• Proceedings of the Korean Society of Plant Pathology Conference
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• 1999.04a
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• pp.46.1-46
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• 1999

• 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.

• The Plant Pathology Journal
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• v.38 no.3
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• pp.229-238
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• 2022

Fusarium wilt caused by Fusarium oxysporum f. sp. niveum (Fon) is the most serious soil-borne disease in the world and has become the main limiting factor of watermelon production. Reliable and quick detection and quantification of Fon are essential in the early stages of infection for control of watermelon Fusarium wilt. Traditional detection and identification tests are laborious and cannot efficiently quantify Fon isolates. In this work, a real-time polymerase chain reaction (PCR) assay has been described to accurately identify and quantify Fon in watermelon plants and soil. The FONRT-18 specific primer set which was designed based on identified specific sequence amplified a specific 172 bp band from Fon and no amplification from the other formae speciales of Fusarium oxysporum tested. The detection limits with primers were 1.26 pg/µl genomic DNA of Fon, 0.2 pg/ng total plant DNA in inoculated plant, and 50 conidia/g soil. The PCR assay could also evaluate the relationships between the disease index and Fon DNA quantity in watermelon plants and soil. The assay was further used to estimate the Fon content in soil after disinfection with CaCN₂. The real-time PCR method is rapid, accurate and reliable for monitoring and quantification analysis of Fon in watermelon plants and soil. It can be applied to the study of disease diagnosis, plant-pathogen interactions, and effective management.

• Plant Resources
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• v.3 no.3
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• pp.173-178
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• 2000

This test checked jujube witches'-broom disease, sumac witches'-broom disease, paulonia witches'- broom disease, and mulberry dwarf disease whether or not they were infected by phytoplasma, using universal and specific primers. Upon treatment of DNA amplified by PCR of phytoplasma with Alu I , Hpa II and Sat I restricted enzymes, distinction of phytoplasmas was possible. Particularly, phytoplasma of each host was distinguishable by treatment of Hpa II restricted enzyme. Meanwhile, analysis of restricted enzymes of jujube witches'-broom disease showed a higher infectivity of phytoplasmas of two origins. There were a lot of relations between jujube witches'-broom disease and sumac witches'-broom disease, and between paulonia witches'-broom disease and mulberry dwarf disease.

• Korean Journal of Veterinary Service
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• v.46 no.2
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• pp.123-135
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• 2023

Feline calicivirus (FCV) is considered the main viral pathogen of feline upper respiratory tract disease (URTD). The frequent mutations of field FCV strains result in the poor diagnostic sensitivity of previously developed molecular diagnostic assays. In this study, a more sensitive real-time reverse transcription-polymerase chain reaction (qRT-PCR) assay was developed for broad detection of currently circulating FCVs and comparatively evaluated the diagnostic performance with previously developed qRT-PCR assay using clinical samples collected from Korean cat populations. The developed qRT-PCR assay specifically amplified the FCV p30 gene with a detection limit of below 10 copies/reaction. The assay showed high repeatability and reproducibility, with coefficients of intra-assay and inter-assay variation of less than 2%. Based on the clinical evaluation using 94 clinical samples obtained from URTD-suspected cats, the detection rate of FCV by the developed qRT-PCR assay was 47.9%, which was higher than that of the previous qRT-PCR assay (43.6%). The prevalence of FCV determined by the new qRT-PCR assay in this study was much higher than those of previous Korean studies determined by conventional RT-PCR assays. Due to the high sensitivity, specificity, and accuracy, the new qRT-PCR assay developed in this study will serve as a promising tool for etiological and epidemiological studies of FCV circulating in Korea. Furthermore, the prevalence data obtained in this study will contribute to expanding knowledge about the epidemiology of FCV in Korea.

• Microbiology and Biotechnology Letters
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• v.46 no.3
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• pp.269-276
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• 2018

Rabies virus (RABV)-specific antibodies in animals and humans are measured using standard methods such as fluorescent antibody virus neutralization (FAVN) tests and rapid fluorescent focus inhibition tests, which are based on cell culture systems. An alternative assay that is safe and easy to perform is required for rapid sero-surveillance following mass vaccination of animals. Two purified monoclonal antibodies (4G36 and B2H17) against RABV were selected as capture and detection antibodies, respectively. A genetically modified RABV, the ERAGS strain, was propagated and concentrated by polyethylene glycol precipitation. Optimal conditions for the RABV antigen, antibodies, and serum dilution for a blocking enzymelinked immune sorbent assay (B-ELISA) were established. We evaluated the sensitivity, specificity, and accuracy of the B-ELISA using serum samples from 138 dogs, 71 raccoon dogs, and 25 cats. The B-ELISA showed a diagnostic sensitivity of 95.8-96.3%, specificity of 91.3-100%, and accuracy of 96.0-97.2% compared to the FAVN test. These results suggest that the B-ELISA is useful for sero-surveillance of RABV in dogs, raccoon dogs, and cats.

• Molecules and Cells
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• v.45 no.7
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• pp.502-511
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• 2022

Bacterial volatile compounds (BVCs) exert beneficial effects on plant protection both directly and indirectly. Although BVCs have been detected in vitro, their detection in situ remains challenging. The purpose of this study was to investigate the possibility of BVCs detection under in situ condition and estimate the potentials of in situ BVC to plants at below detection limit. We developed a method for detecting BVCs released by the soil bacteria Bacillus velezensis strain GB03 and Streptomyces griseus strain S4-7 in situ using solid-phase microextraction coupled with gas chromatography-mass spectrometry (SPME-GC-MS). Additionally, we evaluated the BVC detection limit in the rhizosphere and induction of systemic immune response in tomato plants grown in the greenhouse. Two signature BVCs, 2-nonanone and caryolan-1-ol, of GB03 and S4-7 respectively were successfully detected using the soil-vial system. However, these BVCs could not be detected in the rhizosphere pretreated with strains GB03 and S4-7. The detection limit of 2-nonanone in the tomato rhizosphere was 1 µM. Unexpectedly, drench application of 2-nonanone at 10 nM concentration, which is below its detection limit, protected tomato seedlings against Pseudomonas syringae pv. tomato. Our finding highlights that BVCs, including 2-nonanone, released by a soil bacterium are functional even when present at a concentration below the detection limit of SPME-GC-MS.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• The Plant Pathology Journal
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• v.37 no.5
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• pp.437-445
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• 2021

Tilletia laevis Kuhn (syn. Tilletia foetida (Wallr.) Liro.) causes wheat common bunt, which is one of the most devastating plant diseases in the world. Common bunt can result in a reduction of 80% or even a total loss of wheat production. In this study, the characteristics of T. laevis infection in compatible wheat plants were defined based on the combination of scanning electron microscopy, transmission electron microscopy and laser scanning confocal microscopy. We found T. laevis could lead to the abnormal growth of wheat tissues and cells, such as leakage of chloroplasts, deformities, disordered arrangements of mesophyll cells and also thickening of the cell wall of mesophyll cells in leaf tissue. What's more, T. laevis teliospores were found in the roots, stems, flag leaves, and glumes of infected wheat plants instead of just in the ovaries, as previously reported. The abnormal characteristics caused by T. laevis may be used for early detection of this pathogen instead of molecular markers in addition to providing theoretical insights into T. laevis and wheat interactions for breeding of common bunt resistance.

• 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.