• The Plant Pathology Journal
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• v.38 no.5
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• pp.490-502
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• 2022

Xanthomonas oryzae pv. oryzae (Xoo) causes bacterial leaf blight (BLB) disease in rice (Oryza sativa L.) and it is among the most destructive pathogen responsible for severe yield losses. Potential bacterial biocontrol agents (BCAs) with plant growth promotion (PGP) abilities can be applied to better manage the BLB disease and increase crop yield, compared to current conventional practices. Thus, this study aimed to isolate, screen, and identify potential BCAs with PGP abilities. Isolation of the BCAs was performed from internal plant tissues and rhizosphere soil of healthy and Xoo-infected rice. A total of 18 bacterial strains were successfully screened for in vitro antagonistic ability against Xoo, siderophore production and PGP potentials. Among the bacterial strains, 3 endophytes, Bacillus sp. strain USML8, Bacillus sp. strain USML9, and Bacillus sp. strain USMR1 which were isolated from diseased plants harbored the BCA traits and significantly reduced leaf blight severity of rice. Simultaneously, the endophytic BCAs also possessed plant growth promoting traits and were able to enhance rice growth. Application of the selected endophytes (BCAs-PGP) at the early growth stage of rice exhibited potential in suppressing BLB disease and promoting rice growth.

• Research in Plant Disease
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• v.19 no.3
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• pp.196-200
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• 2013

This study was carried out to investigate yield loss due to soybean leaf spot disease caused by Cercospora sojina Hara and to determine the economic threshold level. The investigations revealed highly significant correlations between disease severity (diseased leaf area) and yield components (pod number per plant, total grain number per plant, total grain weight per plant, percent of ripened grain, weight of hundred seed, and yield). The correlation coefficients between leaf spot severity and each component were -0.90, -0.90, -0.92, -0.99, -0.90 and -0.94, respectively. The yield was inversely proportional to the diseased leaf area increased. The regression equation, yield prediction model, between disease severity (x) and yield (y) was obtained as y = -3.7213x + 354.99 ($R^2$ = 0.9047). Based on the yield prediction model, economic injury level and economic threshold level could be set as 3.3% and 2.6% of diseased leaf area of soybean.

• Korean Journal Plant Pathology
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• v.14 no.1
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• pp.52-61
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• 1998

This paper is the fifth report about the fungal diseases of economic resource plants observed newly in Korea. It contains short descriptions on symptoms, occurrence conditions, pathogens, and some phytopathological notes for each of 10 fungal plant diseases. They are identified as leaf spot of Adenophora triphylla var. japonica by Septoria lengyelii, leaf spot of Calystegia soldanella by S. convolvuli, leaf spot of Campanula punctata by S. campanulae, leaf spot of Codonopsis lanceolata by S. codonopsidis, leaf spot of Geum japonicum by s. gei, black spot of Oenanthe javanica by s. oenanthes, leaf spot of Oenothera odorata by S. oenotherae, angular leaf spot of Rehmannia glutinosa by S. digitalis, brown spot of Rubus crataegifolius by s. rubi, and leaf spot of Viola verecunda by S. violae-palustris, respectively.

• Research in Plant Disease
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• v.30 no.1
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• pp.82-87
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• 2024

Leatherleaf fern (Rumohra adiantiformis) is an important ornamental plant in Indonesia and global. Green fern leaves with bold dark green color with long shelf-life, attract florists as decoration. Indonesia is one important leatherleaf fern exporters, however currently an outbreak of leaf blight decreased production significantly. Initial symptom was reddish brown spots from edge of leaf, which was gradually followed by dark-brown necrotic lesions causing leaf blight and dried. This is a study to do Koch-Postulate approach and molecular identification, to identify the pathogen of the "new emerging disease" reported. Based on multigene analysis using primers from ITS, β-tub and tef1-α gene markers, the pathogen was identified as Neopestalotiopsis sp. All sequences have been deposited in GenBank with accession number of OR905551 (ITS), OR899817 (ßtubulin) and OR899816 (TEF). This Neopestalotiopsis leaf blight causes an emerging concern in leatherleaf fern in Indonesia and global biosecurity because it infected an export commodity.

• Korean Journal Plant Pathology
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• v.3 no.4
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• pp.285-290
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• 1987

Septoria brown spot caused by Septoria glycines Hemmi is one of the serious fungal diseases in soybean. Since little has been known about the disease progress in the field, the present study was conducted to determine the factors affecting the disease progress in the soybean plant. Disease severity and pattern of the progress of the Septoria brown spot were different with varieties. Susceptibility of soybean plants increased with increase of plant age and leaf maturity in order from the primary leaf to the newly expanded leaf. It seemed to be related with conidial germination on the leaves. Germination and germtube elongation were more inhibited by the diffusates obtained from upper leaves than those from lower leaves and they were higher in a susceptible variety than in a moderately resistant one.

• Korean Journal Plant Pathology
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• v.10 no.3
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• pp.169-174
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• 1994

A new unidentified pear leaf spot disease presumed to first occur in the late 1970's has recently become prevalent over the pear growing areas, and caused the greatest problem for pear production in Korea. The disease began to develop on pear leaves at mid- to late May, peaked at mid- to late une, but stopped further development until September in cool climate. Leaf lesions are 0.9∼2.5 mm in diam., oval or irregular to rectangular in shape, first appeared reddish purple, later changed to dark brown, and to whitish grey in the late season. Lesions were limited to appear only on the mature, hardened leaves, initially from leaf margin or near the leaf veins, and later scattered over the leaf surface. Individual lesions usually did not enlarge, but often coalesced each other, commonly causing shot holes and eventual early falling. The disease was most severe on the major pear cultivars Niitaka and Okusankichi ranged with 4 to 100% infections in trees, depending on the orchards, but not on the cultivar Chojuro. Damages from the disease included lower fruit weight, and higher acid and less sugar content in fruits, resulting in lowering the overall fruit quality. Etiology of the disease including identification of the causal organism is in a separate paper.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1250-1257
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• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• Korean Journal of Organic Agriculture
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• v.19 no.spc
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• pp.123-126
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• 2011

To investigate the forecasting of plant disease and insect for an agricultural complex and farm in environment-friendly cultivation of Rice, environment-friendly agricultural five complexs and five farms were selected in Youngam and Naju area, Jonnam, Korea. Preventation objects of plant disease and insect were leaf blast, neck blast, sheath blight, bacterial leaf blight, and hopper. Factors of sheath blight occurrence in environment-friendly agricultural complex were a fast transplanting time and a narrow planting density. Bacterial leaf blight in rice occurred severely in the area under water. Rice growth in environment-friendly agricultural complex was decreased heavy drying by hopper appearance.

• Research in Plant Disease
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• v.21 no.4
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• pp.330-333
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• 2015

In June 2013, we collected leaf spot disease samples of Datura metel from Gangneung, Gangwon Province, Korea. The symptoms observed were small circular to oval dark brown spots with irregular in shape or remained circular with concentric rings. We isolated the pathogen from infected leaves and cultured the fungus on potato dextrose agar. We examined the fungus morphologically and confirmed its pathogenicity according to Koch's postulates. The results of morphological examinations, pathogenicity tests, and the rDNA sequences of the internal transcribed spacer regions (ITS1 and ITS4), glycerol-3-phosphate dehydrogenase (G3PDH) and the RNA polymerase II second largest subunit (RPB2) gene sequence revealed that the causal agent was Alternaria tenuissima. To the best of our knowledge, this is the first report of leaf spot of D. metel caused by A. tenuissima in Korea as well as worldwide.

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.446-452
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• 2008

Machine vision system was used for analyzing leaf color disorders of tomato plants in a greenhouse. From the day when a few leave of tomato plants had started to wither, a series of images were captured by 4 times during 14 days. Among several color image spaces, Saturation frame in HSI color space was adequate to eliminate a background and Hue frame was good to detect infected disease area and tomato fruits. The processed image ($G{\sqcup}b^*$ image) by OR operation between G frame in RGB color space and $b^*$ frame in $La^*b^*$ color space was useful for image segmentation of a plant canopy area. This study calculated a ratio of the infected area to the plant canopy and manually analyzed leaf color disorders through an image segmentation for Hue frame of a tomato plant image. For automatically analyzing plant leave disease, this study selected twenty-seven color patches on the calibration bars as the corresponding to leaf color disorders. These selected color patches could represent 97% of the infected area analyzed by the manual method. Using only ten color patches among twenty-seven ones could represent over 85% of the infected area. This paper showed a proposed machine vision system may be effective for evaluating various leaf color disorders of plants growing in a greenhouse.