• The Plant Pathology Journal
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• v.37 no.2
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• pp.200-203
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• 2021

Rice blast caused by the filamentous fungus Magnaporthe oryzae, is arguably the most devastating rice disease worldwide. Development of a high-throughput and reliable field blast resistance evaluation system is essential for resistant germplasm screening, resistance genes identification and resistant varieties breeding. However, the occurrence of rice blast in paddy field is easily affected by various factors, particularly lack of sufficient inoculum, which always leads to the non-uniform occurrence and reduced disease severity. Here, we described a procedure for adequately inducing the occurrence of rice seedling blast in paddy field, which involves pretreatment of diseased straw, initiation of seedling blast for the first batch of spreader population, inducing the occurrence of the second batch of spreader population and test materials. This procedure enables uniform and consistent infection, which facilitates efficient and accurate assessment of seedling blast resistance for diverse rice materials.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.50-57
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• 2020

The work that detection and elimination to disease and pest have important in agricultural field because it is directly related to the production of the crops, early detection and treatment of the disease insects. Image classification technology based on traditional computer vision have not been applied in part such as disease and pest because that is falling a accuracy to extraction and classification of feature. In this paper, we proposed model that determine to disease and pest of shiitake based on deep-CNN which have high image recognition performance than exist study. For performance evaluation, we compare evaluation with Alexnet to a proposed deep learning evaluation model. We were compared a proposed model with test data and extend test data. The result, we were confirmed that the proposed model had high performance than Alexnet which approximately 48% and 72% such as test data, approximately 62% and 81% such as extend test data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.440-446
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• 2019

Pine wilt disease occurred for the first time in Busan in 1988 and the damage has since been increasing. In 2005, a special law was enacted for pine wilt disease by Korea Forest Service. Incidences relating to the forest pest had been frequent and chemical control as well as physical control techniques had been applied to control it. Therefore, there is a need to reduce the damage caused by the pine wilt disease through intensive management such as continuous monitoring, control, and monitoring based on active control as well as management measures. In this study, the UAV-based monitoring method was proposed as an economical way of monitoring the forest pest. The efficiency of the existing method and UAV method had been analyzed, and as a result the study suggested that UAV can be used for forest pest monitoring and indeed improve efficiency. The UAV-based forest pest monitoring method has a cost reduction of about 50% compared with the conventional method and will also help to reduce the area where the survey was omitted.

• Korean Journal of Environmental Agriculture
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• v.33 no.1
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• pp.52-56
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• 2014

BACKGROUND: It is known that impacts of climate change on damage occurrence by insect pests and diseases are increasing. The negative effects of climate change on production will threaten our food security. It is needed that on the basis of analysis of the impacts, proper strategies in response to climate change are developed. METHODS AND RESULTS: The objective of this paper is to estimate impacts of climate change on rice damage occurrence by insect pests and diseases, using the panal model which analyzes both cross-section data and time series data. The result of an analysis on impacts of climate change on rice damage occurrence by pest insect and disease showed that the damage occurrence by Rice leaf roller and Rice water weevil increased if temperature increased, and damage occurrence by Stripe, Sheath blight, and Leaf Blast increased if precipitation(or amount of sunshine) increased(or decreased). CONCLUSION: Adaptation strategies, supplying weather forecasting information by region, developing systematical strategies for prevention of damage occurrence by pest insect and disease, analyzing the factors of damage occurrence by unexpected pest insect and disease, enforcing international cooperation for prevention of damage occurrence are needed to minimize the impacts of damage occurrence on rice production.

• Proceedings of the Korean Society of Crop Science Conference
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• 1998.10a
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• pp.415-445
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• 1998

For effective prevention of the spreading and outbreak of crop insects and disease pests, an intensive Pest surveillance system was established to predict their density changes, and distribution. After their initial establishment by either immigration or overwintering, it is necessary to anticipate how they spread out geographically and predict where/when outbreaks are possible. The two major tools, boundary layer atmospheric model (Blayer) and the geographic information system(GIS), have been being developed to facilitate the prediction of pest occurrence in recent days. We are also developing the PeMos (Pest Monitoring System) that is able to manage the pest surveillance data collected from 152 pest monitoring stations in Korea. These three system related to the pest surveillance should be integrated into an internet based comprehensive database management system to facilitate information resources systematically organized and closely linked. Considering various data types and large data size in each system, a new special information management system is suggested. The integrated system should express complex types of information, such as text, multimedia, and other scientific data under the Internet environment. This paper discussed the major three systems, GIS, Blayer, and PeMos, relevant to the crop pest surveillance, then how they can be integrated in a comprehensive system under the Internet environment.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.1
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• pp.133-143
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• 2018

In spite of the growing importance of landscaping trees, the rate of flawed and withered trees damaged by pest, disease, drought or frost is increasing. In order to evaluate the performance of the Tree Protective Pad, which are developed to reduce the failure ratio in landscape planting, the tree protective pad for 'digging', 'pest controlling', and 'insulating' are tested based on the five functional criteria; moisturizing effect, wither preventive effect, pest and disease control, thermal effect, tensile strength, and environmental performance. The result of this study is as follows. The moisturizing effect of the tree protective pad for digging is found to be outstanding. According to the result of testing the pad on trees, in particular, it is better than jute tape in wither preventive effect, which means it is expected to prevent flaw and wilt from planting during the improper seasons like summertime. The experiment of installing the protective tree pad for pest controlling to the trunk of Quercus mongolica shows that preventive effect of the pad from diseases and insects is superior, and it also has economical effect by reducing the use of agricultural chemicals. The comparative test of the pad for insulating and jute tape proves that the temperature of the pad is about $2^{\circ}C$ higher than outside. The rate of tensile strength and biodegradation of the pad exceeds the optimal level, so it is revealed that the pad may be the work efficient and environment-friendly product. Likewise, by timely irrigating trees, the tree protective pad economically prevents trees from pest, disease,drought or frost, which may be caused by improper seasonal or delayed planting. As a means of reducing the flaw and facilitating the growth of trees, the exceptional performance of the pad is expected to effectively used in landscape planting and management.

• The Plant Pathology Journal
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• v.35 no.5
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• pp.521-529
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• 2019

Tomato yellow leaf curl China virus is a species of the widespread geminiviruses. The infection of Nicotiana benthamiana by Tomato yellow leaf curl China virus (TYLCCNV) causes a reduction in photosynthetic activity, which is part of the viral symptoms. ${\beta}C1$ is a viral factor encoded by the betasatellite DNA ($DNA{\beta}$) accompanying TYLCCNV. It is a major viral pathogenicity factor of TYLCCNV. To elucidate the effect of ${\beta}C1$ on plants' photosynthesis, we measured the relative chlorophyll (Chl) content and Chl fluorescence in TY-LCCNV-infected and ${\beta}C1$ transgenic N. benthamiana plants. The results showed that Chl content is reduced in TYLCCNV A-infected, TYLCCNV A plus $DNA{\beta}$ (TYLCCNV A + ${\beta}$)-infected and ${\beta}C1$ transgenic plants. Further, changes in Chl fluorescence parameters, such as electron transport rate, $F_v/F_m$, NPQ, and qP, revealed that photosynthetic efficiency is compromised in the aforementioned N. benthamiana plants. The presense of ${\beta}C1$ aggravated the decrease of Chl content and photosynthetic efficiency during viral infection. Additionally, the real-time quantitative PCR analysis of oxygen evolving complex genes in photosystem II, such as PsbO, PsbP, PsbQ, and PsbR, showed a significant reduction of the relative expression of these genes at the late stage of TYLCCNV A + ${\beta}$ infection and at the vegetative stage of ${\beta}C1$ transgenic N. benthamiana plants. In summary, this study revealed the pathogenicity of TYLCCNV in photosynthesis and disclosed the effect of ${\beta}C1$ in exacerbating the damage in photosynthesis efficiency by TYLCCNV infection.

• International Journal of Industrial Entomology and Biomaterials
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• v.25 no.2
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• pp.163-169
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• 2012

A decrease in pass ratio of Korean exporting pears causes several negative effects including an increase in pesticide dependency. In this study, we attempted to establish the pest forecasting management system, composed of weekly field forecasting by pear farmers, meteorological data obtained by automatic weather station (AWS), newly designed internet web page ($\underline{http://pearpest.jnu.ac.kr/}$) as information collecting and providing ground, and information providing service. The weekly field forecasting information on major pear diseases and pests was collected from the forecasting team composed of five team leaders from each pear exporting complex. Further, an abridged weather information for the prediction of an infestation of major disease (pear scab) and pest (pear psylla and scale species) was obtained from an AWS installed at Bonghwang in Naju City. Such information was then promptly uploaded on the web page and also publicized to the pear famers specializing in export. We hope this pest forecasting management system increases the pass ratio of Korean exporting pears throughout establishment of famer-oriented forecasting, inspiring famers' effort for the prevention and forecasting of diseases and pests occurring at pear orchards.

• Korean Journal of Medicinal Crop Science
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• v.16 no.1
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• pp.33-38
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• 2008

This study was carried out to reduce the number of chemical treatment by optimal apply for the disease and pest and obtain the basal data of environmental-friendly cultivation in Panax ginseng in 2006. The result by checking disease Incidence and pests fried in ginseng field of jeollabuk-Do was as follows. The kind of disease occurred in Jinan was 8 including Rhizoctonia solani, 7 with Botrytis cinerea in Jeongeup, and also 6 with Botrytis cinerea in Kochang within Jeollabuk-Do. It was required thorough disease control before the rainy season because the occurrence time was peaked around July. Also, the most serious disease in Jeollabuk-Do was Alternaria alternata, Botrytis cinerera, and Colletotrichum gloeosporioides. The actual harmful pests in ginseng field were Asusta despecta steboldiana and Holotrichia sp. and in other method using black light trap, Maladera orientalis, Ostrinia furnacalis, and Holotrichia morosa were mainly trapped.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.133-143
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• 2023

In this study, we propose a big data set of strawberry pests collected directly for diagnosis model learning and an automatic pest diagnosis model architecture based on deep learning. First, a big data set related to strawberry pests, which did not exist anywhere before, was directly collected from the web. A total of more than 12,000 image data was directly collected and classified, and this data was used to train a deep learning model. Second, the deep-learning-based automatic pest diagnosis module is a module that classifies what kind of pest or disease corresponds to when a user inputs a desired picture. In particular, we propose a model architecture that can optimally classify pests based on a convolutional neural network among deep learning models. Through this, farmers can easily identify diseases and pests without professional knowledge, and can respond quickly accordingly.