• 한국식물병리학회지
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• 제14권5호
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• pp.473-483
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• 1998

This paper is the sixth 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 circular leaf spot of Ligustrum ovalifolium by Cercospora adusta, leaf spot of Viola spp. by c. violae, leaf spot of Trifolium repens by C. zebrina, hypophyllous leaf sot of Angelica gigas by Passalora depressa, brown leaf spot of Euonymus japonicus by Pseudocercospora destructiva, brown leaf spot of Lonicera japonica by P. lonicericola, brown leaf spot of Parthenocissus tricuspidata by P. vitis, black spot of Echinops latifolius by Ramularia cynarae, leaf spot of Petasites japonicus by R. major, and leaf spot of Plantagoasiactica by R. plantaginis, respectively.

• 한국식물병리학회지
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• 제11권3호
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• pp.197-209
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• 1995

This paper is a third report about the new fungal diseases of economic resource plants in Korea. It contains short descriptions on symptoms, occurrence conditions, pathogen, and some phytopathological notes for each of 10 fungal plant diseases. They are angular leaf spot of Achyranthes japonica by Cercospora achyranthis causing leaf spot and defoliation in the shade of plants, leaf spot of Armoracia lapathifolia by Cercospora armoraciae causing leaf spot to blight from the rainy season to autumn, hypophyllous mold of Dioscorea tokoro by Distocercospora pachyderma causing leaf spot and yellowing, hypophyllous mold of Artemisia spp.by Mycovellosiella ferruginea causing leaf spot and yellowing, angular leaf spot of Aralia elata by Pseudocercospora araliae causing velvety leaf spot and defoliation, hypophyllous mold of Lycium chinense by Pseudocercospora chengtuensis causing velvety leaf spot and defoliation from the rainy season to autumn, angular leaf spot of Diospyros lotus by Pseudocercospora disospyri-morrisianae causing leaf spot and defoliation from summer to autumn, brown leaf spot of Impatiens textori by Pseudocercospora nojimae causing leaf spot to blight from the rainy season, leaf spot of Cephalonoplos segetum by Ramularia cirsii causing leaf spot to blight throughout the growing season, and white mold of Leonurus sibiricus by Ramularia leonuri causing leaf spot to blight mostly in autumn.

• 한국식물병리학회지
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• 제10권3호
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• pp.181-191
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• 1994

Occurrence of diseases in economic resource plants in Korea is poorly known. This paper reports short descriptions on symptom, occurrence condition, pathogen, and some phytopathological notes for each 10 fungal plant diseases new to Korea; leaf spot of Rosa multiflora with Seimatosporium discosioides causing leaf spot and defoliation, leaf blight of Equisetum arvense with Titaeospora equiseti causing leaf spot to leaf blight, leaf blight of Setaria viridis with Phyrenochaeta setariae causing leaf spot of Aster tataricus with Septoria astericola causing leaf spot and black spot, powdery mildew of Clematis fusca var. coreana with Erysiphe ranunculi causing powdery mildew and dwarfing, powder mildew of Ligularia stenocephala with Erysiphe galeopsidis causing powdery mildew and dwarfing, powdery mildew of Phlox subulata with Erysiphe cichoracearum causing powdery mildew and defoliation tar spot of Lonicera japonica with Rhytisma lonicericola causing tar spot and dwarfing, white rust of Pharbitis nil with Albugo ipomoeae-pandulatae causing white rust and deformation, and white rust of Achyranthes japonica with Albugo achyranthis causing white rust and defoliation.

• 식물병연구
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• 제24권1호
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• pp.1-8
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• 2018

우리나라에서 재배되고 있는 키위나무 잎에는 궤양병과 세균성점무늬병처럼 세균에 의한 병과 잿빛곰팡이병, 흰가루병, 과실곰보병을 비롯하여 여러 가지 형태 점무늬병 등 곰팡이에 의한 병이 발생하여 피해를 주고 있다. 이 총설에서는 키위나무잎에 발생하는 피해를 주는 주요 병에 대하여 병징과 발생특성을 요약하고 지난 30년간 수행한 연구 업적과 현장 경험 그리고 세계적인 주요 연구 산물들을 기초로 하여 키위나무 잎에 발생하는 주요 병에 의한 피해를 경감할 수 있도록 농가에서 실용적으로 사용할 수 있는 방제방법을 제시하고자 한다.

• 방송공학회논문지
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• 제27권6호
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• pp.923-935
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• 2022

본 논문에서는 스마트팜 시스템에서 재배 중인 식물 잎의 질병을 검출하고, 질병 유형을 분류하는 방법을 제안한다. 영상으로부터식물 잎의 컬러 정보와 질병 유형의 형태 정보를 다층 퍼셉트론(MLP) 모델을 이용하여 학습한다. 1단계에서는 입력된 영상의 컬러분포를 분석하여 질병 존재 여부를 판단한다. 1단계의 질병 존재 가능성이 높은 영상에 대하여 2단계에서는 Mean shift clustering을 이용하여 작은 영역으로 분할하고, 각 분할된 영역 단위로 컬러 정보를 추출하여 제안한 Color Network에 의하여 질병 여부를 판별한다. 컬러 분할된 영역이 Color Network에 의하여 질병으로 판별되면, 3단계에서는 그 영역의 형태 정보를 추출하여 제안한 Shape Network를 이용하여 질병의 유형을 분류한다. 사과나무 잎과 서양 양상추(Iceberg)에서 발생하는 두 가지 대분류 유형의 질병에 대하여, 제안한 기법은 작은 영역 단위로는 92.3%의 잎 질병 검출률을 보였으며, 보통 2개 이상의 질병 영역이 존재하는 한 장의 영상 단위로는 99.3% 이상의 검출률을 보였다. 본 논문에서 제안한 방법은 스마트팜 환경에서 잎 식물의 질병 여부를 조기에 발견할 수 있으며, 대상 식물에 따른 추가 학습 없이 다양한 식물과 질병 유형으로 확대 적용이 가능하다.

• International Journal of Computer Science & Network Security
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• 제23권6호
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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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• 제24권4호
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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.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• International Journal of Industrial Entomology and Biomaterials
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• 제12권1호
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• pp.35-39
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• 2006

Fifty-six indigenous and twenty nine exotic mulberry varieties were screened against powdery mildew, Myrothecium leaf spot, Pseudocercospora leaf spot, sooty mold and bacterial leaf spot for a period of three years under field condition. The percent disease index (PDI) was recorded during peak season of the foliar diseases. Out of eighty-five varieties studied, ten varieties were highly resistant and eight were resistant to powdery mildew; six varieties were immune and seventy-eight varieties were highly resistant to Myrothecium leaf spot; sixty varieties were highly resistant and 21 were resistant to Pseudocercospora leaf spot; forty four varieties were highly resistant to sooty mold and two varieties were immune and fifty-eight were highly resistant to bacterial leaf spot. Lowest cumulatative disease index was observed in M. multicaulis (7.28) followed by Thailand lobed (7.85) and Italian mulberry (8.06).

• 한국식물병리학회지
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• 제14권1호
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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.