• 한국작물학회:학술대회논문집
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• 한국작물학회 2023년도 춘계학술대회
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• pp.117-117
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• 2023

• 한국작물학회:학술대회논문집
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• 한국작물학회 2023년도 춘계학술대회
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• pp.171-171
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• 2023

• 한국작물학회:학술대회논문집
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• 한국작물학회 2021년도 추계학술대회
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• pp.115-115
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• 2021

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 춘계학술대회
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• pp.22-22
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• 2022

• 한국작물학회:학술대회논문집
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• 한국작물학회 2021년도 춘계학술대회
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• pp.113-113
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• 2021

• 한국약용작물학회:학술대회논문집
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• 한국약용작물학회 2011년도 추계학술발표회
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• pp.154-155
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• 2011

• 식물병연구
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• 제17권1호
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• pp.52-58
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• 2011

우리나라에서 주로 재배되는 십자화과 작물(상추, 콜라비, 무, 배추, 양배추)의 종자 전염 병원균 중에서 세균성 병원균 Xanthomonns campestris pv. campestris(Xcc)와 바이러스 병원균 Lettuce Mosaic Virus(LMV)를 종자에서 동시검출하기 위한 One-step multiplex RT-PCR을 개발하였다. 각각의 병원균을 특이적으로 증폭시키는 병원균 검출용 프라이머 2종(Xcc-F/R, LMV-F/R)을 primerblast 프로그램을 이용하여 제작하였고 이들 프라이머 세트는 프라이머간 또는 병원균 cDNA간의 간섭없이 특이적으로 타겟 병원균만을 검출하였다. PCR을 이용한 병원균의 검출 최소 민감도는 1 ng이었다. 십자화과 작물중에서 유통 중인 콜라비 10품종, 상추 50품종, 무 20품종, 배추 20품종 그리고 양배추 20품종에 대한 종자 감염 병원균 검출을 위한 One-step multiplex RT-PCR 수행 결과, LMV는 전체 120품종 중에서 39품종에서 검출되었고, Xcc는 2개 품종에서 검출되었다. 그리고 50품종의 상추 종자 시료 중에 8품종의 시료에서 LMV와 Xcc가 동시 검출되었다.

• 한국연초학회지
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• 제36권1호
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• pp.26-33
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• 2014

Simultaneous determination of crop protection agents(CPAs) in food are done with multi-residue methods, which are composed of sample clean-up, concentration, chromatographic separation and detection. Stir Bar Sorptive Extraction(SBSE) technique is used for sample preparation of various analytes in several fields. The aim of this study was to develop a sensitive and fast method based on SBSE followed by thermal desorption - gas chromatography - mass spectrometry(TD - GC/MS) to determine CPAs in tobacco sample. For the analysis of tobacco sample prior to the SBSE method, solvent extraction or ultrasound-assisted solvent extraction was performed. methanol was used as the extraction solvent. The extract was then diluted with water. Finally, the sample was subjected to SBSE. A method for fast screening of crop protection agents in tobacco using SBSE-TD - GC/MS has been developed. About 17 CPAs including organochlorine, organophosphorous and others were identified and quantified. This method showed good linearity and high sensitivity for most of the target CPAs. The method was applied to the determination of CPAs at ng/mL levels in tobacco sample. This method is simple, rapid and may be applied in detection of other components.

• Journal of Biosystems Engineering
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• 제41권3호
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• pp.263-272
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• 2016

Purpose: Pulse crop damage due to wild birds is a serious problem, to the extent that the rate of damage during the period of time between seeding and the stage of cotyledon reaches 45.4% on average. This study investigated a method of fundamentally blocking birds from eating crops by conducting vinyl mulching after seeding and identifying the growing locations for beans to perform punching. Methods: Infrared (IR) sensors that could measure the temperature without contact were used to recognize the locations of soybean cotyledons below vinyl mulch. To expand the measurable range, 10 IR sensors were arranged in a linear array. A sliding mechanical device was used to reconstruct the two-dimensional spatial variance information of targets. Spatial interpolation was applied to the two-dimensional temperature distribution information measured in real time to improve the resolution of the bean coleoptile locations. The temperature distributions above the vinyl mulch for five species of soybeans over a period of six days from the appearance of the cotyledon stage were analyzed. Results: During the experimental period, cases where bean cotyledons did and did not come into contact with the bottom of the vinyl mulch were both observed, and depended on the degree of growth of the bean cotyledons. Although the locations of bean cotyledons could be estimated through temperature distribution analyses in cases where they came into contact with the bottom of the vinyl mulch, this estimation showed somewhat large errors according to the time that had passed after the cotyledon stage. The detection results were similar for similar types of crops. Thus, this method could be applied to crops with similar growth patterns. According to the results of 360 experiments that were conducted (five species of bean ${\times}$ six days ${\times}$ four speed levels ${\times}$ three repetitions), the location detection performance had an accuracy of 36.9%, and the range of location errors was 0-4.9 cm (RMSE = 3.1 cm). During a period of 3-5 days after the cotyledon stage, the location detection performance had an accuracy of 59% (RMSE = 3.9 cm). Conclusions: In the present study, to fundamentally solve the problem of damage to beans from birds in the early stage after seeding, a working method was proposed in which punching is carried out after seeding, thereby breaking away from the existing method in which seeding is carried out after punching. Methods for the accurate detection of soybean growing locations were studied to allow punching to promote the continuous growth of soybeans that had reached the cotyledon stage. Through experiments using multiple IR sensors and a sliding mechanical device, it was found that the locations of the crop could be partially identified 3-5 days after reaching the cotyledon stage regardless of the kind of pulse crop. It can be concluded that additional studies of robust detection methods considering environmental factors and factors for crop growth are necessary.

• 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.