• 한국자원식물학회지
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• 제31권6호
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• pp.714-718
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• 2018

A new Gomchwi cultivar 'Sammany' was developed by a cross between Gomchwi (Ligularia fischeri (Ledeb.) Turcz.) and Handaeri-gomchwi (Ligularia fischeri var. spiciformis Nakai). Gomchwi is a common Korean name referring wild edible plant species within Ligularia genus. 'Sammany' has purple colored petiole ears and petiole trichome is absent. It has 2nd degree leaf vein density. Plant height, leaf length, leaf width and petiole length were 46.2, 19.1, 19.5 and 32.1 cm, respectively. Plant height was higher than 'Gondalbi'. Bolting occurred in mid. July and it flowered from late August to early September. 'Gondalbi' bolted and flowered 26 days earlier than 'Sammany', and consequently has earlier flowering time more than 26 day. Leaf number of 'Sammany' was 156 per plant but 'Gondalbi' had 130. 'Sammany' had thicker leaves (0.61 mm) compared to 'Gondalbi' (0.46 mm). As a result, yield was higher in 'Sammany (1,077 g/plant)' than 'Gondalbi (798 g/plant)' and leaf hardness was lower in 'Sammany ($20.8kg/cm^2$)' compared to 'Gondalbi ($23.0kg/cm^2$)'. In addition, 'Sammany' was found to be moderately resistant to powdery mildew. With enhanced agronomic and pathology traits, 'Sammany' was newly registered as a new Gomchwi cultivar (variety protection no. 131 on May 2017).

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2018년도 춘계 종합학술대회 논문집
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• pp.459-460
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• 2018

In this paper, we present a data augmentation method that generates synthetic plant disease images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation techniques to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of 2789 images of tomato plant diseases (Gray mold, Canker, Leaf mold, Plague, Leaf miner, Whitefly etc.).

• The Plant Pathology Journal
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• 제36권5호
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• pp.459-467
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• 2020

Areca palm yellow leaf (AYL) disease caused by the 16SrI group phytoplasma is a serious threat to the development of the Areca palm industry in China. The 16S rRNA gene sequence was utilized to establish a rapid and efficient detection system efficient for the 16SrI-B subgroup AYL phytoplasma in China by loop-mediated isothermal amplification (LAMP). The results showed that two sets of LAMP detection primers, 16SrDNA-2 and 16SrDNA-3, were efficient for 16SrI-B subgroup AYL phytoplasma in China, with positive results appearing under reaction conditions of 64℃ for 40 min. The lowest detection limit for the two LAMP detection assays was the same at 200 ag/μl, namely approximately 53 copies/μl of the target fragments. Phytoplasma was detected in all AYL disease samples from Baoting, Tunchang, and Wanning counties in Hainan province using the two sets of LAMP primers 16SrDNA-2 and 16SrDNA-3, whereas no phytoplasma was detected in the negative control. The LAMP method established in this study with comparatively high sensitivity and stability, provides reliable results that could be visually detected, making it suitable for application and research in rapid diagnosis of AYL disease, detection of seedlings with the pathogen and breeding of disease-resistant Areca palm varieties.

• 한국균학회지
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• 제50권4호
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• pp.367-372
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• 2022

Leaf spot symptoms were observed in water spinach (Ipomoea aquatica) plants growing in fields in Ansan and Hongseong, Korea, during disease surveys in 2019 and 2020. The symptoms appeared as brown to dark brown circular or irregular spots on the leaves of the plants. The disease incidence on the plant leaves in the fields investigated at the two locations ranged from 1% to 20%. Five single-spore isolates of Phoma sp. Were obtained from lesions of the diseased leaves. All the isolates were identified as Ectophoma multirostrata based on their cultural and morphological characteristics, as well as molecular analysis. Two isolates of E. multirostrata were tested for pathogenicity on water spinach leaves using artificial inoculation. The tested isolates caused leaf spot symptoms in the inoculated plants. These symptoms were similar to those observed in plants from the investigated fields. To our knowledge, this is the first report of E. multirostrata causing leaf spot in water spinach.

• Mycobiology
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• 제51권6호
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• pp.393-400
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• 2023

During a disease survey in October 2019, leaf spot symptoms with a yellow halo were observed on Korean angelica (Anglica gigas) plants grown in fields in Pyeongchang, Gangwon Province, Korea. Incidence of diseased leaves of the plants in the investigated fields ranged from 10% to 60%. Morphological and cultural characteristics of two single-spore isolates from the leaf lesions indicated that they belonged to the genus Didymella. Molecular phylogenetic analyses using combined sequences of LSU, ITS, TUB2, and RPB2 regions showed distinct clustering of the isolates from other Didymella species. In addition, the morphological and cultural characteristics of the isolates were somewhat different from those of closely related Didymella spp. Therefore, the novelty of the isolates was proved based on the investigations. Pathogenicity of the novel Didymella species isolates was confirmed on leaves of Korean angelica plants via artificial inoculation. This study reveals that Didymella gigantis sp. nov. causes leaf spot in Korean angelica.

• 한국식물병리학회:학술대회논문집
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• 한국식물병리학회 2003년도 정기총회 및 추계학술발표회
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• pp.25-27
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• 2003

Since 1993, a total of 50 problematic plant diseases unrecorded in Korea were surveyed in Gyeongnam province. Totally 34 new host plants to corresponding pathogens investigated in this study were 5 fruit trees, 9 vegetables, 12 ornamental plants, 3 industrial crops, and 5 medicinal plants. Among the newly recorded fruit tree diseases, fruit rot of pomegranate caused by Coniella granati and Rhizopus soft rot of peach caused by Rhizopus nigricans damaged severely showing 65.5% and 82.4% infection rate. Among the vegetable diseases, corynespora leaf spot of pepper caused by Corynespora cassiicola and the crown gall of pepper caused by Agrobacterium tumefaciens, powdery mildew of tomato caused by Oidiopsis taurica were the most severe revealing 47.6%, 84.7%, and 54.5% infection rate in heavily infected fields, respectively. In ornamental plants, collar rot of lily caused by Sclerotium rolfsii, gray mold of primula caused by Botrytis cinerea, soot leaf blight of dendrobium caused by Pseudocercospora dendrobium, sclerotinia rot of obedient plant caused by Sclerotinia sclerotiorum showed 32.7 to 64.8% disease incidence. On three industrial plants such as sword bean, broad bean, and cowpea, eight diseases were firstly found in this study. Among the diseases occurring on broad bean, rust caused by Uromyces viciae-fabae and red spot caused by Botrytis fabae were the major limiting factor for the cultivation of the plant showing over 64% infection rate in fields. In medicinal plants, anthracnose of safflower caused by Collectotrichum acutatum was considered the most severe disease on the plant and followed by collar rot caused by Sclerotium rolfsii.(중략)

• The Plant Pathology Journal
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• 제35권2호
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• pp.100-111
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• 2019

Glomerella leaf spot (GLS) caused by Colletotrichum spp. is a destructive disease of apple restricted to a few regions worldwide. The distribution and evolution of GLS symptoms were observed for two years in Uruguay. The recurrent ascopore production on leaves and the widespread randomized distribution of symptoms throughout trees and orchard, suggest that ascospores play an important role in the disease dispersion. The ability of ascospores to produce typical GLS symptom was demonstrated by artificial inoculation. Colletotrichum strains causing GLS did not result in rot development, despite remaining alive in fruit lesions. Based on phylogenetic analysis of actin, ${\beta}$-tubulin and glyceraldehyde-3-phosphate dehydrogenase gene regions of 46 isolates, 25 from fruits and 21 from leaves, C. karstii was identified for the first time causing GLS in Uruguay and C. fructicola was found to be the most frequent (89%) and aggressive species. The higher aggressiveness of C. fructicola and its ability on to produce abundant fertile perithecia could help to explain the predominance of this species in the field.

• The Plant Pathology Journal
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• 제19권4호
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• pp.200-202
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• 2003

Leaf spots were first observed on watermelon (Citrullus vulgaris Schrad) under polyethylene film-covered green-house in November 2002. Symptoms appeared as dark-brown circles or large irregular spots on the leaves of watermelon. Occasionally, zonal growth of the lesions was observed. Under humid conditions, small black sclerotium-like bodies (sporodochia) were produced on the surface of the lesions. The sporodochia on leaf lesions were sessile, polymorphic, variable in size, 35-850 $\mu\textrm{m}$ in diameter, and 30-470 $\mu\textrm{m}$ in depth. Conidia in sporodochium were black in mass, one-celled, rod-shaped, with rounded ends, hyaline, guttulate, and measured 6-8$\times$1.6-2.2 $\mu\textrm{m}$ in size. The pathogen was identified as Myrothecium roridum Tode ex Fr. This is the first report of Myrothecium leaf spot on watermelon naturally occurring in commercial greenhouses.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.