• Research in Plant Disease
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• v.18 no.4
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• pp.361-369
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• 2012

A population density model for bacterial wilt, which is caused by Ralstonia solanacearum, in hot pepper was developed to estimate the primary infection date after overwintering in the field. We developed the model mechansitically to predict reproduction of the pathogen and pathogensis on seedlings of the host. The model estimates the pathogen's populations both in the soil and in the host. In order to quantify environmental infection factors, various temperatures and initial population densities were determined for wilt symptoms on the seedlings of hot pepper in a chamber. Once, the pathogens living in soil multiply up to 400 cells/g of soil, they can infect successfully in the host. Primary infection in a host was supposed to be started when the population of the pathogen were over $10^9$ cells/g of root tissue. The estimated primary infection dates of bacterial wilt in 2011 in Korea were mostly mid-July or late-July which were 10-15 days earlier than those in 2010. Two kinds of meterological data, synoptic observation and field measurements from paddy field and orchard in Kyunggi, were operated the model for comparing the result dates. About 1-3 days were earlier from field data than from synoptic observation.

• Research in Plant Disease
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• v.28 no.3
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• pp.143-151
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• 2022

In order to investigate the cause of wilting symptoms in red pepper field of Korea, the frequency of occurrence of red peppers showing wilting symptoms was investigated in pepper cultivation fields in Goesan, Chungcheongbuk-do for 5 years from 2010 to 2014. There was a difference in the frequency of wilting symptoms depending on the year of investigation, but the frequency of occurrence increased as the investigation period passed from June and July to August. During this period, Ralstonia solanacearum causing the bacterial wilt was isolated at a rate four times higher than Phytophthora capsica causing the Phytophthora late blight. In wilted peppers collected in Goesan of Chungbuk and Andong of Gyeongbuk in 2013 and 2014, R. solanacearum and P. capsici were isolated from 20.3% and 3.8% of the total fields, respectively. In the year with a high rate of wilting symptoms, the average temperature was high, and the disease occurrence date of the bacterial wilt, estimated with disease forecasting model, was also fast. The inconsistency between the number of days at risk of Phytophthora late blight and the frequency of occurrence of wither symptoms is thought to be due to the generalization of the use of cultivars resistant to the Phytophthora late blight in the pepper field. In our study, the wilting symptoms were caused by the bacterial wilt caused by R. solanacearum rather than the Phytophthora late blight caused by P. capsica, which is possibly caused by increasing cultivation of pepper varieties resistant to the Phytophthora late blight in the field.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.139-148
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• 2020

Infectious diseases have long plagued mankind, and predicting and preventing them has been a big challenge for mankind. For this reasen, various studies have been conducted so far to predict infectious diseases. Most of the early studies relied on epidemiological data from the Centers for Disease Control and Prevention (CDC), and the problem was that the data provided by the CDC was updated only once a week, making it difficult to predict the number of real-time disease outbreaks. However, with the emergence of various Internet media due to the recent development of IT technology, studies have been conducted to predict the occurrence of infectious diseases through web data, and most of the studies we have researched have been using single Web data to predict diseases. However, disease forecasting through a single Web data has the disadvantage of having difficulty collecting large amounts of learning data and making accurate predictions through models for recent outbreaks such as "COVID-19". Thus, we would like to demonstrate through experiments that models that use multiple Web data to predict the occurrence of infectious diseases through LSTM models are more accurate than those that use single Web data and suggest models suitable for predicting infectious diseases. In this experiment, we predicted the occurrence of "Malaria" and "Epidemic-parotitis" using a single web data model and the model we propose. A total of 104 weeks of NEWS, SNS, and search query data were collected, of which 75 weeks were used as learning data and 29 weeks were used as verification data. In the experiment we predicted verification data using our proposed model and single web data, Pearson correlation coefficient for the predicted results of our proposed model showed the highest similarity at 0.94, 0.86, and RMSE was also the lowest at 0.19, 0.07.

• Research in Plant Disease
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• v.10 no.3
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• pp.167-174
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• 2004

To make the forecasting model of rice bacterial grain rot (RGBR) using the statistical procedures with SAS(Statistical Analysis System) based on micro-weather factors during heading period of rice, 21 rice varieties having the different heading time (40％ panicles headed) were planted at 30 May and 15 June in Naju. Heading time and diseased panicles were investigated from July to August in 1998. RGBR mainly occurred on varieties headed from 29 July to 19 August, but not on varieties headed after 22 August. RGBR was highly correlated with diurnal temperature during 7 days (r =-0.871 **) and 10 days (r =-0.867**) and minimum relative humidity during 15 days from 3 days before heading time. After examining the models with several ways ($R^2$, Adjusted $R^2$, MSE), one equations were selected： Y =92.83 - 2.43Tavr + 1.88Tmin - 1.04RHavr + 0.37RHmin + 0.43RD - 3.68WS ($R^2$=0.824) using six variables of average and minimum temperature (Tavr and Tmin), average and minimum relative humidity (RHavr and RHmin), rainy days (RD), and wind speed (WS) during 7 days from 3 days before to 3 days after heading time.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.305-317
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• 2022

To investigate the blossom infection risk of fire blight on pears, the program Maryblyt has been executed from 2018 to 2022 based on meteorological data from central-Korean cities where fire blight has occurred as well as from southern Korean cities where the disease has not yet occurred. In the past five years, years with the highest risk of pear blossom blight were 2022 and 2019. To identify the optimal time for spraying, we studied the spray mode according to the Maryblyt model and recommend spraying streptomycin on the day after a "High" warning and then one day before forecasted precipitation during the blossom period. Maryblyt also recommends to initiate surgical controls from mid-May for canker blight symptoms on pear trees owing to over-wintering canker in Korea. Web-cam pictures from pear orchards at Cheonan, Icheon, Sangju, and Naju during the flowering period of pear trees were used for comparing real data and constructing a phenological model. The actual starting dates of flowering at southern cities such as Sangju and Naju were consistently earlier than those calculated by the model. It is thus necessary to improve the forecasting model to include field risks by recording the actual flowering period and the first day of the fire blight symptoms, according to the farmers, as well as mist or dew-fall, which are not easily identifiable from meteorological records.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.376-383
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• 2022

The current study, which consisted of two independent studies (laboratory and greenhouse), was carried out to project the hypothesis fungi-spray scheduling for leaf mold and gray leaf spot in tomato, as well as to evaluate the effect of temperature and leaf wet duration on the effectiveness of different fungicides against these diseases. In the first experiment, tomato leaves were infected with 1 × 10⁴ conidia·mL^-1 and put in a dew chamber for 0 to 18 hours at 10 to 25℃ (Fulvia fulva) and 10 to 30℃ (Stemphylium lycopersici). In farm study, tomato plants were treated for 240 hours with diluted (1,000 times) 30% trimidazole, 50% polyoxin B, and 40% iminoctadine tris (Belkut) for protection of leaf mold, and 10% etridiazole + 55% thiophanate-methyl (Gajiran), and 15% tribasic copper sulfate (Sebinna) for protection of gray leaf spot. In laboratory test, leaf condensation on the leaves of tomato plants were emerged after 9 hrs. of incubation. In conclusion, the incidence degree of leaf mold and gray leaf spot disease on tomato plants shows that it is very closely related to formation of leaf condensation, therefore the incidence of leaf mold was greater at 20 and 15℃, while 25 and 20℃ enhanced the incidence of gray leaf spot. The incidence of leaf mold and gray leaf spot developed 20 days after inoculation, and the latency period was estimated to be 14-15 days. Trihumin fungicide had the maximum effectiveness up to 168 hours of fungicides at 12 hours of wet duration in leaf mold, whereas Gajiran fungicide had the highest control (93%) against gray leaf spot up to 144 hours. All the chemicals showed an around 30-50% decrease in effectiveness after 240 hours of treatment. The model predictions in present study could be help in timely, effective and ecofriendly management of leaf mold disease in tomato.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.63-75
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• 2023

Due to the development of industry, interest in air pollutants has increased. Air pollutants have affected various fields such as environmental pollution and global warming. Among them, environmental diseases are one of the fields affected by air pollutants. Air pollutants can affect the human body's skin or respiratory tract due to their small molecular size. As a result, various studies on air pollutants and environmental diseases have been conducted. Asthma, part of an environmental disease, can be life-threatening if symptoms worsen and cause asthma attacks, and in the case of adult asthma, it is difficult to cure once it occurs. Factors that worsen asthma include particulate matter and air pollution. Asthma is an increasing prevalence worldwide. In this paper, we study how air pollutants correlate with the number of emergency room admissions in asthma patients and predict the number of future asthma emergency patients using highly correlated air pollutants. Air pollutants used concentrations of five pollutants: sulfur dioxide(SO₂), carbon monoxide(CO), ozone(O₃), nitrogen dioxide(NO₂), and fine dust(PM₁₀), and environmental diseases used data on the number of hospitalizations of asthma patients in the emergency room. Data on the number of emergency patients of air pollutants and asthma were used for a total of 5 years from January 1, 2013 to December 31, 2017. The model made predictions using two models, Informer and LTSF-Linear, and performance indicators of MAE, MAPE, and RMSE were used to measure the performance of the model. The results were compared by making predictions for both cases including and not including the number of emergency patients. This paper presents air pollutants that improve the model's performance in predicting the number of asthma emergency patients using Informer and LTSF-Linear models.