• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.353-362
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• 1993

The purpose of this study is to develop a crop-row detector which can be applied to an automatic row following control for cultivators or thinning machines. In this report, a possibility of new crop-row detecting method was discussed. This detecting method consists of two principal means. One is the hardware means to convert the two dimensional crop-row vision to the compacted one dimensional information. The conversion is achieved by a color line sensor and a rotating mirror. In order to extract crop-row , R and G signals of RGB color system are used. The locations of two different points on the target row are detected by this means. Another is the software means to estimate the offset value and the heading angle between the detector and the target row which can be assumed as a straight line. As a result of discussion, it was concluded that this detecting method would be accurate enough for practical use.

• Toxicological Research
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• 제20권2호
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• pp.101-108
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• 2004

유전자변형작물의 이점과 잠재적 위해에 관한 다른 사회적 인식은 각국에서 다른 반응을 유발시켜왔다 한국을 포함한 많은 국가는 새로운 규제를 제정하기 위해 부심하고 있다 한국은 최근에 3% 이상의 유전자변형작물 혼입을 포함하는 모든 식품에 표시제를 실시하였다. 유전자변형작물 혼입을 신속하고 간편하게 검출하는 방법의 하나는 PCR에 의한 도입 DNA의 증폭이다 이 목적을 위한 많은 PCR kit가 시판되고 있어, 본 연구는 이들 상업화된 유전자변형작물 검출 kit의 성능을 시험하였다. 그 결과 이들 15개 kit 중 6개는 안정성, 재현성 및 검출 한계의 측면에서 제작사 스스로 제시한 요구조건도 충족하지 못하여 이들 kit의 개발 및 생산 단계에서 품질관리에 문제점이 있음을 시사하였다 본 시험은 또한 duplex와 triplex검출 kit가 단일 PCR 반응에서 명백한 검출을 보장할지라도, monoplex 검출 kit의 검출 능력이 가장 높다는 것을 시사하였다. 또한, kit들은 콩과 옥수수 사이에서 다른 검출 한계를 보였다. 본 연구의 결과는 GM 작물의 재배, 국가간 이동, GM 작물을 사용한 식품 생산 과정의 모니터링 뿐만 아니라 GM작물과 관련한 정부의 법규를 준수하기 위한 GM작물의 혼입의 건전한 과학적 추적체계의 개발에 유용할 것이라 사료된다.

• 식물병연구
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• 제23권1호
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• pp.65-68
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• 2017

2단계의 nested PCR 방법을 이용하여 보리 및 벼 재배 토양에서 Barley yellow mosaic virus (BaYMV)를 검출하였다. BaYMV 분절 RNA1 외피단백질 영역의 특이 프라이머로 1차 PCR을 하고 내부서열로부터 작성된 프라이머로 2차 PCR을 실시하여 확보된 372 bp의 PCR 산물이 BaYMV 외피단백질 영역과 98%-100% 염기서열이 일치하여 BaYMV를 검출할 수 있음을 확인하였다. 이 결과는 토양으로부터 BaYMV 검출에 관한 최초의 보고이며 토양전염성 바이러스의 정확한 진단과 예찰에 적용될 수 있을 것으로 생각한다.

• The Plant Pathology Journal
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• 제36권5호
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• pp.497-502
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• 2020

Barley yellow dwarf virus (BYDV) is an economically important plant pathogen that causes stunted growth, delayed heading, leaf yellowing, and purple leaf tip, thereby reducing the yields of cereal crops worldwide. In the present study, a reverse transcription recombinase polymerase amplification (RT-RPA) assay was developed for the detection of BYDV in oat leaf samples. The RT-RPA assay involved incubation at an isothermal temperature (42℃) and could be performed rapidly in 5 min. In addition, no cross-reactivity was observed to occur with other cereal-infecting viruses, and the method was 100 times more sensitive than conventional reverse transcription polymerase chain reaction. Furthermore, the assay was validated for the detection of BYDV in both field-collected oat leaves and viruliferous aphids. Thus, the RT-RPA assay developed in the present study represents a simple, rapid, sensitive, and reliable method for detecting BYDV in oats.

• The Plant Pathology Journal
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• 제38권2호
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• pp.159-166
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• 2022

Barley yellow dwarf virus (BYDV) has been a major viral pathogen causing significant losses of cereal crops including oats worldwide. It spreads naturally through aphids, and a rapid, specific, and reliable diagnostic method is imperative for disease monitoring and management. Here, we established a rapid and reliable method for isothermal reverse transcription recombinase polymerase amplification (RT-RPA) combined with a lateral flow strips (LFS) assay for the detection of BYDV-infected oat samples based on the conserved sequences of the BYDV coat protein gene. Specific primers and a probe for RT-RPA reacted and optimally incubated at 42℃ for 10 min, and the end-labeled amplification products were visualized on LFS within 10 min. The RT-RPA-LFS assay showed no cross-reactivity with other major cereal viruses, including barley mild mosaic virus, barley yellow mosaic virus, and rice black streaked dwarf virus, indicating high specificity of the assay. The sensitivity of the RT-RPA-LFS assay was similar to that of reverse transcription polymerase chain reaction, and it was successfully validated to detect BYDV in oat samples from six different regions and in individual aphids. These results confirm the outstanding potential of the RT-RPA-LFS assay for rapid detection of BYDV.

• 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.

• Journal of Forest and Environmental Science
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• 제40권1호
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• 한국정보전자통신기술학회논문지
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• 제16권4호
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• pp.210-219
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• 2023

농촌 인구의 감소와 고령화로 인한 노동력 부족, 비닐하우스 내의 악화된 환경과 위험에 따른 사망 사례가 발생하고 있다. 이에 따라, 비닐하우스에서의 작물 재배와 병해 검출을 자동화하여 인력 손실을 막는 시스템이 필요하다. 본 논문에서는 비닐하우스에서 작물의 병해를 검출하기 위해 객체 검출 기반의 모델을 활용한다. 또한, 클라우드에서 인공지능 모델의 환경을 구성하여 안정성을 확보한다. 제안하는 시스템은 비닐하우스 내에서 촬영한 영상을 데이터베이스에 저장하고, 클라우드에서 영상을 다운로드한 후 Yolo-v4를 기반으로 추론한 검출 결과를 JSON 파일로 생성한다. 이 파일을 분석하여 데이터베이스로 전송하여 저장한다. 실험 결과로 객체 검출을 통한 병해 감지는 비닐하우스와 같은 실제 환경에서 높은 성능을 나타냄을 확인할 수 있고 데이터베이스를 통하여 효율적인 모니터링이 가능함을 확인하였다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• IEIE Transactions on Smart Processing and Computing
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• 제4권4호
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• pp.265-271
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• 2015

This study proposes an algorithm for recognizing apple trees in images and detecting apples to measure the number of apples on the trees. The proposed algorithm explores whether there are apple trees or not based on the number of image block-unit edges, and then it detects apple areas. In order to extract colors appropriate for apple areas, the CIE $L^*a^*b^*$ color space is used. In order to extract apple characteristics strong against illumination changes, modified census transform (MCT) is used. Then, using the AdaBoost learning algorithm, characteristics data on the apples are learned and generated. With the generated data, the detection of apple areas is made. The proposed algorithm has a higher detection rate than existing pixel-based image processing algorithms and minimizes false detection.