice.

• Korean Journal of Veterinary Research
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• v.55 no.1
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• pp.9-12
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• 2015

Brachyspira (B.) hyodysenteriae is a causative agent of swine dysentery that is responsible for death and economic losses in the pig industry. It is imperative that clinical samples be delivered fresh for accurate diagnosis. The viability and DNA detection of B. hyodysenteriae using lab-made (phosphate buffered saline and modified tryptic soy broth) or commercial transport media (C, D, and E) were compared by culturing and real-time PCR at $4^{\circ}C$ or room temperature (RT), respectively. B. hyodysenteriae grown in D (Anaerobe Systems, USA) and E (Starplex Scientific, Canada) media was viable for 4 days at $4^{\circ}C$ and RT. However, B. hyodysenteriae in A, B, and C (culture swab; BD Biosciences, USA) media were not recovered after 2 days at RT. Ct values for real-time PCR at $4^{\circ}C$ and RT ranged from $27.2{\pm}2.1$ (C) to $29.6{\pm}0.5$ (B), and $28.0{\pm}0.9$ (E) to $30.2{\pm}1.5$ (B), respectively. Considering the field conditions, it is important that transport media is used for specimen isolation and PCR to obtain an accurate diagnosis of swine dysentery.

• The Plant Pathology Journal
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• v.36 no.1
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• pp.54-66
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• 2020

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

• Journal of Plant Biology
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• v.3 no.1
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• pp.32-38
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• 1960

Since the peculiar virus disease of chinese date tree (Zizyphus jujuba Mill. var. inermis Rehd.) has been noted in South Korea around 1950, 70% to 80% of the economically important trees have been either completely destroyed or infected with the virus, severe damage has been noted, particularly, across the area ranged from middle east to the middle part of Korea, including Seoul area. Yoon-Koock-Byung in 1958 first reported the disease and descirbed it might be caused by a kinds of yellows. But he did not conform in his paper that the disease is pecisely caused by yellows virus. The authors, hereby intend to identify the true cause of the desease of the chinese data tree by studying the external symptoms of the disease and the internal morphological characteristics of the diseaset plant which shows various abnormalities in contrast to the healthy checks. In view of fact that leaves of the infected plants become yellowish in color similar to the peach yellows, aster yellows, it is likely to be identifiable as the common yellows. Furthermore, the abnormal characteristics observed by the authors are as follow: The floral organs such as petals, sepals, stamens, and pistil turn into vegetative leaves, the leaves on heavily infected plant appear as small sized one and also showing as a common witch's broom like symptom. There are also an occuring of numerous advantitious shoots developed from both of stems and roots. The amount of photosynthetic starch grains increases in parenchymatous cells, necrosis takes place in mesophyll, Particularly, Palisade Parenchyma in the leaves of infected plants are distinguished in contrast to the healthy checks. From the symptoms and the present experimetns described above, the authors are believed that the disease of chinese data tree is not caused by the yellows. It appears the disease is rather similar to the symptoms of sandal spike virus which was noted in India early in this centry. But the host plant of standal disease, Santalum albun L. and the insect vector, Jassus indicus Wal., have never been reported in Korean flora and the founa. The termperature and the otehr environmental factors is quite different Korea and India. Thus the authors believe that the peculiar disease must be an endemic new virus origin in Korea and must be called as "shoot cluster disease of chinese date tree."