• The Plant Pathology Journal
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• v.21 no.4
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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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• v.43 no.2
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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.

• Research in Plant Disease
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• v.30 no.3
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• pp.256-267
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• 2024

We have developed FBcastS (Fire Blight Forecasting System), a cloud-based information system that leverages the K-Maryblyt forecasting model. The FBcastS provides an optimal timing for spraying antibiotics to prevent flower infection caused by Erwinia amylovora and forecasts the onset of disease symptoms to assist in scheduling field scouting activities. FBcastS comprises four discrete subsystems tailored to specific functionalities: meteorological data acquisition and processing, execution of the K-Maryblyt model, distribution of web-based information, and dissemination of spray timing notifications. The meteorological data acquisition subsystem gathers both observed and forecasted weather data from 1,583 sites across South Korea, including 761 apple or pear orchards where automated weather stations are installed for fire blight forecast. This subsystem also performs post-processing tasks such as quality control and data conversion. The model execution subsystem operates the K-Maryblyt model and stores its results in a database. The web-based service subsystem offers an array of internet-based services, including weather monitoring, mobile services for forecasting fire blight infection and symptoms, and nationwide fire blight monitoring. The final subsystem issues timely notifications of fire blight spray timing alert to growers based on forecasts from the K-Maryblyt model, blossom status, pesticide types, and field conditions, following guidelines set by the Rural Development Administration. FBcastS epitomizes a smart agriculture internet of things (IoT) by utilizing densely collected data with a spatial resolution of approximately 4.25 km to improve the accuracy of fire blight forecasts. The system's internet-based services ensure high accessibility and utility, making it a vital tool in data-driven smart agricultural practices.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2002.11a
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• pp.100.1-100
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• 2002

• 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 Korean Society for Quality Management
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• v.48 no.4
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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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• v.26 no.1
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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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.208-217
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• 2023

Accurate estimation of dormancy breaking and first bloom dates is crucial for effective fire blight control by disease model such as Maryblyt in apple orchards. The duration from dormancy breaking to first bloom in apple trees was influenced by daily minimum temperatures during the dormant period. The purpose of this study is to investigate the relationship between minimum temperatures during this period and the time taken for flowering to commence. Webcam data from eight apple orchards, equipped by the National Institute of Horticultural and Herbal Science, were observed from 2019 to 2023 to determine the dates of starting bloom (B1). Additionally, the dormancy breaking dates for these eight sites were estimated using an apple chill day model, with a value of -100.5 DD, based on collected weather data. Two regressions were performed to analyze the relationships: the first regression between the number of days under 0℃ (X₁) and the time from calculated dormancy breaking to observed first bloom (Y), resulting in Y = 0.87 × X₁ + 40.76 with R² = 0.84. The second regression examined the starting date of breaking dormancy (X₂) and the duration from dormancy breaking to observed first bloom (Y), resulting in Y = -1.07 × X₂ + 143.62 with R² = 0.92. These findings suggest that apple anti-chill days are significantly affected by minimum temperatures during the period from dormancy breaking to flowering, indicating their importance in fire blight control measures.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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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.