• The Plant Pathology Journal
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• v.34 no.3
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• pp.191-198
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• 2018

Fast and accurate diagnosis is needed to eradicate and manage economically important and invasive diseases like fire blight. Loop-mediated isothermal amplification (LAMP) is known as the best on-site diagnostic, because it is fast, highly specific to a target, and less sensitive to inhibitors in samples. In this study, LAMP assay that gives more consistent results for on-site diagnosis of fire blight than the previous developed LAMP assays was developed. Primers for new LAMP assay (named as DS-LAMP) were designed from a histidine-tRNA ligase gene (EAMY_RS32025) of E. amylovora CFBP1430 genome. The DS-LAMP amplified DNA (positive detection) only from genomic DNA of E. amylovora strains, not from either E. pyrifoliae (causing black shoot blight) or from Pseudomonas syringae pv. syringae (causing shoot blight on apple trees). The detection limit of DS-LAMP was 10 cells per LAMP reaction, equivalent to $10^4$ cells per ml of the sample extract. DS-LAMP successfully diagnosed the pathogens on four fire-blight infected apple and pear orchards. In addition, it could distinguish black shoot blight from fire blight. The $B{\ddot{u}}hlmann$-LAMP, developed previously for on-site diagnosis of fire blight, did not give consistent results for specificity to E. amylovora and on-site diagnosis; it gave positive reactions to three strains of E. pyrifoliae and two strains of P. syringae pv. syringae. It also, gave positive reactions to some healthy sample extracts. DS-LAMP, developed in this study, would give more accurate on-site diagnosis of fire blight, especially in the Republic of Korea, where fire blight and black shoot blight coexist.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.1-7
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• 2020

Early detection and classification of crop diseases play significant role to help farmers to reduce disease spread and to increase agricultural productivity. Recently, many researchers have used deep learning techniques like convolutional neural network (CNN) classifier for crop disease inspection with dataset of crop leaf images (e.g., PlantVillage dataset). These researches present over 90% of classification accuracy for crop diseases, but they have ability to detect only the pre-trained diseases. This paper proposes an efficient disease inspection CNN model for new crops not used in the pre-trained model. First, we present a benchmark crop disease classifier (CDC) for the crops in PlantVillage dataset using VGG16. Then we build a modified crop disease classifier (mCDC) to inspect diseases for untrained crops. The performance evaluation results show that the proposed model outperforms the benchmark classifier.

• The Plant Pathology Journal
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• v.38 no.3
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• pp.194-202
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• 2022

Erwinia amylovora and Erwinia pyrifoliae cause fire blight and black-shoot blight, respectively, in apples and pears. E. pyrifoliae is less pathogenic and has a narrower host range than that of E. amylovora. Fire blight and black-shoot blight exhibit similar symptoms, making it difficult to distinguish one bacterial disease from the other. Molecular tools that differentiate fire blight from black-shoot blight could guide in the implementation of appropriate management strategies to control both diseases. In this study, a primer set was developed to detect and distinguish E. amylovora from E. pyrifoliae by conventional polymerase chain reaction (PCR). The primers produced amplicons of different sizes that were specific to each bacterial species. PCR products from E. amylovora and E. pyrifoliae cells at concentrations of 10⁴ cfu/ml and 10⁷ cfu/ml, respectively, were amplified, which demonstrated sufficient primer detection sensitivity. This primer set provides a simple molecular tool to distinguish between two types of bacterial diseases with similar symptoms.

• The Plant Pathology Journal
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• v.34 no.6
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• pp.459-469
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• 2018

Plants and microorganisms (microbes) use information from chemicals such as volatile compounds to understand their environments. Proficiency in sensing and responding to these infochemicals increases an organism's ecological competence and ability to survive in competitive environments, particularly with regard to plant-pathogen interactions. Plants and microbes acquired the ability to sense and respond to biogenic volatiles during their evolutionary history. However, these signals can only be interpreted by humans through the use of state-of the-art technologies. Newly-developed tools allow microbe-induced plant volatiles to be detected in a rapid, precise, and non-invasive manner to diagnose plant diseases. Beside disease diagnosis, volatile compounds may also be valuable in improving crop productivity in sustainable agriculture. Bacterial volatile compounds (BVCs) have potential for use as a novel plant growth stimulant or as improver of fertilizer efficiency. BVCs can also elicit plant innate immunity against insect pests and microbial pathogens. Research is needed to expand our knowledge of BVCs and to produce BVC-based formulations that can be used practically in the field. Formulation possibilities include encapsulation and sol-gel matrices, which can be used in attract and kill formulations, chemigation, and seed priming. Exploitation of biogenic volatiles will facilitate the development of smart integrated plant management systems for disease control and productivity improvement.

• 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.

• Research in Plant Disease
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• v.13 no.3
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• pp.145-151
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• 2007

Xanthomonas axonopodis pv. glycines is the causal agent of bacterial pustule of soybean(Glycine max. (L.) Merr), which is one of the most prevalent bacterial diseases in Korea. In this study, Polymerase Chain Reaction (PCR) assay was applied to detect Xanthomonas axonopodis pv. glycines and to survey on seed contamination in 36 soybean cultivars of Korea. And we have to compare PCR assay with dilution-plating assay of detection and identification. We confirmed detection of pathogen from artificial infected seeds and natural Infected seeds using PCR assay. This assay gave results similar to a seed-wash dilution plating assay and proved more effective than classical methods. Results of survey on seed contamination by X. axonopodis pv. glycines from 36 cultivar seeds showed that the pathogen was detected from Pungsan-namulkong, Mallikong, Taekwangkong, Daemangkong, Ajukkarikong using PCR assay. Therefore, The PCR assay provides a sensitive, rapid tool for the specific detection of X. axonopodis pv. glycines in soybean seeds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• Smart Media Journal
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• v.12 no.7
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• pp.9-17
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• 2023

Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• The Plant Pathology Journal
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• v.33 no.3
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• pp.307-317
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• 2017

Satsuma dwarf virus (SDV) or Citrus mosaic sadwavirus (CiMV) were not consistently detected in RTPCR assay with the primer sets based on gene of Japan isolates. SDV and CiMV isolates were distinctively divided into two groups based on phylogenetic analysis of PP2 gene cloned from 22 Korean isolates, and the Korean CiMV and SDV isolates shared 95.5-96.2% and 97.1-97.7% sequence identity with Japanese isolate, respectively. We developed PP2-1 primer set based on the PP2 gene sequence of Korean isolates to simultaneously and effectively detect SDV and CiMV. And CTLV-2013 and CTV-po primer sets were newly designed for detection of Citrus tatter leaf virus (CTLV) and Citrus tristeza virus (CTV), respectively. Using these primer sets, a new multiplex PCR assay was developed as a means to simultaneously detect 4 citrus viruses, CTV, CTLV, SDV, and CiMV. The degree of detection by the multiplex PCR were consistent with those of uniplex RT-PCR for detection of each of the viruses. Therefore, the new multiplex PCR provides an efficient method for detecting 4 citrus viruses, which will help diagnose many citrus plants at the same time. We verified that 35.2% and 72.1% of 775 trees in 155 orchards were infected with SDV or CiMV (SDV/CiMV) and CTV by the multiplex-PCR assay, respectively, and CTLV was not detected in any of the trees tested.