• Korean journal of applied entomology
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• v.12 no.3
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• pp.93-107
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• 1973

Garlic (Allium sativum L.) is an important vegetable crop for the Korean people and has long been cultivated extensively in Korea. More recently it has gained importance as a source of certain pharmaceuticals. This additional use has also contributed to the increasing demand for Korean garlic. Garlic has been propagated vegetatively for a long time without control measures against virus diseases. As a result it is presumed that most of the garlic varieties in Korea may have degenerated. The production of virus-free plants offers the most feasible way to control the virus diseases of garlic. However, little is known about garlic viruses both domestically and in foreign countries. More basic information regarding garlic viruses is needed before a sound approach to the control of these diseases can be developed. Currently garlic mosaic disease is most prevalent in plantings throughout Korea and is considered to be the most important disease of garlic in Korea. Because of this importance, studies were initiated to isolate and characterize the garlic mosaic virus. Symptom expression in test plants, physical properties, purification, serological reaction and morphological characteristics of the garlic mosaic virus were determined. Results of these studies are summarized as follows. 1. Surveys made throughout the important garlic growing areas in Korea during 1970-1972 revealed that most of the garlic plants were heavily infected with mosaic disease. 2. A strain of garlic mosaic virus was obtained from infected garlic leaves and transmitted mechanically to Chenopodium amaranticolor by single lesion isolation technique. 3. The symptom expression of this garlic mosaic virus isolate was examined on 26 species of test plants. Among these, Chenopodium amaranticolor, C. quince, C. album and C. koreanse expressed chlorotic local lesions on inoculated leaves 11-12 days after mechanical inoculation with infective sap. The remaining 22 species showed no symptoms and no virus was recovered from them whet back-inoculated to C. amaranticolor. 4. Among the four species of Chtnopodium mentioned above, C. amaranticolor and C. quinoa appear to be the most suitable local lesion test plants for garlic mosaic virus. 5. Cloves and top·sets originating from mosaic infected garlic plants were $100\%$ infected with the same virus. Consequently the garlic mosaic virus is successively transmitted through infected cloves and top-sets. 6. Garlic mosaic virus was mechanically transmitted to C, amaranticolor when inoculations were made with infective sap of cloves and top-sets. 7. Physical properties of the garlic mosaic virus as determined by inoculation onto C. amaranticolor were as follows. Thermal inactivation point: $65-70^{\circ}C$, Dilution end poiut: $10^-2-10^-3$, Aging in vitro: 2 days. 8. Electron microscopic examination of the garlic mosaic virus revealed long rod shaped particles measuring 1200-1250mu. 9. Garlic mosaic virus was purified from leaf materials of C. amaranticolor by using two cycles of differential centrifugation followed by Sephadex gel filtration. 10. Garlic mosaic virus was successfully detected from infected garlic cloves and top-sets by a serological microprecipitin test. 11 Serological tests of 150 garlic cloves and 30 top-sets collected randomly from seperated plants throughout five different garlic growing regions in Korea revealed $100\%$ infection with garlic mosaic virus. Accordingly it is concluded that most of the garlic cloves and top-sets now being used for propagation in Korea are carriers of the garlic mosaic virus. 12. Serological studies revealed that the garlic mosaic virus is not related with potato viruses X, Y, S and M. 13. Because of the difficulty in securing mosaic virus-free garlic plants, direct inoculation with isolated virus to the garlic plants was not accomplished. Results of the present study, however, indicate that the virus isolate used here is the causal virus of the garlic mosaic disease in Korea.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.