• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.83-95
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• 2020

Being a dense and complex task of precisely detecting the weeds in practical crop field environment, previous approaches lack in terms of speed of processing image frames with accuracy. Although much of the attention has been given to classify the plants diseases but detecting crop weed issue remained in limelight. Previous approaches report to use fast algorithms but inference time is not even closer to real time, making them impractical solutions to be used in uncontrolled conditions. Therefore, we propose a detection model for the complex rice weed detection task. Experimental results show that inference time in our approach is reduced with a significant margin in weed detection task, making it practically deployable application in real conditions. The samples are collected at two different growth stages of rice and annotated manually

• Korean Journal of Weed Science
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• v.32 no.1
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• pp.10-16
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• 2012

This study was carried out to investigate the agronomic traits, comparison of weed characteristics and possibility of gene flow in 'vitamin A enforced GM rice' and the donor plant, 'Nagdong'. The GM rice was not significantly different agronomic traits compared to the donor plant, Nagdong. Weed population changes were investigated in the cultivation of the GM rice and the donor plant, Nagdong. Dominant weed species and their dry matter did not show the difference between GM rice and the donor plant, Nagdong in macro-GM crop field. Dominant weed species with the GM rice and the donor plant, Nagdong were Monochoria vaginalis, followed by Eleocharis kuroguwai, Echinochloa crus-galli and Lindernia procumbens. The detection of gene from the GM rice was done using PCR, gene flow can't be detected by weed species. Results of this study on the agronomic traits, weed characteristics and possibility of gene flow has elucidated that GM rice might not be different from the donor plant, Nagdong.

• Journal of Biosystems Engineering
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• v.31 no.1 s.114
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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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• v.27 no.2
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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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• v.27 no.1
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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.