• The Plant Pathology Journal
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• v.16 no.6
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• pp.325-327
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• 2000

A leaf spot of rye (Secale cereale L.) was observed during the summer 1999 in Korea. A fungus associated with the disease was identified as Bipolaris sorokiniana (Sacc.) Shoem., based on the morphological characteristics of conidia and conidiophores. Pathogenicity of the fungus was proven by artificial inoculation on rye plants. This is the first record of leaf spot on rye caused by B. sorokiniana in Korea.

• Korean Journal Plant Pathology
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• v.11 no.3
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• pp.282-284
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• 1995

Grey leaf spot on tomato plants was first observed in Sedo-myeon, Puyo-kun, Chungnam province of Korea in 1994. This disease which had not been reported before in Korea exhibited different symptoms from those of other leaf spot diseases on tomato plants. The symptoms were characterized by small irregular-shaped spots on leaves at the initial stage of the infection, subsequent spread and coalescence of the spots throughout the leaves with ultimate necrosis, and abscission from the plants. When healthy tomato plants were inoculated with a conidial suspension of the fungus isolated from the lesion of a diseased plant in a field, the same characteristic symptoms as those in the field were produced. Furthermore, the same pathogen could be reisolated from the lesions formed buy the inoculation. Conidial characteristics of the pathogen were as follows; oblong shape with constricted 3 transverse septa, round-shaped base, round- or point-shaped apex, size of 45~75$\times$12.5~17.5 ${\mu}{\textrm}{m}$, and 3.5 : 1 ratio of length to width. The pathogen was identified as Stemphylium lycopersici and thus this is the first report on the occurrence of grey leaf spot disease on tomato plants caused by s. lycopersici in Korea.

• Research in Plant Disease
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• v.24 no.1
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• pp.1-8
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• 2018

Bacterial diseases such as bacterial canker and bacterial leaf spot and fungal diseases such as gray mold, powdery mildew, side rot and leaf spots are major diseases damaging leaves of kiwifruit in Korea. In this review, we summarize symptoms and epidemiological characteristics of the major bacterial and fungal leaf diseases of kiwifruit and propose proper control methods of the diseases that can be practically utilized at the farmers' kiwifruit orchards in order to prevent the diseases on the basis of our research works and field experiences and important research products conducted during the last three decades in the world.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• The Plant Pathology Journal
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• v.31 no.2
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• pp.132-139
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• 2015

Bt-cotton germplasm, consisting of 75 genotypes was evaluated against cotton leaf curl disease (CLCuD) under high inoculum pressure in the field and using graft inoculation in glasshouse by visual symptom scoring assessments. None of the tested genotype was found disease free under both evaluation tests. Under field conditions in 2011, 3 genotypes were found resistant, 4 moderately resistant, 3 tolerant, 2 moderately susceptible and one susceptible; in 2012, 3 genotypes were tolerant, 7 moderately susceptible, 5 susceptible and 38 highly susceptible; in 2013, one was moderately susceptible and 51 were highly susceptible with varying degree of percent disease index (PDI) and severity index (SI). However, through graft evaluation in glasshouse, none of the graft inoculated plant was symptomless. All tested genotypes showed disease symptoms with SI values ranging between 5.0 and 6.0, and latent period between 12 and 14 days. Of the 75 genotypes evaluated using graft inoculation, 11 were found susceptible with SI values of 5.0 to 5.4 while remaining 64 were highly susceptible with SI values of 5.5 to 6.0. Inoculated plants of all tested genotypes exhibited severe disease symptoms within 10 days after the appearance of initial symptoms. No reduction in SI value was observed until the end of the experiment i.e., 90 days after grafting. Information generated under the present study clearly demonstrates that no sources of resistance to CLCuD are available among the tested Bt-cotton genotypes. So, a breeding programme is needed to introgress the CLCuD-resistance from other resistant sources to agronomically suitable Bt-cotton genotypes.

• Research in Plant Disease
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• v.10 no.4
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• pp.341-344
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• 2004

Grapevine leaf spot caused by Pseudocercospora vitis also called leaf Isariopsis spot which is one of the most important disease in Korea. Spots on the leaves are irregular to angular. They are brown, measure 2${\sim}$20 mm in diameter. Campbell Early, the major grape cultivar in Korea, is very susceptible to this disease. Leaf spot of grape is outbreak about 95% in ratio of diseased leaves according to the areas and management state of orchards in Korea. The diseased grapevines have some difficulty in raise of the trees by early defoliation and decrease the quality of fruits by disadvantage of assimilation products accumulation. Severe outbreak of the grapevine leaf spot is one of the factors that decrease of sugar content of the fruits about 20%. The strong negative correlation was observed between disease increase of grapevine and decrease of sugar content of the fruits. The correlation equation between relative sugar content of fruits and accumulated percentage of diseased leaves was Y = 0.0009$X^2$ - 0.3717X + 116.45($r^2$= 0.98).

• Korean journal of applied entomology
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• v.24 no.2 s.63
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• pp.103-106
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• 1985

The present study was carried out to determine appropriate growth stage for evaluating resistance to septoria brown spot in field and to search resistance sources from soybean germ plasm. Disease severity expressed by log $\frac{x}{1-x}$ was different with soybean genotypes and vertical progress of the disease was related to the diseased leaf area. Correlation between diseased leaf area and the area under septoria brown spot disease progress curve (AUBC) was highest at full blooming stage, indicating a reasonable stage for measuring the disease severity to evaluate resistance in field. There was no lines highly resistant to the disease among 1,428 native soybean lines tested.

• Korean Journal of Organic Agriculture
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• v.22 no.4
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• pp.705-718
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• 2014

This study was performed to evaluate the suppressive effects of organic fungicides made using environment-friendly materials on leaf spot disease and anthracnose that infect ginseng. Anthracnose (Colletotrichum gloeosporioides) and leaf spot disease (Alternaria panax) are principal diseases that decrease the yield of ginseng by defoliation before root enlargement. Fermented eggs and oyster shells, water extract of green tea and ethanol extract of red ginseng dregs were significantly effective in suppressing leaf spot disease. Fermented crab and shrimp shells and fermented motherwort were also effective in suppressing the recurrence of ginseng anthracnose. The preventive effects of these environment-friendly materials were definitely superior to the therapeutic effects. Therefore, these materials could be used as alternatives to chemical pesticides, which can not be applied in organic ginseng cultivation field. These organic fungicides need to be applied before the incidence of ginseng anthracnose in order to maximize their suppressive effects.

• The Plant Pathology Journal
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• v.35 no.5
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• pp.521-529
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• 2019

Tomato yellow leaf curl China virus is a species of the widespread geminiviruses. The infection of Nicotiana benthamiana by Tomato yellow leaf curl China virus (TYLCCNV) causes a reduction in photosynthetic activity, which is part of the viral symptoms. ${\beta}C1$ is a viral factor encoded by the betasatellite DNA ($DNA{\beta}$) accompanying TYLCCNV. It is a major viral pathogenicity factor of TYLCCNV. To elucidate the effect of ${\beta}C1$ on plants' photosynthesis, we measured the relative chlorophyll (Chl) content and Chl fluorescence in TY-LCCNV-infected and ${\beta}C1$ transgenic N. benthamiana plants. The results showed that Chl content is reduced in TYLCCNV A-infected, TYLCCNV A plus $DNA{\beta}$ (TYLCCNV A + ${\beta}$)-infected and ${\beta}C1$ transgenic plants. Further, changes in Chl fluorescence parameters, such as electron transport rate, $F_v/F_m$, NPQ, and qP, revealed that photosynthetic efficiency is compromised in the aforementioned N. benthamiana plants. The presense of ${\beta}C1$ aggravated the decrease of Chl content and photosynthetic efficiency during viral infection. Additionally, the real-time quantitative PCR analysis of oxygen evolving complex genes in photosystem II, such as PsbO, PsbP, PsbQ, and PsbR, showed a significant reduction of the relative expression of these genes at the late stage of TYLCCNV A + ${\beta}$ infection and at the vegetative stage of ${\beta}C1$ transgenic N. benthamiana plants. In summary, this study revealed the pathogenicity of TYLCCNV in photosynthesis and disclosed the effect of ${\beta}C1$ in exacerbating the damage in photosynthesis efficiency by TYLCCNV infection.