892 research outputs found

    Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

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    In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

    Appropriate use of endoscopy in the diagnosis and treatment of gastrointestinal diseases: up-to-date indications for primary care providers

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    The field of endoscopy has revolutionized the diagnosis and treatment of gastrointestinal (GI) diseases in recent years. Besides the ‘traditional’ endoscopic procedures (esophagogastroduodenoscopy, colonoscopy, flexible sigmoidoscopy, and endoscopic retrograde cholangiopancreatography), advances in imaging technology (endoscopic ultrasonography, wireless capsule endoscopy, and double balloon enteroscopy) have allowed GI specialists to detect and manage disorders throughout the digestive system. This article reviews various endoscopic procedures and provides up-to-date endoscopic indications based on the recommendations of American Society for Gastrointestinal Endoscopy and American Cancer Society for primary care providers in order to achieve high-quality and cost-effective care

    Performances of the LBP based algorithm over CNN models for detecting crops and weeds with similar morphologies

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    Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, ResNet-50 and InceptionV3. The experimental results on the “bccr-segset” dataset in the laboratory testbed setting show that the accuracy of CNN models with fine-tuned hyper-parameters is slightly higher than the k-FLBPCM method, while the accuracy of the k-FLBPCM algorithm is higher than the CNN models (except for VGG-16) for the more realistic “fieldtrip_can_weeds” dataset collected from real-world agricultural fields. However, the CNN models require a large amount of labelled samples for the training process. We conducted another experiment based on training with crop images at mature stages and testing at early stages. The k-FLBPCM method outperformed the state-of-the-art CNN models in recognizing small leaf shapes at early growth stages, with error rates an order of magnitude lower than CNN models for canola–radish (crop–weed) discrimination using a subset extracted from the “bccr-segset” dataset, and for the “mixed-plants” dataset. Moreover, the real-time weed–plant discrimination time attained with the k-FLBPCM algorithm is approximately 0.223 ms per image for the laboratory dataset and 0.346 ms per image for the field dataset, and this is an order of magnitude faster than that of CNN models

    IMPACT OF THE EVFTA AGREEMENT: A STUDY ON VIETNAM'S EXPORTED GOODS

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    Abstract The research aims to assess the quantitative impact of the Vietnam-European Union Free Trade Agreement (EVFTA) on Vietnam's export growth to the EU market. The study employs quantitative analysis using the SMART model with data on export turnover and scenarios of tariff reduction to 0% when EVFTA takes effect. Based on the export turnover data and necessary parameters, the analysis results show an increase in Vietnam's exports to the EU market when EVFTA becomes effective. As a result, the research proposes some implications to promote Vietnam's export activities to the EU in the future

    Application of HEC-HMS model and satellite precipitation products to restore runoff in Laigiang river basin in Vietnam

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    The Laigiang river basin in the South Central Coast of Vietnam plays an important role in the socio-economic development of Binhdinh Province. In recent decades, the region has experienced commonly flooding in vast areas. This research aims to simulate event-based rainfall-runoff modelling, a historical flood event in December 2016, by applying the HEC-HMS model and rainfall data from CHIRPS. The CHIRPS data is an acceptable potential data input of the hydrology model. These have been confirmed through reliable validation indexes: The peak flood flow rate of 2,542.6 m3/s corresponds to the flood frequency of 5%; NSE with the value at 0.95; R2 coefficient reached 0.87; PBIAS being around 0.45, and PFC being at 0.89. It shows better performance in the rainy season than in the dry season. Inclusive, the CHIRPS rainfall data set and the HEC model could be used for some operational purposes in weather forecasting, especially for flood warnings in river basins in the South Central Coast, Vietnam

    A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators

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    Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of crops and weeds was achieved using support vector machine with radial basis function kernel. By investigating optimal parameters, this method reached a classification accuracy of 98.63% with 4 classes in the bccr-segset dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method. Conclusions: The proposed method enhances the identification of crops and weeds with similar appearance and demonstrates its capabilities in real-time weed detection. © 2020 The Author(s) 2020

    The Impact of Social Media Marketing on Brand Awareness and Purchase Intention: Case Study of Vietnam's domestic fashion brands

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    The study aimed to examine the impact of social media marketing on brand awareness and purchase intention for Vietnamese domestic fashion brands. Quantitative research was conducted on 302 Vietnamese people of Generation Z. The questionnaire designed on Google forms was sent to research samples who were willing to participate. Research results determined the role and benefits of social media marketing in 2 aspects: (1) information about the brand of social media marketing on social networks and (2) brand engagement on social networks. Social media marketing has a positive impact on brand awareness and purchase intention of Vietnamese domestic fashion brands. In particular, brand information when communicating on social networks has a direct and positive impact on brand awareness and purchase intention. Brand engagement on social networks has a positive direct impact on brand awareness and a positive indirect impact on purchase intention through brand awareness. The research results show that Vietnamese domestic fashion brands do quite well in social media marketing, and are highly appreciated by the online community of generation Z in Vietnam. In the future, in order to improve brand awareness and purchase intention, Vietnamese domestic fashion brands need to pay attention to the brand information properties of social media marketing programs and need to invest more in brand engagement characteristics of social networks. Keywords: social media marketing, brand awareness, purchase intentio

    An Overview of Sustainable Ocean Resources for Socio-economic Development in Vietnam

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    Vietnam is one of the nation with long coastal with many valuable resources, which is the basis for the economy development from the ocean-based activities. This paper outlined the main maritime resources and their contribution to the socio-economic development in Vietnam. The raw data was collected from the readable sources such as General Statistics Office of Vietnam, Vietnam meteorological and meteorological data center
. as well as from the practical investigation. This raw data was processed and analyzed in order to have a general view on the majority marine resources in Vietnam. The results showed that there were 5 main resources (oil, fisheries, natural hydrate, green energy, and tourism) from the ocean, which significantly contribute to the economy development of Vietnam. The highest contribution of the oil value to the GDP was witnessed in 2011 with 26.6% of the GDP. While the value of the fisheries and tourism increased every year, the natural hydrate and energy from the ocean are still young areas, however, they come one step closer to the exploitation, which can greatly contribute to the Vietnam economy growth. With these valuable resources from the sea, Vietnam need to have reasonable policies and strategies for the management, exploitation, and export in order to able to effectively use of these resources for the sustainable economy development. Keywords: Ocean resources, sustainable development, fisheries, crude oil exploitation DOI: 10.7176/JRDM/69-05 Publication date:September 30th 202

    Determinants Influencing Entrepreneurial Intention in Hanoi, Vietnam

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    This research employed survey data from 204 students between two groups of  economics and technical majors in Hanoi city for assessing the impact levels of determinants on entrepreneurial intention. The results show that a number of determinants including Need for achievement, Self-efficacy, and Instrumental readiness have positive impacts on student’s entrepreneurial intention. Besides, this study is also to create a basis for  comparative students among different economics and technical majors, work exoperience, and gender. These findings are the basis to recommend policies and solutions to promote entrepreneurship movement in Vietnam. Keywords: Entrepreneurial intention, need for achievement, self-efficacy, instrumental readiness. DOI: 10.7176/EJBM/12-15-10 Publication date:May 31st 2020

    Research on chemical constituents, anti-bacterial and anti-cancer effects of components isolated from Zingiber officinale Roscoe from Vietnam

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    Ginger, a commonly used spice and medicinal herb, is an abundant source of bioactive compounds. However, the utilization of ginger in the pharmaceutical industry is still moderate and not commensurate with the potential of the Vietnamese horticulture industry, mainly due to a lack of information about the quality of input materials. In this study, we compared the volatile compounds of gingers collected from 13 provinces of Vietnam using GC/MS and GC-FID analysis to provide a basis for selecting and standardizing input materials. Furthermore, ginger essential oil from Ben Tre province of Vietnam exhibited significant antibacterial activity particularly in inhibiting Gram-positive bacteria, including S. aureus and S. epidermidis, with inhibition zones of 30.00 ± 1.41 and 24.67 ± 3.30 mm, respectively. However, no significant inhibition was observed against Gram-negative bacteria P. aeruginosa and E. coli. We also isolated 5 non-volatile compounds from ginger extract, namely 6-shogaol (1), quercetin (2), rutin (3), beta-sitosterol (4) and beta-sitosterol-3-O-beta-D-glucopyranoside (5). Among them, compounds 1–3 displayed cytotoxicity against Hep3B, SK-LU-1, MCF-7, SK-LU-1, SW480 and HepG2 tumour cell lines, with an IC50 values ranging between 62.7 ± 2.1 and 97.6 ± 1.1 ”M, using Ellipticine as a positive control. Compounds 4 and 5 showed cytotoxicity against Hep3B and HepG2 tumor cells, with the IC50 values ranging between 21.5 ± 5.1 and 46.9 ± 3.7 ”M but did not exhibit any significant cytotoxicity against SW480 and SK-LU-1 cells. Compound 4 also demonstrated middling cytotoxicity against the MCF7 cell line, with an IC50 value of 43.6 ± 5.1 ”M. These findings suggest further applications of Vietnamese ginger for the treatment of infectious and cancer-related diseases
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