• The Plant Pathology Journal
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• 제38권1호
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• pp.46-51
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• 2022

Rice blast is the most destructive disease threatening stable rice production in rice-growing areas. Cultivation of disease-resistant rice cultivars is the most effective way to control rice blast disease. However, the rice blast resistance is easy to breakdown within years by blast fungus that continually changes to adapt to new cultivars. Therefore, it is important to continuously monitor the incidence of rice blast disease and race differentiation of rice blast fungus in fields. In 2020, a severe rice blast disease occurred nationwide in Korea. We evaluated the incidence of rice blast disease in Yeoju and compared the weather conditions at the periods of rice blast disease in 2019 and 2020. We investigated the races and avirulence genes of rice blast isolates in Yeoju to identify race diversity and genetic characteristics of the isolates. This study will provide empirical support for rice blast control and the breeding of blast-resistant rice cultivars.

• The Plant Pathology Journal
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• 제37권2호
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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.

• 한국농림기상학회지
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• 제25권4호
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• pp.245-257
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• 2023

우리나라 대표적인 식량 작물인 벼는 넓은 면적에서 장기간 재배되어 병해충에 대한 저항성이 낮아지고 있다. 대표적인 벼 질병 중 하나인 도열병이 대규모로 발생하면 수확량이 감소하여 조기에 병을 발견하고 방제를 하여야 한다. 하지만 농지 중심에서 도열병이 발생 하면 육안으로 병을 발견하기 어렵다. 드론을 이용한 작물 모니터링 기법은 생육 이상을 탐지하는데 유용하나 언제 발생할지 모르는 도열병 발생을 위해 빈번하게 촬영을 하는 것은 많은 인력과 비용이 소모된다. 본 연구의 목적은 드론, 위성과 같은 원격탐사 자료와 기상자료를 공동 활용하여 벼 도열병을 조기에 탐지하는 것이다. 위성영상은 벼 재배 필지를 추출하는데 유용하였다. 식생지수와 수분지수의 특성을 이용하여 관개가 된 논을 효과적으로 탐지하였다. 이후 기온, 상대습도, 강우일수 자료를 활용하여 벼 도열병 발생위험도를 계산하였다. 도열병 발생위험도가 증가하면 병이 발생할 가능성이 높아진다는 것을 의미하며, 해당 시점에 드론 관측을 수행하였다. 병이 발생한 지점, 병이 발생한 지점과 일반 벼가 혼재되어 있는 지점, 일반 벼 지점에서 분광반사도의 변화를 살펴보았으며, 갈색 병반을 가지는 도열병 특성상 적색과 근적외선 파장대에서 분광반사도의 변화가 급격한 것을 확인하였다. 도열병이 발생한 지점에서는 주변 지점에 비해 식생지수 값이 낮은 군집이 나타났으며, 시계열 드론 영상을 통해 해당 지점으로부터 벼 도열병 확산을 추적하였다. 최종적으로 수확 전 드론 영상을 활용하여 필지별 도열병 발생률에 대한 공간정보를 생산하였다.

• 한국식물병리학회지
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• 제10권1호
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• pp.47-53
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• 1994

Incidences of blast caused by Pyricularia grisea Sacc. on 24 leading cultivars and elite lines of rice were investigated in the fields at Icheon, chuncheon, Jecheon, Naju from 1990 to 1992. In the blast nursery, disease index of leaf blast on Jinmibyeo and Ilpumbyeo were very low as 1 to 3 at Naju, but as high as 6 to 9 at Icheon and other Chuncheon in 1990, but the disease did not occur in other locations and years. The most variable incidence of temporal and spatial leaf blast was observed on Nagdongbyeo, which was 30.6% at Icheon and 2.1% at Chuncheon on 1990, but the disease did not occur at Naju during the investigation. Percentages of diseased panicles on Chucheongbyeo were 11.6% in 1990 and 4.3% in 1992 at Icheon. Odaebyeo and Sobaekbyeo revealed more severe blast occurrences at Chuncheon and Sangju where the elevation was higher than the other places. Regional race distributions of rice blast fungus were more variable at Icheon and Chuncheon than the others.

• The Plant Pathology Journal
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• 제18권1호
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• pp.43-49
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• 2002

Direct effects of atmospheric ozone on conidia of the rice blast pathogen, Magnaporthe grisea, were investigated to evaluate ozone-induced effects on infection potential of the rice blast fungus. Acute ozone exposure (200 nl $1^{-1}$, 8 h $day^{-1}$3 days) during sporulation significantly affected conidial morphology, appressorium formation, and disease development on rice loaves. Ozone caused reduction in conidial size and change in conidial shape. Relative cytoplasmic volume of lipids and vacuoles were increased in ozone-exposed conidia. Inhibition of appressorium formation and simultaneous increase in endogenous levee of polyamines were found in ozone-exposed conidia. The inverse relationship between appressorium formation and level of polyamines implies that ozone-mediated increase in intracellular level of polyamines may inhibit appressorium formation in rice blast fungus. Furthermore, rice plants inoculated with ozone-fumigated conidia exhibited less severe disease development than those with unfumigated conidia. This result suggests that the anti-conidial consequence of acute ozone will eventually weaken the rice blasts potential for multiple infection cycle. This further suggests that consequently, rice blast can be transformed from an explosive disease to one that has limited epidemiological potential in the field.

• 한국기후변화학회지
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• 제9권2호
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• pp.133-142
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• 2018

Climate change will affect not only the crop productivity but also the pattern of rice disease epidemics in Korea. Impact assessments for the climate change are conducted using various climate change scenarios from many global climate models (GCM), such as a scenario from a best GCM or scenarios from multiple GCMs, or a combination of both. Here, we evaluated the feasibility of using a climate change scenario from the best GCM for the impact assessment on the potential epidemics of a rice leaf blast disease in Korea, in comparison to a multi?model ensemble (MME) scenario from multiple GCMs. For this, this study involves analyses of disease simulation using an epidemiological model, EPIRICE?LB, which was validated for Korean rice paddy fields. We then assessed likely changes in disease epidemics using the best GCM selected for individual agro?climatic zones and MME scenarios constructed by running 11 GCMs. As a result, the simulated incidence of leaf blast epidemics gradually decreased over the future periods both from the best GCM and MME. The results from this study emphasized that the best GCM selection approach resulted in comparable performance to the MME approach for the climate change impact assessment on rice leaf blast epidemic in Korea.

• The Plant Pathology Journal
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• 제38권4호
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• pp.395-402
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• 2022

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory, with diverse input datasets, and compares their performance. The Blast_Weathe long short-term memory r_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.

• 한국식물병리학회지
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• 제13권2호
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• pp.79-84
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• 1997

Incidence of rice blast on new rice cultivars and elite lines was observed from 194 to 1996 in Icheon, Chuncheon, Jecheon and Naju areas. The observations were made in the nuseries and in the fields. In the nurseries, only cultivars Daesanbyeo and Hyangmibyeo 2 showed moderate levels of resistance to leaf blast, with the disease index 0 to 6. From the field observations, it was found that cultivars Hyangmibyeo 2 and Suwon 414 were highly resistant to leaf blast, but susceptible to neck blast. the fields, leaf blast was not observed. In general, there was great yearly and regional variation in the incidence of neck blast within the same cultivars, some times ranging from 0 to 100% of incidence. However, the range of fluctuation in the disease incidence were relatively small in the cultivars Daejinbyeo (0∼17.5%), Daesanbyeo (0∼4.0%), Donganbyeo (0∼21.4%) and Hwasambyeo (0∼13.9%). Hyangmibyeo 2 and Seojinbyeo were rarely infected with neck blast in Chuncheon and Naju all of the years, the same cultivars were severely infested with neck blast; 45.1 and 45.5%, respectively, in Jecheon in 1995. The occurrence of different races of rice blast fungus were different at different areas. However, it was found that in Icheon, Chuncheon, Jecheon and Naju areas, the dominant races were KI-409, KJ-201 and KJ-301.

• The Plant Pathology Journal
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• 제36권5호
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• pp.406-417
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• 2020

Early warning services for crop diseases are valuable when they provide timely forecasts that farmers can utilize to inform their disease management decisions. In South Korea, collaborative disease controls that utilize unmanned aerial vehicles are commonly performed for most rice paddies. However, such controls could benefit from seasonal disease early warnings with a lead time of a few months. As a first step to establish a seasonal disease early warning service using seasonal climate forecasts, we developed the EPIRICE Daily Risk Model for rice blast by extracting and modifying the core infection algorithms of the EPIRICE model. The daily risk scores generated by the EPIRICE Daily Risk Model were successfully converted into a realistic and measurable disease value through statistical analyses with 13 rice blast incidence datasets, and subsequently validated using the data from another rice blast experiment conducted in Icheon, South Korea, from 1974 to 2000. The sensitivity of the model to air temperature, relative humidity, and precipitation input variables was examined, and the relative humidity resulted in the most sensitive response from the model. Overall, our results indicate that the EPIRICE Daily Risk Model can be used to produce potential disease risk predictions for the seasonal disease early warning service.

• The Plant Pathology Journal
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• 제31권1호
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• pp.12-24
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• 2015

Rice Blast is the most devastating disease causing major yield losses in every year worldwide. It had been proved that using resistant rice varieties would be the most effective way to control this disease. Molecular screening and genetic diversities of major rice blast resistance genes were determined in 192 rice germplasm accessions using simple sequence repeat (SSR) markers. The genetic frequencies of the 10 major rice blast resistance genes varied from 19.79% to 54.69%. Seven accessions IC337593, IC346002, IC346004, IC346813, IC356117, IC356422 and IC383441 had maximum eight blast resistance gene, while FR13B, Hourakani, Kala Rata 1-24, Lemont, Brown Gora, IR87756-20-2-2-3, IC282418, IC356419, PKSLGR-1 and PKSLGR-39 had seven blast resistance genes. Twenty accessions possessed six genes, 36 accessions had five genes, 41 accessions had four genes, 38 accessions had three genes, 26 accessions had two genes, 13 accessions had single R gene and only one accession IC438644 does not possess any one blast resistant gene. Out of 192 accessions only 17 accessions harboured 7 to 8 blast resistance genes.