• Journal of Korean Society for Quality Management
• /
• v.48 no.4
• /
• pp.581-596
• /
• 2020

Purpose: purpose of this study was to propose the web application platform which can be to detect and discriminate various diseases and pest of tomato plant based on the large amount of disease image data observed in the facility or the open field. Methods: The deep learning algorithms uesed at the web applivation platform are consisted as the combining form of Faster R-CNN with the pre-trained convolution neural network (CNN) models such as SSD_mobilenet v1, Inception v2, Resnet50 and Resnet101 models. To evaluate the superiority of the newly proposed web application platform, we collected 850 images of four diseases such as Bacterial cankers, Late blight, Leaf miners, and Powdery mildew that occur the most frequent in tomato plants. Of these, 750 were used to learn the algorithm, and the remaining 100 images were used to evaluate the algorithm. Results: From the experiments, the deep learning algorithm combining Faster R-CNN with SSD_mobilnet v1, Inception v2, Resnet50, and Restnet101 showed detection accuracy of 31.0%, 87.7%, 84.4%, and 90.8% respectively. Finally, we constructed a web application platform that can detect and discriminate various tomato deseases using best deep learning algorithm. If farmers uploaded image captured by their digital cameras such as smart phone camera or DSLR (Digital Single Lens Reflex) camera, then they can receive an information for detection, identification and disease control about captured tomato disease through the proposed web application platform. Conclusion: Incheon Port needs to act actively paying.

• The Plant Pathology Journal
• /
• v.34 no.2
• /
• pp.104-112
• /
• 2018

Accurate and rapid detection of bacterial plant pathogen is the first step toward disease management and prevention of pathogen spread. Bacterial plant pathogens Clavibacter michiganensis subsp. nebraskensis (Cmn), Pantoea stewartii subsp. stewartii (Pss), and Rathayibacter tritici (Rt) cause Goss's bacterial wilt and blight of maize, Stewart's wilt of maize and spike blight of wheat and barley, respectively. The bacterial diseases are not globally distributed and not present in Korea. This study adopted comparative genomics approach and aimed to develop specific primer pairs to detect these three bacterial pathogens. Genome comparison among target pathogens and their closely related bacterial species generated 15-20 candidate primer pairs per bacterial pathogen. The primer pairs were assessed by a conventional PCR for specificity against 33 species of Clavibacter, Pantoea, Rathayibacter, Pectobacterium, Curtobacterium. The investigation for specificity and sensitivity of the primer pairs allowed final selection of one or two primer pairs per bacterial pathogens. In our assay condition, a detection limit of Pss and Cmn was $2pg/{\mu}l$ of genomic DNA per PCR reaction, while the detection limit for Rt primers was higher. The selected primers could also detect bacterial cells up to $8.8{\times}10^3cfu$ to $7.84{\times}10^4cfu$ per gram of grain seeds artificially infected with corresponding bacterial pathogens. The primer pairs and PCR assay developed in this study provide an accurate and rapid detection method for three bacterial pathogens of grains, which can be used to investigate bacteria contamination in grain seeds and to ultimately prevent pathogen dissemination over countries.

• The Plant Pathology Journal
• /
• v.31 no.3
• /
• pp.212-218
• /
• 2015

In this study, we developed a species-specific PCR assay for rapid and accurate detection of three Xanthomonas species, X. axonopodis pv. poinsettiicola (XAP), X. hyacinthi (XH) and X. campestris pv. zantedeschiae (XCZ), based on their draft genome sequences. XAP, XH and XCZ genomes consist of single chromosomes that contain 5,221, 4,395 and 7,986 protein coding genes, respectively. Species-specific primers were designed from variable regions of the draft genome sequence data and assessed by a PCR-based detection method. These primers were also tested for specificity against 17 allied Xanthomonas species as well as against the host DNA and the microbial community of the host surface. Three primer sets were found to be very specific and no amplification product was obtained with the host DNA and the microbial community of the host surface. In addition, a detection limit of $1pg/{\mu}l$ per PCR reaction was detected when these primer sets were used to amplify corresponding bacterial DNAs. Therefore, these primer sets and the developed species-specific PCR assay represent a valuable, sensitive, and rapid diagnostic tool that can be used to detect three specific pathogens at early stages of infection and may help control diseases.

• The Plant Pathology Journal
• /
• v.35 no.6
• /
• pp.635-643
• /
• 2019

To detect Xanthomonas arboricola pv. pruni, a loopmediated isothermal amplification (LAMP) detection method were developed. The LAMP assay was designed to test crude plant tissue without pre-extraction, or heating incubation, and without advanced analysis equipment. The LAMP primers were designed by targeting an ABC transporter ATP-binding protein, this primer set was tested using the genomic DNA of Xanthomonas and non-Xanthomonas strains, and a ladder product was generated from the genomic DNA of X. arboricola pv. pruni strain but not from 12 other Xanthomonas species strains and 6 strains of other genera. The LAMP conditions were checked with the healthy leaves of 31 peach varieties, and no reaction was detected using either the peach leaves or the peach DNA as a template. Furthermore, the high diagnostic accuracy of the LAMP method was confirmed with 13 X. arboricola pv. pruni strains isolated from various regions in Korea, with all samples exhibiting a positive reaction in LAMP assays. In particular, the LAMP method successfully detected the pathogen in diseased peach leaves and fruit in the field, and the LAMP conditions were proven to be a reliable diagnostic method for the specific detection and identification of X. arboricola pv. pruni in peach orchards.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.46 no.1
• /
• pp.53-56
• /
• 2001

Barley Yellow Dwarf Virus (BYDV), an aphid-borne luteovirus, is a major plant pathogenic disease causing a huge economic loss in the grain production of a wide range of Gramineae species throughout the world. It has been recently reported that BYDV also occurred frequently in wheat field of Korea. Here, we performed to develop the detection and classification methods of BYDV strains that were accomplished by reverse transcription-polymerase chain reaction (RT-PCR). Since there are high variations among BYDV strains, three pairs of primers were designed to detect BYDV strains such as PAV (Vic-PAV and CN-PAV) and MAV (primer A) simultaneously, specifically Vic-PAV(primer B), and MAV (primer C) based on the genomic RNA sequences of BYDV strains previously published. The validity of the primers was confirmed using several BYDV strains obtained from CIMMYT. Though three BYDV strains were able to be detected using primer A, PCR products were not distinguished between two PAV strains. It was possible to separate them with a restriction enzyme, EcoRI, whose restriction site was present in the amplified DNA fragment from Vic-PAV, but not from CN-PAV.

• Research in Plant Disease
• /
• v.17 no.2
• /
• pp.184-190
• /
• 2011

Ralstonia solanacearum, a causal agent of bacterium wilt is very difficult to control once the disease becomes endemic. Thus, Ralstonia solanacearum is a plant quarantine bacterium in many countries including Korea. In this study, we developed PCR assays, which can detect Ralstonia solanacearum from the Solanaceae seeds. Primers RS-JH-F and RS-JH-R amplified specifically a 401 bp fragment only from Ralstonia solanacearum race 1 and race 3. The nested PCR primers, RS-JH-F-ne and RS-JH-R-ne that were designed inside of 1st PCR amplicon amplified specifically a 131 bp fragment only from Ralstonia solanacearum race 1 and race 3. The primers did not amplify any non-specific DNA from the seed extracts of the Solanaceae including tomato and pepper. When detection sensitivity were compared using the Solanaceae seeds inoculated with target bacteria artificially, the nested PCR method developed in this study 100 times more sensitive than ELISA and selective medium. Therefore, we believe that the PCR assays developed in this work is very useful to detect Ralstonia solanacearum in the Solanaceae seeds.

• Research in Plant Disease
• /
• v.16 no.2
• /
• pp.121-124
• /
• 2010

Immunocapture reverse transcription polymerase chain reaction (IC-RT-PCR) was applied to the detection of Cucumber green mottle mosaic virus (CGMMV), Kyuri green mottle mosaic virus (KGMMV), and Zucchini green mottle mosaic virus (ZGMMV) on Cucurbitaceae crops. These seed-borne tobamoviruses were accurately detected from the infected leaves and seeds by IC-RT-PCR. This method was estimated to be about 100 times more sensitive than ELISA, and also it allowed the direct confirmation of ELISA results by using the captured antigens from a completed ELISA microwell. This convenient and reliable method could be used routinely for large-scale field surveys or seed tests of Cucurbitaceae crops.

• The Plant Pathology Journal
• /
• v.39 no.6
• /
• pp.614-624
• /
• 2023

Botrytis cinerea is a major fungal plant pathogen that causes gray mold disease in strawberries, leading to a decrease in strawberry yield. While benzimidazole is widely used as a fungicide for controlling this disease, the increasing prevalence of resistant populations to this fungicide undermines its effectiveness. To investigate benzimidazole resistant B. cinerea in South Korea, 78 strains were isolated from strawberries grown in 78 different farms in 2022, and their EC₅₀ values for benzimidazole were examined. As a result, 64 strains exhibited resistance to benzimidazole, and experimental tests using detached strawberry leaves and the plants in a greenhouse confirmed the reduced efficacy of benzimidazole to control these strains. The benzimidazole resistant strains identified in this study possessed two types of mutations, E198A or E198V, in the TUB2 gene. To detect these mutations, TaqMan probes were designed, enabling rapid identification of benzimidazole resistant B. cinerea in strawberry and tomato farms. This study utilizes TaqMan real-time polymerase chain reaction analysis to swiftly identify benzimidazole resistant B. cinerea, thereby offering the possibility of effective disease management by identifying optimum locations and time of application.

• The Plant Pathology Journal
• /
• v.34 no.3
• /
• pp.163-170
• /
• 2018

Strawberry Fusarium wilt disease, caused by Fusarium oxysporum f. sp. fragariae, is the most devastating disease in strawberry production. The pathogen produces chlamydospores which tolerate against harsh environment, fungicide and survive for decades in soil. Development of detection and quantification techniques are regarded significantly in many soilborne pathogens to prevent damage from diseases. In this study, we improved specific-quantitative primers for F. oxysporum f. sp. fragariae to reveal correlation between the pathogen density and the disease severity. Standard curve $r^2$ value of the specific-quantitative primers for qRT-PCR and meting curve were over 0.99 and $80.5^{\circ}C$, respectively. Over pathogen $10^5cfu/g$ of soil was required to cause the disease in both lab and field conditions. With the minimum density to develop the wilt disease, the pathogen affected near 60% in nursery plantation. A biological control microbe agent and soil solarization reduced the pathogen population 2-fold and 1.5-fold in soil, respectively. The developed F. oxysporum f. sp. fragariae specific qRT-PCR protocol may contribute to evaluating soil healthiness and appropriate decision making to control the disease.

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