• The Plant Pathology Journal
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• 제21권4호
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• pp.406-408
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• 2005

Web blight symptoms were frequently observed on soybean plants grown in a farmer's fields located in Jincheon in Korea during a disease survey in August, 2005. Incidence of the disease was $5-20\%$ infected plants in two of four soybean fields investigated. A total of 31 isolates of Rhizoctonia sp. were obtained from leaves, leaf petioles, and pods of diseased soybean plants. The isolates were identified as Rhizoctonia solani AG-l(IA) by anastomosis test and based on the morphological and cultural characteristics. Three isolates of R. solani AG-l(IA) were tested for pathogenicity to five cultivars of soybean by artificial inoculation. All the isolates induced blight symptoms on the leaves of soybean and formed sclerotia on the lesions, which were similar to those observed in the field. The pathogenicity tests revealed that all the soybean cultivars tested were susceptible to the pathogen. There was no difference in the pathogenicity among the isolates. The present study first reveals that R. solani AG-l(IA) causes web blight of soybean in Korea.

• Mycobiology
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• 제43권2호
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• pp.170-173
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• 2015

Herein, we report the first occurrence of web blight of rosemary caused by Rhizoctonia solani AG-1-IB in Gangneung, Gangwon Province, Korea, in August 2014. The leaf tissues of infected rosemary plants were blighted and white mycelial growth was seen on the stems. The fungus was isolated from diseased leaf tissue and cultured on potato dextrose agar for identification. The young hyphae had acute angular branching near the distal septum of the multinucleate cells and mature hyphal branches formed at an approximately $90^{\circ}$ angle. This is morphologically identical to R. solani AG-1-IB, as per previous reports. rDNA-ITS sequences of the fungus were homologous to those of R. solani AG-1-IB isolates in the GenBank database with a similarity percentage of 99%, thereby confirming the identity of the causative agent of the disease. Pathogenicity of the fungus in rosemary plants was also confirmed by Koch's postulates.

• 한국식물병리학회:학술대회논문집
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• 한국식물병리학회 2002년도 총회 및 추계학술발표회
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• pp.100.1-100
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• 2002

• 한국농림기상학회지
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• 제24권4호
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• pp.305-317
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• 2022

배 화상병의 성공적 방제를 위해 2018년부터 2022년까지 우리나라 중부지방의 주요 발병지와 남부지방의 미발병 주산지 주요지점 25곳에 대한 Maryblyt를 구동하여 꽃감염 위험도를 조사하였다. 최근 5년 중 2019년과 2022년 개화기간 중 꽃감염 위험도가 가장 높았다. 한편, 개화기간 중 최적의 꽃감염 방제 처리는 High 경보 다음날에 방제하고, 강우예보를 발령한 전날 방제하는 처리가 배 꽃감염을 낮추는 것으로 평가하였다. 월동 궤양으로부터 활성화된 궤양이 병징을 보일 것으로 Maryblyt가 예측한 날은 대략 중부지방 기준 5월 중순이었는데 이때부터 현장에서 궤양 모니터링을 개시하도록 권장하였다. 천안, 이천, 상주, 나주 등 4곳의 배 과수원에 설치한 영상자료로부터 배 개화기간을 이론적으로 계산한 값과 실제 관측한 값의 차이점을 비교한 결과 남부지방은 이론치나 실측치보다 늘 빠르게 개화를 예측하므로 재조정이 필요하였다. 향후 현장 관리자와 농민들로부터 과원에서 관측한 기상, 기주인 과수, 병징 출현일 등의 정보들이 축적된다면 발병 예측 모델은 현재보다 더 정확한 정보를 제공할 수 있을 것으로 기대된다.

• 품질경영학회지
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• 제48권4호
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• pp.581-596
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• 2020

Purpose: purpose of this study was to propose the web application platform which can be to detect and discriminate various diseases and pest of tomato plant based on the large amount of disease image data observed in the facility or the open field. Methods: The deep learning algorithms uesed at the web applivation platform are consisted as the combining form of Faster R-CNN with the pre-trained convolution neural network (CNN) models such as SSD_mobilenet v1, Inception v2, Resnet50 and Resnet101 models. To evaluate the superiority of the newly proposed web application platform, we collected 850 images of four diseases such as Bacterial cankers, Late blight, Leaf miners, and Powdery mildew that occur the most frequent in tomato plants. Of these, 750 were used to learn the algorithm, and the remaining 100 images were used to evaluate the algorithm. Results: From the experiments, the deep learning algorithm combining Faster R-CNN with SSD_mobilnet v1, Inception v2, Resnet50, and Restnet101 showed detection accuracy of 31.0%, 87.7%, 84.4%, and 90.8% respectively. Finally, we constructed a web application platform that can detect and discriminate various tomato deseases using best deep learning algorithm. If farmers uploaded image captured by their digital cameras such as smart phone camera or DSLR (Digital Single Lens Reflex) camera, then they can receive an information for detection, identification and disease control about captured tomato disease through the proposed web application platform. Conclusion: Incheon Port needs to act actively paying.

• The Plant Pathology Journal
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• 제26권1호
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• pp.37-48
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• 2010

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of $240\;m{\times}240\;m$ based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

• 한국농림기상학회지
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• 제25권3호
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• pp.208-217
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• 2023

정확한 자발휴면 타파일 및 개화기간 추정은 사과 화상병의 효과적 방제를 위해 매우 중요하다. 자발휴면 타파일부터 개화 개시일까지 기간은 이 기간 동안의 일 최저기온에 의해 영향을 받았다. 본 연구는 이 기간의 일 최저기온이 사과 생육단계 중 개화기간에 미치는 영향을 조사함으로써 화상병 방제를 위한 병모델 구동이 목적이었다. 원예특작과학원에서 제공하는 우리나라 사과나무 재배지역을 대표하는 8개 과수원에서 2019년부터 2023년까지 웹캠으로 관측한 영상자료로부터 최초 개화 관측일을 얻었다. 또한 같은 과수원에서 자발휴면 타파일은 전년도 10월 1일부터 자동기상 측정 장비로부터 받은 기상자료를 활용하여 자발휴면 타파일은 -100.5 DD에 도달하는 날로 추정하였다. 본 연구에서 실시한 회귀분석은 자발휴면 타파일부터 개화 개시일까지의 기간(Y)을 종속변수로 이 기간 중 일 최저 기온이 0℃ 이하인 날(X₁)이 며칠 인지를 독립변수로 하는 회귀식으로서 Y = 0.87 × X₁ + 40.76, R²= 0.83의 결과로서 뚜렷한 양의 상관관계를 얻었다. 또한 같은 기간(Y)을 종속변수로 하고 자발휴면 타파일을 줄리안데이(X₂)를 독립변수로 하는 회귀분석을 실시하여 Y = -1.07 × X₂ + 143.62, R²=0.92의 결과로서 뚜렷한 부의 상관관계를 얻었다. 따라서 자발휴면 타파일부터 개화 개시일까지의 기간은 월동 중 최저기온에 영향을 받으며, 이것이 사과 화상병 감염에 중요한 개화기간 변동에 영향을 준다는 것을 확인하였다.

• 한국식물병리학회:학술대회논문집
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• 한국식물병리학회 2003년도 정기총회 및 추계학술발표회
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• pp.108.2-109
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• 2003

The Gyeonggi-do Agricultural Research and Extension Services has developed a web-site (www.epilove.com) in collaboration with EPINET to provide information on agricultural weather and rice disease and insect pest management in Gyeonggi-do. Weather information includes near real-time weather data monitored by automated weather stations (AWS) installed at rice paddy fields of 11 Agricultural Technology Centers (ATC) in Gyeonggi-do, and weekly weather forecast by Korea Meteorological Administration (KMA). Map images of hourly air temperature and rainfall are also generated at 309m x 309m resolution using hourly data obtained from AWS installed at 191 locations by KMA. Based on near real-time weather data from 11 ATC, hourly infection risks of rice blast, sheath blight, and bacterial grain rot for individual districts are estimated by disease forecasting models, BLAST, SHBLIGHT, and GRAINROT. Users can diagnose various diseases and insects of rice and find their information in detail by browsing thumbnail images of them. A database on agrochemicals is linked to the system for disease and insect diagnosis to help users search for appropriate agrochemicals to control diseases and insect pests.