• Molecules and Cells
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• 제45권7호
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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.

• 한국멀티미디어학회논문지
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• 제23권10호
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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.

• 식물병연구
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• 제17권3호
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• pp.376-380
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• 2011

P. syringae pv. tomato는 토마토에서 bacterial speck병을 일으키는 종자전염 세균으로, 감수성 품종에서 주로 발병하여 경제적으로 큰 손실을 입힌다. 따라서 P. syringae pv. tomato는 한국을 비롯한 많은 나라에서 식물 검역대상 세균으로 지정하여 관리되고 있다. 본 연구에서 우리는 토마토 종자로부터 PCR을 이용하여 Pst를 검출할 수 있는 방법을 개발하였다. P. syringae pv. tomato의 hrpZ 유전자에서 특이적인 프라이머를 개발하였다. 개발된 프라이머는 P. syringae pv. tomato에서만 501 bp 크기의 특이적 DNA를 증폭하였으며, P. syringae pv. glycinea, P. syringae pv. maculicola, P. syringae pv. atropurpurea, P. syringae pv. morsprunorum와 같은 다른 토마토 세균병원균과 P. syringae pathovar 균주들에서는 증폭되지 않았다. Nested PCR 프라이머를 이용한 PCR에서도 오직 P. syringae pv. tomato에서만 119 bp 크기의 특이적 DNA가 증폭되었고, 토마토 종자에서 P. syringae pv. tomato을 정확하고 민감하게 검출하였다. 본 연구는 현재까지 사용되고 있는 Pst의 검출방법의 민감도를 비교한 최초의 보고로 본 연구에서 개발된 PCR방법들은 토마토 종자에서 Pst을 검출하는 매우 유용한 방법으로 생각된다.

• 품질경영학회지
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• 제48권4호
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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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• 제33권6호
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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.

• 식물병연구
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• 제21권3호
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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.

• 식물병연구
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• 제18권4호
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• pp.298-303
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• 2012

토마토황화잎말림바이러스(Tomato yellow leaf curl virus; TYLCV)는 온실가루이(Bemisia tabaci)에 의해서 영속전염되는 DNA 바이러스로 토마토에 발생하는 가장 중요한 병 중 하나이다. 우리나라에서 TYLCV는 2008년 최초로 보고된 이래 급속하게 전국적으로 확산되어 토마토 생산에 심각한 경제적 손실을 일으키고 있다. 토마토 생산과정에서 TYLCV의 확산을 최소화하기 위해 바이러스의 조기진단이 매우 중요하다. 본 연구에서는 바이러스의 신속진단을 위해 초고속 정밀 PCR 진단기술을 개발하였으며, 이는 마이크로칩을 기반으로 하여 $5{\mu}l$의 시료만으로 PCR을 수행할 수 있도록 고안된, 휴대가 가능할 정도의 소형 GenSpector$^{TM}$ TMC-1000 PCR 기기를 이용한 새로운 기술이다. 본 연구에서 개발된 초고속 정량 PCR을 이용하였을 때 TYLCV 진단을 위한 30 cycle의 PCR과 용융점분석(melting curve analysis)에 15분 이내의 시간이 소요되었으며, GenSpector$^{TM}$ TMC-1000 PCR을 이용한 초고속 정밀진단 기술은 향후 TYLCV의 대발생을 모니터링하는데 유용하게 사용될 수 있을 것으로 생각한다. 본 연구결과는 GenSpector$^{TM}$ TMC-1000 PCR기반의 초고속정량 PCR 기술을 이용한 식물 바이러스의 진단기술 개발로는 최초의 보고이다.

• The Plant Pathology Journal
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• 제32권4호
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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.

• 식물병연구
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• 제24권4호
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• pp.342-347
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• 2018

Clavibacter michiganensis는 토마토에 궤양병을 일으키는 식물병원성 세균으로 인공배지에서 자라는 속도가 매우 느리기 때문에 감염조직으로부터 병원균을 분리 배양하는 방법을 통해서는 진단하기가 쉽지 않다. 또한 토마토 궤양병균은 식물체 내에서 오랜 잠복기를 거친 후에 병징을 나타내기 때문에 방제하기 어려운 세균병 중에 하나이므로 발병 시 신속한 진단을 통해 빠른 방제가 이루어져야 한다. 본 연구에서는 토마토 궤양병균의 검출을 위한 특이 프라이머를 제작함으로써 감염 식물체의 direct PCR을 통해 토마토 궤양병에 대한 빠르고 정확한 진단이 가능하도록 하였다. 새로 개발된 CmmF와 CmmR 프라이머 세트로 PCR을 수행했을 때, 토마토 궤양병균의 16-23S ribosomal RNA intergenic spacer 영역에서 약 165 bp의 단일 밴드가 특이적으로 증폭되었다. 반면에 토마토 궤양병균과 유연관계에 있는 고추 궤양병균이나 다른 Clavibacter 종 세균에서는 전혀 증폭되지 않는 것을 확인할 수 있었다. 이 방법은 감염 식물체로부터 DNA를 추출하지 않더라도 감염조직의 즙액에서 바로 토마토 궤양병균의 DNA 증폭이 가능하고 총 진단시간을 줄일 수 있다는 이점이 있기 때문에 토마토 궤양병의 진단에 유용하게 사용될 수 있을 것으로 판단된다.

• 식물병연구
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• 제24권4호
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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.