• 사물인터넷융복합논문지
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• 제6권1호
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• pp.83-95
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• 2020

실제 복잡다난한 농작물 밭 환경에서 잡초를 정밀하게 검출하는 것은 이전의 접근방법들로는 이미지 프레임을 정확하게 처리하는 속도 면에서 부족했다. 식물의 질병 분류 문제가 중요시 되는 상황에서 특히 작물의 잡초 문제는 큰 화제가 되고 있다. 이전의 접근방식들은 빠른 알고리즘을 사용하지만 추론 시간이 실시간에 가깝지 않아 통제되지 않은 조건에서 비현실적인 해결책이 된다. 따라서, 복잡한 벼 잡초 검출 과제에 대한 탐지 모델을 제안한다. 실험 결과에 따르면, 우리의 접근 방식의 추론 시간은 잡초 검출 과제에서 상당한 시간절약을 보여준다. 실제 조건에서 실제로 적용할 수 있는 것으로 나타난다. 주어진 예시들은 쌀의 두 가지 성장 단계에서 수집되었고 직접 주석을 달았다.

• 한국잡초학회지
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• 제32권1호
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• pp.10-16
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• 2012

이 연구는 비타민 A 강화벼(GM 벼)와 모품종인 낙동 및 일반품종을 대조로 농업적인 생육특성과 잡초를 대상으로 유전자 전이 정도를 조사하였다. GM 벼의 농업적인 특성에서 모품종인 낙동과 차이를 보이지 않았으며, 우점 잡초군과 건물중에서 유의성이 보이지 않았다. GM 벼와 모품종인 낙동 재배구의 우점 잡초는 물달개비, 올방개, 좀개구리밥, 물피 등의 10여 종이었다. 잡초의 유전자 전이 정도를 PCR 분석 결과, GM 벼와 낙동 그리고 우점잡초 8종에서 유전자 전이가 나타나지 않았다. 그러므로 비타민 A 강화벼의 화분이 비래하여 비표적 다른 품종의 벼 또는 주변 잡초에 유전자 이동이 일어나 외래유전자가 함유된 잡초가 출현하는 경우는 거의 없을 것으로 사료된다.

• Journal of Biosystems Engineering
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• 제31권1호
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• pp.59-64
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• 2006

Application of herbicide for rice cropping is inevitable but notorious for its side effect of environmental pollution. Precision fanning will be one of important tools for the least input and sustainable fanning and could be achieved by implementation of the variable rating technology. If a device to detect weeds in rice field is available, herbicide could be applied only to the places where it is needed by the manner of the variable rating technology. The study was carried out to develop an algorithm of image processing to detect weeds in rice field using a machine vision system of multi-spectral digital images. A series of multi-spectral rice field picture of 560, 680 and 800 nm of center wavelengths were acquired from the 27th day to the 39th day after transplanting in the ineffective tillering stage of a rice growing period. A discrimination model to distinguish pixels of weeds from those of rice plant and weed image was developed. The model was proved as having accuracies of 83.6% and 58.9% for identifying the rice plant and the weed, respectively. The model was used in the algorithm to differentiate weed images from mingled images of rice plant and weed in a frame of rice field picture. The developed algorithm was tested with the acquired rice field pictures and resulted that 82.7%, 11.9% and 5.4% of weeds in the pictures were noted as the correctly detected, the undetected and the misclassified as rice, respectively, and 81.9% and 18.0% of rice plants in the pictures were marked as the correctly detected and the misclassified as weed, respectively.

• Journal of Biosystems Engineering
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• 제27권2호
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• pp.133-142
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• 2002

This is a fundamental study to develop a sensor to detect weeds in paddy field using machine vision adopted spectralphotometric technique in order to use the sensor to spread herbicide selectively. A set of spectral reflectance data was collected from dry and wet soil and leaves of rice and 6 kinds of weed to select desirable wavelengths to classify soil, rice and weeds. Stepwise variable selection method of discriminant analysis was applied to the data set and wavelengths of 680 and 802 m were selected to distinguish plants (including rice and weeds) from dry and wet soil, respectively. And wavelengths of 580 and 680 nm were selected to classify rice and weeds by the same method. Validity of the wavelengths to distinguish the plants from soil was tested by cross-validation test with built discriminant function to prove that all of soil and plants were classified correctly without any failure. Validity of the wavelengths for classification of rice and weeds was tested by the same method and the test resulted that 98% of rice and 83% of weeds were classified correctly. Feasibility of CCD color camera to detect weeds in paddy field was tested with the spectral reflectance data by the same statistical method as above. Central wavelengths of RGB frame of color camera were tried as tile effective wavelengths to distingush plants from soil and weeds from plants. The trial resulted that 100% and 94% of plants in dry soil and wet soil, respectively, were classified correctly by the central wavelength or R frame only, and 95% of rice and 85% of weeds were classified correctly by the central wavelengths of RGB frames. As a result, it was concluded that CCD color camera has good potential to be used to detect weeds in paddy field.

• The Plant Pathology Journal
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• 제27권1호
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• pp.37-43
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• 2011

Rice stripe virus (RSV) is one of serious epidemic pathogens for rice species grown in many Asian countries. Therefore, it is necessary to produce a diagnostic detection kit applicable in fields for RSV detection. In this study, RSV proteins that were derived from recombinant proteins and synthetic polypeptides as antigens were generated and were raised in rabbits for antiserum production. Among seven proteins in RSV, genes that code for NCP and NS3 proteins were cloned and subcloned into vector carrying His-tag protein and were expressed in E. coli. Of two recombinant proteins, only anti-NCP displayed stable hybridization signals in western blot analysis. Alternately, synthetic RSV polypeptides for CP, NCP, NS3 and NSvc4 we also generated and only antibodies against CP and NCP were very effective to detect RSV in both RSV infected rice and weed plants. However, antibodies against NS3 and NSvc4 showed weak specific bands as well as strong non-specific background due to the difference of viral proteins produced in the infected leaves. In summary, the antibodies generated against RSV proteins produced in this study will be useful for various assays such as for RSV diagnostic detection, immunoprecipitation, protein purification, and western blot analysis.