• Korean Journal of Veterinary Research
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• v.54 no.2
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• pp.113-115
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• 2014

Salmonella are causative agents of gastroenteritis and systemic disease in animals. The invA gene was selected as a target sequence of loop-mediated isothermal amplification (LAMP) assay for diagnosis of Salmonella infection. The detection limits for broth dilution, spiked feces and enrichment were $10^4$, $10^5$ and $10^2$ CFUs/mL, respectively. The LAMP assay developed in the present study may be a reliable method for detection of Salmonella spp. in pig feces.

• Journal of Veterinary Science
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• v.19 no.6
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• pp.855-857
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• 2018

Porcine parvovirus 7 (PPV7) was first detected in Korean pig farms in 2017. The detection rate of PPV7 DNA was 24.0% (30/125) in aborted pig fetuses and 74.9% (262/350) in finishing pigs, suggesting that PPV7 has circulated among Korean domestic pig farms. Phylogenetic analysis based on capsid protein amino acid sequences demonstrated that the nine isolated Korean strains (PPV-KA1-3 and PPV-KF1-6) were closely related to the previously reported USA and Chinese PPV7 strains. In addition, the Korean strains exhibit genetic diversity with both insertion and deletion mutations. This study contributes to the understanding of the molecular epidemiology of PPV7 in Korea.

• The Plant Pathology Journal
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• v.39 no.4
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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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.

• Smart Media Journal
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• v.11 no.4
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• pp.38-45
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• 2022

Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improves the performance by ameliorating networks and optimizing the loss function. However, the data-centric part of a whole project also needs more investigation. In this paper, we proposed a systematic strategy with three different annotation methods for plant disease detection: local, semi-global, and global label. Experimental results on our paprika disease dataset show that a single class annotation with semi-global boxes may improve accuracy. In addition, we also studied the noise factor during the labeling process. An ablation study shows that annotation noise within 10% is acceptable for keeping good performance. Overall, this data-centric numerical analysis helps us to understand the significance of annotation methods, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection tasks. Our work encourages researchers to pay more attention to label quality and the essential issues of labeling methods.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• The Plant Pathology Journal
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• v.22 no.4
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• pp.314-317
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• 2006

A phage technique for detection of Xanthomonas axonopodis pv. citri, a causal bacterium of canker on Sastuma mandarin fruits was developed. Phage and ELISA techniques were compared for their sensitivity for detection of Xanthomonas axonopodis pv. citri on orange fruits. Both of techniques revealed a similar efficiency for the bacterial detection; the pathogenic bacteria were observed in pellet from the fruits with over one canker spot with below 2 mm in diameter. In field assays, the increase of phage population(120%) on surface of the fruits related to the disease development one month later indicated that the bacterial pathogens inhabit on the surface. The procedure will be effectively used for detection of only living bacterial pathogen on fruit surfaces of Satsuma mandarin and for the disease forecasting.

• The Plant Pathology Journal
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• v.25 no.4
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• pp.328-332
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• 2009

Diseases caused by Pepper yellow leaf curl virus infection is considered to be emerging plant diseases in Indonesia in the last five years. One key factor for disease management is the availability of accurate detection of the virus in plants. Polyclonal antibody for Pepper yellow leaf curl Indonesia virus-Bogor (PYLCIV-Bgr) was produced for detection of the virus using I-ELISA and DIBA methods. The antibody was able to detect PYLCIV-Bgr from infected plants up to dilution 1/16,384 and cross reaction was not observed with Cucumber mosaic virus (CMV), Tobacco mosaic virus (TMV), and Chilli veinal mottle virus (ChiVMV). Positive reaction was readily detected in membrane containing Begomovirus samples from Yogyakarta (Kaliurang and Kulonprogo) and West Java (Bogor and Segunung). Infection of PYLCIV-Bgr in chillipepper, tomato, and Ageratum conyzoides was also confirmed using polyclonal antibody for PYLCIV-Bgr in DIBA. Polyclonal antibody for PYLCIV-Bgr is suggested to be included in disease management approach due to its good detection level.

• The Plant Pathology Journal
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• v.33 no.2
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• pp.184-192
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• 2017

Tylenchulus semipenetrans is an important and widespread plant-parasitic nematode of citrus worldwide and can cause citrus slow decline disease leading to significant reduction in tree growth and yield. Rapid and accurate detection of T. semipenetrans in soil is important for the disease forecasting and management. In this study, a loop-mediated isothermal amplification (LAMP) assay was developed to detect T. semipenetrans using DNA extracted from soil. A set of five primers was designed from the internal transcribed spacer region (ITS1) of rDNA, and was highly specific to T. semipenetrans. The LAMP reaction was performed at $63^{\circ}C$ for 60 min. The LAMP product was visualized directly in one reaction tube by adding SYBR Green I. The detection limit of the LAMP assay was $10^{-2}J2/0.5g$ of soil, which was 10 times more sensitive than conventional PCR ($10^{-1}J2/0.5g$ of soil). Examination of 24 field soil samples revealed that the LAMP assay was applicable to a range of soils infested naturally with T. semipenetrans, and the total assay time was less than 2.5 h. These results indicated that the developed LAMP assay is a simple, rapid, sensitive, specific and accurate technique for detection of T. semipenetrans in field soil, and contributes to the effective management of citrus slow decline disease.

• Korean Journal of Veterinary Service
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• v.44 no.1
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• pp.15-19
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• 2021

Senecavirus A (SVA), previously known as Seneca Valley virus, can cause vesicular disease and neonatal losses in pigs that is clinically indistinguishable from foot-and-mouth disease virus (FMDV). After the first case report in Canada in 2007, it had been restrictively identified in North America including United States. But, since 2015, SVA emerged outside North America in Brazil, and also in several the Asian countries including China, Thailand, and Vietnam. Considering the SVA occurrence in neighboring countries, there has been a high risk that Korea can be introduced at any time. In particular, it is very important in terms of differential diagnosis in the suspected case of vesicular diseases in countries where FMD is occurring. So far, several different molecular detection methods for SVV have been published but not validated as the reference method, yet. In this study, seven different molecular methods for detecting SVA were evaluated. Among them, the method by Flowler et al, (2017) targeted to 3D gene region with the highest sensitivity and no cross reaction with other vesicular disease agents including FMDV, VSV and SVD, was selected and applied further to antigen surveillance of SVA. A total of 245 samples of 157 pigs from 61 farms submitted for animal disease diagnose nationwide during 2018 were tested all negative. In 2018, no sign of SVA occurrence have been confirmed in Korea, but the results of the surveillance for SVA needs to be continued and accumulated at a high risk of SVA in neighboring countries.