•A contrast stretching technique is proposed to enhance the contrast of infected region.•Construction of a codebook using an improved texture, color, and geometric features.•Implement a feature ...selection technique based on PCA, skewness, and entropy.•Preparing the database of diseases images for citrus leaves.
In agriculture, plant diseases are primarily responsible for the reduction in production which causes economic losses. In plants, citrus is used as a major source of nutrients like vitamin C throughout the world. However, ‘Citrus’ diseases badly effect the production and quality of citrus fruits. From last decade, the computer vision and image processing techniques have been widely used for detection and classification of diseases in plants. In this article, we propose a hybrid method for detection and classification of diseases in citrus plants. The proposed method consists of two primary phases; (a) detection of lesion spot on the citrus fruits and leaves; (b) classification of citrus diseases. The citrus lesion spots are extracted by an optimized weighted segmentation method, which is performed on an enhanced input image. Then, color, texture, and geometric features are fused in a codebook. Furthermore, the best features are selected by implementing a hybrid feature selection method, which consists of PCA score, entropy, and skewness-based covariance vector. The selected features are fed to Multi-Class Support Vector Machine (M-SVM) for final citrus disease classification. The proposed technique is tested on Citrus Disease Image Gallery Dataset, Combined dataset (Plant Village and Citrus Images Database of Infested with Scale), and our own collected images database. We used these datasets for detection and classification of citrus diseases namely anthracnose, black spot, canker, scab, greening, and melanose. The proposed technique outperforms the existing methods and achieves 97% classification accuracy on citrus disease image gallery dataset, 89% on combined dataset and 90.4% on our local dataset.
Heat transportation advancement in liquid is a crucial work which can be easily obtained via utilization of hybrid nanoparticles. Therefore, main concern of the current article is to address the ...entropy aspects in Dracy-Forchheimer flow of hybrid nanomaterials (kerosene oil, SWCNT and MWCNT). The novel features of Xue's which is modified and implemented for flow physical empirical relations (thermal conductivity, density and specific heat). Furthermore, entropy concept is utilized to estimate the frame of analysis disorderness. The transformed expressions have been computed through numerical scheme after conversion to ODEs by dimensionless variables. Graphical illustrations and tables are made to investigate the effects of noteworthy embedding variables on various distributions. Moreover, detailed analysis for temperature and velocity gradients against pertinent parameters at the surface is provided. Obtained findings reflect that temperature upgraded for both nanoliquid and hybrid nanoliquid by rising Eckert number and heat source parameter, whereas an opposite features is pointed out for gradient of temperature. It is also perceived that skin friction escalates for inertia coefficient, rotation parameter and porosity parameter. There is remarkable increase for SWCNT-MWCNT/kerosene oil hybrid nanofluid when compared with SWCNT/kerosene oil nanoliquid. Overall, hybrid nano phase has an incredible effect in comparison to nanomaterials.
Main concern of current research is to develop a novel mathematical model for stagnation-point flow of magnetohydrodynamic (MHD) Prandtl-Eyring fluid over a stretchable cylinder. The thermal ...radiation and convective boundary condition are also incorporated. The modeled partial differential equations (PDEs) with associative boundary conditions are deduced into coupled non-linear ordinary differential equations (ODEs) by utilizing proper similarity transformations. The deduced dimensionless set of ODEs are solved numerically via shooting method. Behavior of controlling parameters on the fluid velocity, temperature fields as well as skin friction and Nusselt number are highlighted through graphs. Outcome declared that dimensionless fluid temperature boosts up for both the radiation parameter and Biot number. It is also revealed that the magnitude of both heat transfer rate and skin friction enhance for higher estimation of curvature parameter. Furthermore, comparative analysis between present and previous reports are provided for some specific cases to verify the obtained results.
Modern research has revealed that dietary consumption of flavonoids and flavonoids-rich foods significantly improve cognitive capabilities, inhibit or delay the senescence process and related ...neurodegenerative disorders including Alzheimer's disease (AD). The flavonoids rich foods such as green tea, cocoa, blue berry and other foods improve the various states of cognitive dysfunction, AD and dementia-like pathological alterations in different animal models. The mechanisms of flavonoids have been shown to be mediated through the inhibition of cholinesterases including acetylcholinesterase (AChE), and butyrylcholinesterase (BChE), β-secretase (BACE1), free radicals and modulation of signaling pathways, that are implicated in cognitive and neuroprotective functions. Flavonoids interact with various signaling protein pathways like ERK and PI3-kinase/Akt and modulate their actions, thereby leading to beneficial neuroprotective effects. Moreover, they enhance vascular blood flow and instigate neurogenesis particularly in the hippocampus. Flavonoids also hamper the progression of pathological symptoms of neurodegenerative diseases by inhibiting neuronal apoptosis induced by neurotoxic substances including free radicals and β-amyloid proteins (Aβ). All these protective mechanisms contribute to the maintenance of number, quality of neurons and their synaptic connectivity in the brain. Thus flavonoids can thwart the progression of age-related disorders and can be a potential source for the design and development of new drugs effective in cognitive disorders.
We investigate the hybrid nanofluid convection transportation within a permeable medium in appearance of an externally applied magnetic force by modeling it through control volume-based finite ...element method. To employ the permeability effect terms, one term was added in momentum and radiation was involved to show the diverse states of nanoparticles. The impact of Darcy and Rayleigh as well as magnetic and radiation parameters on the nanofluid performance is investigated and presented. We observe that the conduction mode enhances with higher values of Ha. Nusselt number Nu augments with the consideration of radiation source term. The addition of Lorentz force makes the conduction mode more sensible. The agreement with the published results makes us more confident about the validity of our numerical computations.
•Manual identification of different fish species is a difficult task.•We proposed a framework based on the CNN for fish species identification.•The proposed CNN architecture contains 32 deep ...layers.•We developed a data set termed as Fish-Pak from the tropical area of Pakistan.•The proposed CNN architecture is compared with state of the art CNN models.
Morphological based fish species identification is an erroneous and time-consuming process. There are numerous fish species and due to their close resemblance with each other, it is difficult to classify them by external characters. Recently, computer vision and deep learning-based identification of different animal species is being widely used by the researchers. Convolutional Neural Network (CNN) is one of the most analytically powerful tools in deep learning architecture for the image classification based on visual features. This work aims to propose a deep learning framework based on the CNN method for fish species identification. The proposed CNN architecture contains 32 deep layers that are considerably deep to derive valuable and discriminating features from the image. The deep supervision is inflicted on the VGGNet architecture to increase the classification performance by instantly adding four convolutional layers to the training of each level in the network. To test the performance of proposed 32-Layer CNN architecture, we developed a dataset termed as Fish-Pak and is publicly available at Mendeley data (Fish-Pak: https://doi.org/10.17632/n3ydw29sbz.3#folder-6b024354-bae3-460a-a758-352685ba0e38). Fish-Pak contains 915 images with six distinct classes; Ctenopharyngodon idella (Grass carp), Cyprinus carpio (Common carp), Cirrhinus mrigala (Mori), Labeo rohita (Rohu), Hypophthalmichthys molitrix (Silver carp), and Catla catla (Thala) and three different image views (head region, body shape, and scale). To ensure the superior performance of proposed CNN architecture, we have carried out the experimental comparison with other deep learning frameworks involving VGG-16 for transfer learning, one block VGG, two block VGG, three block VGG, LeNet-5, AlexNet, GoogleNet, and ResNet-50 on the Fish-Pak data set. Comprehensive empirical analyses reveal that the proposed method achieves state of the art performance and outperforms existing methods.
Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical ...regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.
•Proposed a fast image encryption scheme by generating a substitution box and random numbers over elliptic curves.•The computational complexity of the scheme is independent of the point computation ...over elliptic curves.•Secrete keys are highly sensitive to plain text, and hence the scheme is highly secure.•Generated highly secure ciphertexts for all standard images in the USC-SIPI image database.•The scheme has high security against modern cryptanalyses such as differential attacks, statistical attacks, brute-force attacks, and known plain text and chosen plain text attacks.•The scheme has low computational complexity and high security as compared to state-of-the-art schemes over elliptic curves and chaotic systems.
Encryption schemes based on elliptic curves (ECs) have shown high resistance against modern cryptanalysis. However, these schemes involve heavy computation for the generation of points over ECs, and therefore they are not suitable for communication systems such as sensor networks and Internet of Things. Thus, we propose a fast and secure public-key image encryption scheme. In this scheme, the sender and receiver agree on a public EC. Then the scheme performs pixel-masking and pixel-scrambling procedures using random numbers and a dynamic substitution box over ECs that are isomorphic to the public EC. For a fixed public EC, we prove that our scheme has a computational complexity that is a polylogarithmic function of the size of the plain text. In other words, the computational complexity of our scheme is independent of the point generation over ECs. We tested the security of our scheme against modern attacks by encrypting all standard images in the USC-SIPI image database. Compared to some state-of-the-art cryptosystems, our scheme is fast and has high security.
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Cobalt oxide nanoparticles were successfully biosynthesized by complete green process using aqueous leaf extracts of Sageretia thea as chelating agent. Diverse techniques were applied ...for characterization. Antibacterial (with and without UV illumination), antileishmanial, antioxidant and enzyme inhibition applications were assessed, while freshly isolated macrophages and red blood cells were used for biocompatibility studies. Good antibacterial nature and enhancement of bactericidal nature upon UV modulation is reported. Staphylococcus aureus and Escherichia coli are indicated as most susceptible bacterial strains. Significant cytotoxic potential is revealed with IC50 calculated as 12.82µg/ml and 3.16µg/ml against the axenic leishmanial promastigote and amastigote cultures respectively. Biogenic cobalt oxide nanoparticles indicated DPPH free radical scavenging potential, while moderate antioxidant capacity and reducing power was demonstrated. Bioinspired cobalt oxide also demonstrated alpha amylase and protein kinase inhibition at higher concentrations. Biogenic cobalt oxide was found as more cytotoxic to macrophages (IC50=58.55µg/ml) then to RBC’s (IC50>200µg/ml). Our results indicate green synthesis as an alternative, effective and eco-friendly method for the biosynthesis of cobalt oxide nanoparticles with numerous biological applications.
Changes in structural characteristics and physicochemical properties of okara insoluble dietary fiber (IDF) during high-energy wet media milling process were analyzed. Particle size of the IDF was ...effectively reduced from 66.7 μm to 544.3 nm after 6 h of milling and kept constant (p > 0.05) as the milling process prolonged. As the particle size decreased, lightness and whiteness of the IDF significantly (p < 0.05) increased, zeta potential decreased continuously (p < 0.05). Swelling power, water solubility index and apparent viscosity extensively increased (p < 0.05) after 1 h of milling and then steadily decreased (p < 0.05). Electron microscopy observations showed that the IDF was changed from regular and compact rods to aggregates with puffed morphology. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis revealed that intermolecular hydrogen bonds and crystalline structure of the IDF polysaccharides were disturbed after milling.
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•Okara insoluble dietary fiber (IDF) was downsized to nanometer range by wet milling.•Molecular and crystalline structure of IDF was disrupted after milling.•Particle size reduction of the IDF contributed to modify physicochemical properties.•Modified okara IDF may be applied as functional ingredient in foods.
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