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

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

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

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

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

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

Effect of Pharmacist-Led Interventions on Physicians' Prescribing for Pediatric Outpatients

BACKGROUND: Children are at high risk of drug-related problems, increased risk of treatment failures, and high treatment costs. We aimed to evaluate the effect of pharmacist-led interventions on physicians' prescribing for pediatric outpatients. METHODS: A prospective study with pre- and post-intervention measurement assessment was conducted to collect pediatric outpatients' prescriptions during the pre-intervention period (January 2020) and post-intervention (August 2020) at a children's hospital in Vietnam. Drug-related problems were identified and categorized according to Pharmaceutical Care Network Europe (PCNE), version 9.1. The intervention program was developed based on the results of pre-intervention observations. After the intervention, prescriptions were evaluated. Statistical tests were used to compare the proportions of drug-related problems before and after the intervention and to identify factors related to drug-related problems. RESULTS: There were 2788 out of 4218 (66.1%) prescriptions with at least one drug-related problem before the intervention. Of these drug-related problems, the most common was inappropriate timing of administration and incorrect dosage (36.1% and 35.6%, respectively). After the intervention, the percentage of prescriptions with at least one drug-related problem was 45.5% (p < 0.001). Most of the drug-related problem types decreased significantly (p < 0.05). The binary logistic regression analysis results showed that in addition to pharmacists' intervention, patients' gender, primary disease, comorbidity status, and the total number of drugs prescribed were also factors related to drug-related problems. CONCLUSIONS: Drug-related problems in pediatric outpatients were quite common. Pharmacists' intervention helped to improve the prevalence and types of drug-related problems

The relationship between brand equity and intention to buy: the case of convenience stores

The research aims to identify the components of brand equity that affect consumer purchasing intentions and measure the effect of brand equity components on the intention of consumer purchases at the convenience stores in Ho Chi Minh City. The authors conduct the group discussions, expert discussion, and then analyze data from 200 valid questionnaires with four components of brand equity, namely perceived quality, brand loyalty, brand association, brand awareness. The results of Exploratory Factor Analysis (EFA) show that all four elements have a positive effect on the customer' intention to purchase in the convenience store of Ho Chi Minh City. In particular, the brand association factor has the strongest influence, followed by perceived quality, brand awareness, and brand loyalty. This research contributes that the results confirm the theory of Aaker (1991), Brown and Stayman (1992), Cobb-Walgren et al. (1995), MacKenzie (1986) in the new context of convenience stores in Ho Chi Minh City, Vietnam. Besides, the study gives some recommendations to help convenience stores improve the elements of brand equity and it, to enhance attraction for consumers

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

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 µM, 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 µM 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 µM. These findings suggest further applications of Vietnamese ginger for the treatment of infectious and cancer-related diseases

Evaluation of Luminex xTAG Gastrointestinal Pathogen Panel Assay for Detection of Multiple Diarrheal Pathogens in Fecal Samples in Vietnam.

Diarrheal disease is a complex syndrome that remains a leading cause of global childhood morbidity and mortality. The diagnosis of enteric pathogens in a timely and precise manner is important for making treatment decisions and informing public health policy, but accurate diagnosis is a major challenge in industrializing countries. Multiplex molecular diagnostic techniques may represent a significant improvement over classical approaches. We evaluated the Luminex xTAG gastrointestinal pathogen panel (GPP) assay for the detection of common enteric bacterial and viral pathogens in Vietnam. Microbiological culture and real-time PCR were used as gold standards. The tests were performed on 479 stool samples collected from people admitted to the hospital for diarrheal disease throughout Vietnam. Sensitivity and specificity were calculated for the xTAG GPP for the seven principal diarrheal etiologies. The sensitivity and specificity for the xTAG GPP were >88% for Shigellaspp.,Campylobacterspp., rotavirus, norovirus genotype 1/2 (GI/GII), and adenovirus compared to those of microbiological culture and/or real-time PCR. However, the specificity was low (∼60%) for Salmonella species. Additionally, a number of important pathogens that are not identified in routine hospital procedures in this setting, such as Cryptosporidiumspp. and Clostridium difficile, were detected with the GPP. The use of the Luminex xTAG GPP for the detection of enteric pathogens in settings, like Vietnam, would dramatically improve the diagnostic accuracy and capacity of hospital laboratories, allowing for timely and appropriate therapy decisions and a wider understanding of the epidemiology of pathogens associated with severe diarrheal disease in low-resource settings

Disinfection performance of an ultraviolet lamp: a CFD investigation

Ultraviolet (UV)-based devices have shown their effectiveness on various germicidal purposes. To serve their design optimisation, the disinfection effectiveness of a vertically cylindrical UV lamp, whose wattage ranges from P = 30 − 100 W, is numerically investigated in this work. The UV radiation is solved by the Finite Volume Method together with the Discrete Ordinates model. Various results for the UV intensity and its bactericidal effects against several popular virus types, i.e., Corona-SARS, Herpes (type 2), and HIV, are reported and analysed in detail. Results show that the UV irradiance is greatly dependent on the lamp power. Additionally, it is indicated that the higher the lamp wattage employed, the larger the bactericidal rate is observed, resulting in the greater effectiveness of the UV disinfection process. Nevertheless, the wattage of P ≤ 100W is determined to be insufficient for an effective disinfection performance in a whole room; higher values of power must hence be considered in case intensive sterilization is required. Furthermore, the germicidal effect gets reduced with the viruses less sensitive to UV rays, e.g, the bactericidal rate against the HIV virus is only ∼8.98% at the surrounding walls

Multidrug resistance plasmids underlie clonal expansions and international spread of Salmonella enterica serotype 1,4,[5],12:i:- ST34 in Southeast Asia

Salmonella enterica serotype 1,4,[5],12:i:- (Typhimurium monophasic variant) of sequence type (ST) 34 has emerged as the predominant pandemic genotype in recent decades. Despite increasing reports of resistance to antimicrobials in Southeast Asia, Salmonella ST34 population structure and evolution remained understudied in the region. Here we performed detailed genomic investigations on 454 ST34 genomes collected from Vietnam and diverse geographical sources to elucidate the pathogen’s epidemiology, evolution and antimicrobial resistance. We showed that ST34 has been introduced into Vietnam in at least nine occasions since 2000, forming five co-circulating major clones responsible for paediatric diarrhoea and bloodstream infection. Most expansion events were associated with acquisitions of large multidrug resistance plasmids of IncHI2 or IncA/C2. Particularly, the self-conjugative IncA/C2 pST34VN2 (co-transferring blaCTX-M-55, mcr-3.1, and qnrS1) underlies local expansion and intercontinental spread in two separate ST34 clones. At the global scale, Southeast Asia was identified as a potential hub for the emergence and dissemination of multidrug resistant Salmonella ST34, and mutation analysis suggests of selection in antimicrobial responses and key virulence factors

PRE-ENGINEERED (PACKAGE/AND OR ON-SITE) WASTEWATER TREATMENT PLANTS

Joint Research on Environmental Science and Technology for the Eart