• 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 Korea Multimedia Society
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• v.23 no.10
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• pp.1250-1257
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• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• Research in Plant Disease
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• v.17 no.3
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• pp.376-380
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• 2011

The bacterial speck of tomato caused by Pseudomonas syringae pv. tomato leads to serious economic losses especially on fruits of susceptible genotype. Thus, Pseudomonas syringae pv. tomato is a plant quarantine bacterium in many countries including Korea. In this study, we developed specific PCR assays for detection of the bacterium from tomato seeds. A specific primer set is designed from the hrpZ gene for specific detection of Pseudomonas syringae pv. tomato. A 501 bp PCR product corresponding to hrpZ gene was amplified only form Pseudomonas syringae pv. tomato strains, but no PCR product was amplified from other tomato bacterial pathogens, such as Pseudomonas syringae pv. glycinea, P. syringae pv. maculicola, P. syringae pv. atropurpurea, P. syringae pv. morsprunorum, and from other P. syringae pathovar strains. The nested-PCR primer set corresponding to an internal fragment of the 501 bp sequence (hrpZ) gine was used to specific detection of Pseudomonas syringae pv. tomato in tomato seed. A 119 bp PCR product using nested PCR primer was highly specific and sensitive to detect low level of Pseudomonas syrigae pv. tomato in tomato seeds. We believe that the PCR assays developed in this study is very useful to detect Pseudomonas syringae pv. tomato from the tomato seeds.

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

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.446-452
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• 2008

Machine vision system was used for analyzing leaf color disorders of tomato plants in a greenhouse. From the day when a few leave of tomato plants had started to wither, a series of images were captured by 4 times during 14 days. Among several color image spaces, Saturation frame in HSI color space was adequate to eliminate a background and Hue frame was good to detect infected disease area and tomato fruits. The processed image ($G{\sqcup}b^*$ image) by OR operation between G frame in RGB color space and $b^*$ frame in $La^*b^*$ color space was useful for image segmentation of a plant canopy area. This study calculated a ratio of the infected area to the plant canopy and manually analyzed leaf color disorders through an image segmentation for Hue frame of a tomato plant image. For automatically analyzing plant leave disease, this study selected twenty-seven color patches on the calibration bars as the corresponding to leaf color disorders. These selected color patches could represent 97% of the infected area analyzed by the manual method. Using only ten color patches among twenty-seven ones could represent over 85% of the infected area. This paper showed a proposed machine vision system may be effective for evaluating various leaf color disorders of plants growing in a greenhouse.

• Research in Plant Disease
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• v.21 no.3
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• pp.180-185
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• 2015

In the present work, a pair of primers specific to Tomato yellow leaf curl virus (TYLCV) was designed to allow specific amplification of DNA fragments from any TYLCV isolates using an extensive alignment of the complete genome sequences of TYLCV isolates deposited in the GenBank database. A pair of primers which allows the specific amplification of tomato ${\beta}$-tubulin gene was also analyzed as an internal PCR control. A duplex PCR method with the developed primer sets showed that TYLCV could be directly detected from the leaf crude sap of infected tomato plants. In addition, our developed duplex PCR method could determine PCR errors for TYLCV diagnosis, suggesting that this duplex PCR method with the primer sets is a good tool for specific and sensitive TYLCV diagnosis. The developed duplex PCR method was further verified from tomato samples collected from some farms in Korea, suggesting that this developed PCR method is a simple and reliable tool for rapid and large-scale TYLCV detections in tomato plants.

• Research in Plant Disease
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• v.18 no.4
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• pp.298-303
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• 2012

Tomato yellow leaf curl virus (TYLCV), transmitted exclusively by the whitefly (Bemisia tabaci) in a circulative manner is one of the most important virus in tomato. Since the first report of TYLCV incidence in Korea in 2008, the virus has rapidly spread nationwide. TYLCV currently causes serious economic losses in tomato production in Korea. Early detection of TYLCV is one of the most important methods to allow rouging of infected tomato plants to minimize the spread of TYLCV disease. We have developed an ultra-rapid and sensitive real-time polymerase chain reaction (PCR) using a new designed real-time PCR system, GenSpectorTM TMC-1000 that is a small and portable real-time PCR machine requiring only a $5{\mu}l$ reaction volume on microchips. The new system provides ultra-high speed reaction (30 cycles in less than 15 minutes) and melting curve analysis for amplified TYLCV products. These results suggest that the short reaction time and ultra sensitivity of the GenSpector$^{TM}$-based real-time PCR technique is suitable for monitoring epidemics and pre-pandemic TYLCV disease. This is the first report for plant virus detection using an ultra-rapid real-time PCR system.

• The Plant Pathology Journal
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• v.32 no.4
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• pp.300-310
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• 2016

Pathogen infection in plants induces complex responses ranging from gene expression to metabolic processes in infected plants. In spite of many studies on biotic stress-related changes in host plants, little is known about the metabolic and phenotypic responses of the host plants to Pseudomonas cichorii infection based on image-based analysis. To investigate alterations in tomato plants according to disease severity, we inoculated plants with different cell densities of P. cichorii using dipping and syringe infiltration methods. High-dose inocula (${\geq}10^6cfu/ml$) induced evident necrotic lesions within one day that corresponded to bacterial growth in the infected tissues. Among the chlorophyll fluorescence parameters analyzed, changes in quantum yield of PSII (${\Phi}PSII$) and non-photochemical quenching (NPQ) preceded the appearance of visible symptoms, but maximum quantum efficiency of PSII ($F_v/F_m$) was altered well after symptom development. Visible/near infrared and chlorophyll fluorescence hyperspectral images detected changes before symptom appearance at low-density inoculation. The results of this study indicate that the P. cichorii infection severity can be detected by chlorophyll fluorescence assay and hyperspectral images prior to the onset of visible symptoms, indicating the feasibility of early detection of diseases. However, to detect disease development by hyperspectral imaging, more detailed protocols and analyses are necessary. Taken together, change in chlorophyll fluorescence is a good parameter for early detection of P. cichorii infection in tomato plants. In addition, image-based visualization of infection severity before visual damage appearance will contribute to effective management of plant diseases.

• Research in Plant Disease
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• v.24 no.4
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• pp.342-347
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• 2018

Bacterial canker caused by Clavibacter michiganensis is considered to be one of the most serious diseases, leading to economic damage to tomato worldwide. Diagnosis of the bacterial canker on tomato is known to be difficult because the causal pathogen is slow-growing on artificial media as well as causes latent infection in tomato. In this study, as a less time-consuming method, a specific primer set was newly designed for rapid detection of C. michiganensis. The method presented here is so simple, easy, and fast that it can be useful and practical in direct detection of the bacterial canker pathogen from tomato plants.

• Research in Plant Disease
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• v.24 no.4
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• pp.348-352
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• 2018

A multiplex reverse transcription-polymerase chain reaction (mRT-PCR) assay was developed for the detection of Clover yellow vein virus (ClYVV), Peanut mottle virus (PeMoV), and Tomato spotted wilt virus (TSWV), which were recently reported to infect soybean and azuki bean in Korea. Species-specific primer sets were designed for the detection of each virus, and their specificity and sensitivity were tested using mixed primer sets. From among the designed primer sets, two combinations were selected and further evaluated to estimate the detection limits of uniplex, duplex, and multiplex RT-PCR. The multiplex RT-PCR assay could be a useful tool for the field survey of plant viruses and the rapid detection of ClYVV, PeMoV, and TSWV in leguminous plants.