• Korean Journal of Agricultural Science
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• v.41 no.1
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• pp.23-27
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• 2014

Potato late blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, economic threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of potato late blight.

• Korean Journal of Agricultural Science
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• v.41 no.1
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• pp.29-34
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• 2014

Phytophthora blight occurrence is caused by various environmental factors, and the progress can be regularly predicted so that several predictive models have been developed. The models predict the timing of the disease occurrence, but they do not include the methods of the disease control. Effective fungicide control, control threshold, prediction models were investigated in the study to reflect on customized control modules for the model forecasting the occurrence of Phytophthora blight on hot pepper.

• Korean Journal Plant Pathology
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• v.12 no.1
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• pp.121-128
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• 1996

농업 기상 자료의 수집 및 식물병 예찰 절차를 통합한 시스템을 -32비트 개인용 컴퓨터 운영 체계인 OS/2에서 개발하였다. 통합 시스템은 무인기상관측기(AWS)로부터 자료 수집을 하는 절차, 준실시간 기상자료로부터 병예찰을 하는 절차, 기상 정보와 병예찰 정보를 글과 그림으로 출력하는 절차의 세 부분으로 나뉘어 있다. 통합 시스템은 여러 지역의 실시간 기상 자료를 수집하며 기상 자료를 이용하여 각 지역의 병예찰 정보를 즉시 생성한다. 본 연구에서는 기상 자료를 이용한 병예찰 모형의 예로서 도열병 예찰 시뮬레이션 모형을 사용하였다. 또한 식물병 예찰을 위하여 무인기상관측기가 갖추어져야 하는 최소한의 요구 사항을 검토하였다. 본 시스템은 각종 식물병 예찰 모형의 개발과 관련하여 각 모형의 구동을 위하여 쓰여질 수 있을 것이다. 현재 각 농촌진흥원과 지도소에는 많은 수의 무인기상관측기가 설치되어 있으므로 이를 이용하여 본 시스템을 실용화 할 수 있을 것이다.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.145-155
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• 2016

In this paper, we compare forecasting models for disease occurrences in elderly patients due to the weather. For the analysis, the medical data of aged patients released from Health Insurance Review and the weather data of the Korea Meteorological Administration are weekly and regionally merged. The ARMAX model, the VARMAX model and the TSCS regression model are considered to analyze the number of weekly occurrences of some diseases attributable to climate conditions. These models are compared with MSE, MAPE, and MAE criteria.

• The Plant Pathology Journal
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• v.17 no.6
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• pp.373.2-373
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• 2001

• Research in Plant Disease
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• v.21 no.2
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• pp.67-73
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• 2015

Bacterial blossom blight of kiwifruit (Actinidia deliciosa) caused by Pseudomonas syringae pv. syringae is known to be largely affected by weather conditions during the blooming period. While there have been many studies that investigated scientific relations between weather conditions and the epidemics of bacterial blossom blight of kiwifruit, no forecasting models have been developed thus far. In this study, we collected all the relevant information on the epidemiology of the blossom blight in relation to weather variables, and developed the Pss-KBB Risk Model that is based on the Maryblyt model for the fire blight of apple and pear. Subsequent model validation was conducted using 10 years of ground truth data from kiwifruit orchards in Haenam, Korea. As a result, it was shown that the Pss-KBB Risk Model resulted in better performance in estimating the disease severity compared with other two simple models using either temperature or precipitation information only. Overall, we concluded that by utilizing the Pss-KBB Risk Model and weather forecast information, potential infection risk of the bacterial blossom blight of kiwifruit can be accurately predicted, which will eventually lead kiwifruit growers to utilize the best practices related to spraying chemicals at the most effective time.

• Research in Plant Disease
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• v.7 no.1
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• pp.20-24
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• 2001

As stand-missing rate (SMR) of ginseng plants in fields are directly related to the ginseng root rot, the forecasting model for the root rot progress in ginseng fields was developed, using the estimated SMRs by disease incidence (DI) of ginseng seedling in the soil-indexing bioassay and the estimate of DI derived from soil environmental factors or rhizoplane microflora. For fitness analysis of the forecasting model, simple correlation and linear regression between SMRs at different planting ages in fields and their estimates by 3 factors of the model were evaluated.The SMR estimated from the factor of DI in the bioassay had much higher fitness to the SMR observed in fields than that from the factors of soil environments and rhizoplane microflora. The estimated SMRs in young and aged ginseng fields by DI in the bioassay were significantly correlated with the observed SMRs in 3- and 5-year-old ginseng fields, respectively (p=0.01). this implicates that indexing preplanting field soils with the forecasting model using DI in the bioassay can provide an information to determine the suitability of the fields for ginseng cultivation, and that indexing cultivating field soils can be helpful to determine the time of harvesting to reduce further yield loss by root rot in continuous cultivation in the next year.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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• pp.16-21
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• 2001

Regression models were obtained on the base of the correlation between Phytophthora blight incidence in red pepper and the microclimate data obtained from automated weather station (AWS) during 1997 and 1998. A computer program (PEPBLIGHT) was constructed based on the model that the R2 value is highest among regression models. This computer program uses the microclimate data from more than one AWS through the common dialogue box easy and it is able provide disease forecasting information. In addition, it could be applied far other diseases and converts the microclimate data of AWS to the input data for Statical Analysis System (SAS). PEPBLIGHT was first developed for the forecasting computer system of red pepper blight in Korea. PEPBLIGHT is operated on the MS Windows, so that it is easy to use.

• The Plant Pathology Journal
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• v.17 no.6
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• pp.368.4-369
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• 2001

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.