International Journal of Current Research and Review
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IJCRR - Vol 13 Issue 07, April, 2021

Pages: 185-190

Date of Publication: 12-Apr-2021


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Capsicum Plant Leaves Disease Detection Using Convolution Neural Networks

Author: Himanshu Pant, Manoj Chandra Lohani, Janmejay Pant, Prachi Petshali

Category: Healthcare

Abstract:Introduction: Plants are a significant source of energy. Crop protection and production can be increased using the early and accurate diagnosis of plant diseases. In the old-fashioned environment, the identification is processed whether the plant leaf is healthy or infected either by visual observation or by testing leaves in the laboratory. The visual identification is done by the experts of the plant domain but opinion may vary from expert to expert. Testing of the plant leaf in the laboratory is a very time consuming and strenuous process and hence results may not come on time. Aims: The aim of this research article indicates that the proposed Convolutional neural networks (CNN) provide a healthier solution in disease control for capsicum leaf with high accuracy of validation and a faster convergence rate. Methodology: To overcome these issues, image-based plant diseases classification and detection using Convolutional neural networks (CNN) have been presented in the literature. The authors have focused on the capsicum plant (Bell pepper) for this purpose, which belongs to the Grossum cultivar group of the species Capsicum annuum disease. Results: After model development and fitting, the operational performance and quality can be evaluated on the unseen testing dataset. The performance is measured in terms of accuracy. The model accuracy of each block VGG model can be calculated by increasing the convolutional layer and pooling layer. The model accuracy is improved from 84% to approx. 96%. Conclusion: Convolutional neural network is performed to detect, identify and classify the capsicum plant disease in this research. This research article reconnoitred three different improvements to the baseline model. The performance of the different results can be summarized in the terms of model accuracy.

Keywords: Accuracy, Capsicum, Computer vision, Classification, Convolutional neural networks, Leaf disease

Full Text:

India is a rich country having a huge amount of natural and human resources.  Almost 70% of the Indian economy is based upon the agriculture and horticulture sectors.1 This agriculture sector contains numerous ingredients.  Plants are one of the significant factors among them. Fit and healthy plants provide health.2 However, plant diseases are bullying the maintenance of this natural resource. Production and economic losses increases due to plant diseases in the agriculture and forestry sector. Capsicum (Capsicum annum) is the most used food crop in the world. Bell pepper bacterial spot (a fungal disease in capsicum) has caused a momentous economic and commercial loss and just by eliminating 20% of this bacterial infection, the farmers may benefit from an extraordinary profit. Therefore, early detection and identification of capsicum bacterial diseases play the utmost important role to take timely measures for the quality of the plant. There are numerous ways to perceive plant pathologies. Some plants have no visible symptoms of diseases associated with them or diseases may appear only when too late to cure. So it is necessary to perform a classy analysis of the plant diseases in the laboratories by the experts using powerful microscopes or employing different electromagnetic spectrum that is not visible to humans.3

Early-stage identification and classification of crop plant disease is a major problem in agricultural practices. Farmers bear a great loss in the economy every year due to infection in the crops and plants cultivation.4 Therefore, fast, efficient, less expensive and accurate diagnosis is required to prevent bacterial infection in the capsicum. These accurate and automated methods may help to inhibit the loss of crops, improves the quality of the product and helps in the economic growth of the country as well.5

This research study focuses on disease detection and classification of capsicum plant (Bell pepper) based on the bacterial symptoms of the diseases that show unhealthy signs on the leaves of the plant. Capsicum plants are dumpy shrub in nature with woody trunks. These plants nurture with colourful fruits. Capsicum leaves disease detection model to perform various steps. The primary step is to obtain a feature vector and another important step is to classify the feature vectors of the given input data. Mostly, the identification of the disease is guessed first by the human’s visualization. Experts of the domain may be efficiently recognizing the disease present in the particular plant but in most cases, there is no domain professional present in the particular area to give feedback on the disease to the farmers after data analysis on the plant. Hence farmers are required a quick, cheap, accurate and automatic technique to detect the plant disease efficiently.6

Bacterial infection and fungus are the main reason for capsicum plant diseases. Numerous diseases may appear in the capsicum plant. This article considered the images of bacterially infected leaves along with the healthy leaves (HL) images. The Xanthomonas campestris (black rot) is the primary bacterial species of the capsicum plants. Therefore, digital Image-based plant disease identification and classification models have been developed in the literature for plants.7

Computer vision, artificial intelligence, machine learning and deep learning techniques are more popular research areas for object detection and classification from images, text and videos.8 Digital image processing technique minimized the inaccurate manual disease detection and improve the accuracy, feasibility and efficiency to predict the disease on a time from a plant.9 This paper leverages the identification and classification of the disease and healthy images from the capsicum plant using recent advancement in computer vision with the help of a convolutional neural network (CNN).

The first aim of the current research was the collection of a sufficient capsicum image dataset from the field and then classifies the images into two categories (Bacterial spot images and healthy images). The overview of the proposed system architecture of capsicum plant disease identification and classification is shown in figure-1.

Nowadays, researchers pay attention to convolutional neural network techniques due to their great performance in image classification. The advantage of the convolutional neural network is that it avoids extraction of complex hand-crafted features unlike traditional machine learning techniques and provides end-to-end learning.10 For image classification and accurate prediction, the CNN model provides a relationship between layers and spatial information of the image.11 Along this line, there are limited works on capsicum leaves disorders identification and classification using CNN. Some author inspected the capability of the deep CNN technique for the classification of numerous rice diseases. A total of 597 images has been considered and used the CNN model with three convolution layers, three stochastic pooling layers and a softmax layer at the end. The classification accuracy of 91.620% has been reported. After augmentation of the dataset, the model achieved 94.972%% accuracy. A total of 418 images belonging to the training dataset and 179 images belong to the test dataset out of 597.

MATERIALS AND METHODS

In this section, the authors performed the various steps and operations on the capsicum plant image dataset. To identify and classify the infected capsicum leaves from the huge dataset, numerous operations can be performed as shown in figure-2. These steps describe the complete architecture from image acquisition to image disease classification through which farmers can easily predict the healthy plant from the mixture of infected and bacterial image sets.  The methodology followed is discussed in detail.

Dataset Descriptions

In this article, a huge amount of the capsicum plant’s leaf images are required to identify and classify the diseases associated with it. The images are captured from the different agricultural fields and various gardens in Nainital, a district of Uttarakhand, India. These datasets are required for image disease classification research during the training, testing and validation phase. The images are acquired using numerous types of standard cameras, captured from both front and back end leaves. The Apple iPhone 8, the Samsung Galaxy M3 and Redmi note 5 pro cameras are used for image acquisition. It contains a collection of images taken in a different environment. A dataset containing 597 capsicums leaves of two image classes including bacterially infected leaves and healthy leaves. The sample images of both categories are shown in the figure-3.

Data pre-processing

The capsicum plant images are initially unlabelled and not in annotated form. The labelling process of the images is processed by their filenames with the word “HL” and “BS”. Where HL belongs to healthy capsicum leaves while BS represents bacterial spot capsicum leaves. The file naming convention is in the form of HL.1.jpg, Hl.2.jpg, BS.20.jpg, BS.23.jpg etc. Primarily RGB coloured images are taken as a sample. In the captured images dataset, some images are in landscape format, some are in portrait format, and the remaining images are in square format.12

To classify and identify the disease from the capsicum images standardized photo size are required. All the images must be reshaped before modelling so that the size and shape of all images would be the same.13 Keras image processing API is applied to achieve this standardization by uploading all images to the ImageDataGenerator class and reshapes them to 200×200 square photos. The labels of the images are also determined based on the filenames. After standardizations of all images into 200 x 200 sizes, the next objective is to pre-process images into standard directories using flow_from_directory() application programming interface (API). This API divides all data into separate train/ and test/ directories, and under each directory to have a subdirectory for each class, e.g. a train/HL/ and a train/BS/ subdirectories and the same procedure is applied for test images. Images are then organized under the subdirectories.

EXPERIMENTAL SETUP AND PERFORMANCE ANALYSIS

The experiment was designed to appraise the performance of the baseline convolution neural network model for the capsicum image dataset to classify and identify whether a specific image is infected or not.  This baseline CNN model is established to compare the model performance of other CNN models. The general architectural principles of the VGG models are used for the experiment.14This architecture involves assembling convolutional layers with small 3×3 filters followed by a max-pooling layer. A combination of convolutional layers and pooling layers form a block, and when the number of filters in each block is increased with the depth of the network, these blocks can be repeated. Padding is used on the convolutional layers to ensure the height and width shapes of the output feature maps matches the inputs. The authors applied this VGG architecture to the healthy capsicum plant image and bacterial spot image problem and then compare a developed model with this architecture using the first three blocks. In this designed architecture each layer is used the ReLU activation function and the he_uniform weight initialization.

The designed baseline CNN model is fitted with stochastic gradient descent and start with a conservative learning rate of 0.001 and a momentum of 0.9.15The proposed model can be fit using train iterator and dataset validation is done by test iterator. The number of steps for the train and test iterators concerning one epoch must be specified for the model fitting and it can be calculated by dividing the total number of capsicum images (both HL and BS) in the train and test directories by the batch size of 64.

Once the model is fitted, the performance and quality can be evaluated on the unseen testing dataset. The performance is measured in terms of accuracy.  The model accuracy of each block VGG model can be calculated by increasing the convolutional layer and pooling layer.16

One Block baseline Visual Geometry Group (VGG) model performance

The one-block baseline Visual Geometry Group (VGG) model has only one convolutional layer with 32 filters followed by a max-pooling layer. The images are in the form of matrices and have to convert two-dimensional matrixes into dimensional form to create a neural network. For this purpose, the flattening technique is used.  The dense layer is used to create a fully connected neural network by which each input node is connected to all output nodes. The classification accuracy of one block VGG model to classify the capsicum image dataset to predict whether the given image is infected or not and the cross-entropy loss on the test and training dataset at the end of each epoch is shown in figure-4. The one-block VGG baseline model gives 84.358% prediction accuracy.

Two Block baseline VGG model performance

The working procedure of the two blocks VGG model is almost same but in two-block VGG model. It is the extend version of one block VGG model by adding a second block (convolutional layer and max pooling layer) with 64 filters (just double from the first convolutional layer filter) as shown in figure-5. After compiling, the model had achieved a small improvement in the performance of accuracy and prediction from 84.358% to 84.916%.

Three-Block baseline VGG model performance

The architecture of three-block VGG model extends the two block VGG model by adding a third block (additional convolutional layer and max pooling layer) with 128 filters (just double from second convolutional layer filter). Repeating the compilation process using these three layers, model achieved a great improvement in the performance of accuracy and prediction from 84.916% to 91.620%. The classification accuracy and cross-entropy loss with respect to epoch is shown in figure-6.

Performance measurement after capsicum image data augmentation:

Image data augmentation techniques are used to modify the training images in the dataset. It is an approach that allows us to increase the existing training dataset without collecting new. The augmentation process involves cropping the images, padding, and horizontal flipping of the images on the existing dataset to create a new one. Small changes in the input data of healthy capsicum images and bacterial spot capsicum images might generate a huge amount of new data by applying translation, rotation, small shifts and horizontal flips. The augmentation should be used only for training dataset using ImageDataGenerator in Keras.

Images are augmented in the training dataset with small shifting in random horizontal and vertical shifts and used random horizontal flips which create a mirror image of a photo. When this baseline three-block VGG model is compiled with augmentation, the model performance is improved. It is observed that the model lift an excellent performance of about 10% from the baseline one block VGG performance which was 84.358%. Now after augmented model predicts perfectly whether a given image healthy or infected with 95.531% accuracy on the given dataset. The classification accuracy and cross-entropy loss concerning each epoch are shown in figure 7.

To describe the performance of the developed classification VGG model with three layers after augmentation, the confusion matrix is used for which the true values are known on a set of test data. The performance of the classification algorithms in deep learning and machine learning model is summarized by the confusion matrix. This matrix allows the visualizing of the performance of the developed CNN model in the matrix form. It is also used for decision making for selecting the right observations and can help to reduce errors. This binary classification problem is used to categorized and identify the capsicum plant disease. The confusion matrix and the classification report for three block baseline VGG model after augmentation with 95.531% accuracy are shown in the table-1.

CONCLUSION

A convolutional neural network is performed to detect, identify and classify the capsicum plant disease in this research. Limited research has been done on capsicum plant disease classification and automation. In this paper different baseline, Visual Geometry Group (VGG) model with one two and three blocks of CNN was explored to classify the capsicum plant disease. Moreover, the designed model had applied augmentation on the training dataset to improve efficiency and accuracy. The proposed model can classify infected or healthy plants with a classification accuracy of 94.972%. The dataset is split into 80:20 ratios of training and testing respectively. This research article reconnoitred three different improvements to the baseline model. The performance of the different results can be summarized as given table-2. It is shown that the result may be improved when the three-block augmentation approaches further increases the number of training epochs.

FUTURE SCOPE

The performance of the proposed model can be further improved with a large dataset of capsicum plant images with both healthy and infected leaves. The CNN model is trained using the images captured from the natural environment by cameras. The purposed model has achieved 95.531% accuracy of classification and identification of the disease. This accuracy may be increased by applying the transfer learning model on pre-trained model like VGG 16, VGG 19, Alexnet, etc. In the future authors will apply computer vision techniques like image segmentation and object detection on plant leaves.

References:

  1. India economic survey 2018: Farmers gain as agriculture mechanisation speeds up, but more R&D needed. The Financial Express. 29 January 2018. Retrieved 8 January 2019

  2. Phadikar S, Sil J, Das AK. Rice diseases classification using feature selection and rule generation techniques. Comp Electr Agricult2013;90:76–85.

  3. Barbedo JGA. A review of the main challenges in automatic plant disease identification based on visible range images. Bio Syst Engi 2016;144:52-60.

  4. Al Bashis D. A Framework for Detection and Classification of Plant Leaf and Stem Diseases. IEEE International Conference on Signal and Image Processing (ICSIP), Chennai 2010, pp.113-118.

  5. Sannakki SS. Diagnosis and    Classification of    Grape    Leaf    Diseases using    Neural    Networks. IEEE proceedings of 4ICCCNT, 2013.

  6. Smith JS. An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst Eng 2009;102:9–21.

  7. Liu K. Identification method of rice leaf blast using multilayer perception neural network. Transactions Chinese Soc Agricult Engi 2009;25(S2).

  8. Barbedo J. A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 2016;144:52-60.

  9. Asefpour K. An artificial neural network approach to identify fungal diseases of cucumber (Cucumissativus L.) Plants using digital image processing. Arch Phytopathol Plant Protect 2013; 46(13):1580-1588.

  10. Lu. Identification of rice diseases using deep convolutional neural networks. Neurocomputing 2017;267:378–384.

  11. Arel. Deep Machine Learning - A New Frontier in Artificial Intelligence. IEEE Computational Intelligence Magazine. 2010;5(4):13-18.

  12. Dhakate M. Diagnosis of Pomegranate Plant Diseases using Neural Network. IEEE 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Patna2015.

  13. Nam Y. A representation and matching method for shape-based leaf image retrieval. J KIISE: Softw Appl 2005;32(11):1013-1021.

  14. Simonyan K. Very deep convolutional networks for large-scale image recognition. 2015. Available https://arxiv.org/cs/1409.1556

  15. Wang L. Training deeper convolutional networks with deep supervision,” 20 Available https://arxiv.org/cs/1505.02496

  16. Leaf recognition algorithm for plant classification using probabilistic neural network. Available http://flavia.sourceforge.net/

Announcements

Dr. Pramod Kumar Manjhi joined Editor-in-Chief since July 2021 onwards

COPE guidelines for Reviewers

SCOPUS indexing: 2014, 2019 to 2021


Awards, Research and Publication incentive Schemes by IJCRR

Best Article Award: 

One article from every issue is selected for the ‘Best Article Award’. Authors of selected ‘Best Article’ are rewarded with a certificate. IJCRR Editorial Board members select one ‘Best Article’ from the published issue based on originality, novelty, social usefulness of the work. The corresponding author of selected ‘Best Article Award’ is communicated and information of award is displayed on IJCRR’s website. Drop a mail to editor@ijcrr.com for more details.

Women Researcher Award:

This award is instituted to encourage women researchers to publish her work in IJCRR. Women researcher, who intends to publish her research work in IJCRR as the first author is eligible to apply for this award. Editorial Board members decide on the selection of women researchers based on the originality, novelty, and social contribution of the research work. The corresponding author of the selected manuscript is communicated and information is displayed on IJCRR’s website. Under this award selected women, the author is eligible for publication incentives. Drop a mail to editor@ijcrr.com for more details.

Emerging Researcher Award:

‘Emerging Researcher Award’ is instituted to encourage student researchers to publish their work in IJCRR. Student researchers, who intend to publish their research or review work in IJCRR as the first author are eligible to apply for this award. Editorial Board members decide on the selection of student researchers for the said award based on originality, novelty, and social applicability of the research work. Under this award selected student researcher is eligible for publication incentives. Drop a mail to editor@ijcrr.com for more details.


Best Article Award

A Study by Humaira Tahir et al. entitled "Comparison of First Analgesic Demand after Major Surgeries of Obstetrics and Gynecology between Pre-Emptive Versus Intra-Operative Groups by Using Intravenous Paracetamol: A Cross-Sectional Study" is awarded Best Article for Vol 14 issue 14
A Study by Monica K. entitled "Risk Predictors for Lymphoma Development in Sjogren Syndrome - A Systematic Review" is awarded Best Article for Vol 14 issue 13
A Study by Mokhtar M Sh et al. entitled "Prevalence of Hospital Mortality of Critically Ill Elderly Patients" is awarded Best Article for Vol 14 issue 12
A Study by Vidya S. Bhat et al. entitled "Effect of an Indigenous Cleanser on the Microbial Biofilm on Acrylic Denture Base - A Pilot Study" is awarded Best Article for Vol 14 issue 11
A Study by Pandya S. et al. entitled "Acute and 28-Day Repeated Dose Subacute Toxicological Evaluation of Coroprotect Tablet in Rodents" is awarded Best Article for Vol 14 issue 10
A Study by Muhammad Zaki et al. entitled "Effect of Hemoglobin Level on the Severity of Acute Bronchiolitis in Children: A Case-Control Study" is awarded Best Article for Vol 14 issue 09
A Study by Vinita S & Ayushi S entitled "Role of Colour Doppler and Transvaginal Sonography for diagnosis of endometrial pathology in women presenting with Abnormal Uterine Bleeding" is awarded Best Article for Vol 14 issue 08
A Study by Prabhu A et al. entitled "Awareness of Common Eye Conditions among the ASHA (Accredited Social Health Activist) Workers in the Rural Communities of Udupi District- A Pilot Study" is awarded Best Article for Vol 14 issue 07
A Study by Divya MP et al. entitled "Non-Echoplanar Diffusion-Weighted Imaging and 3D Fiesta Magnetic Resonance Imaging Sequences with High Resolution Computed Tomography Temporal Bone in Assessment and Predicting the Outcome of Chronic Suppurative Otitis Media with Cholesteatoma" is awarded Best Article for Vol 14 issue 06
A Study by Zahoor Illahi Soomro et al. entitled "Functional Outcomes of Fracture Distal Radius after Fixation with Two Different Plates: A Retrospective Comparative Study" is awarded Best Article for Vol 14 issue 05
A Study by Ajai KG & Athira KN entitled "Patients’ Gratification Towards Service Delivery Among Government Hospitals with Particular Orientation Towards Primary Health Centres" is awarded Best Article for Vol 14 issue 04
A Study by Mbungu Mulaila AP et al. entitled "Ovarian Pregnancy in Kindu City, D.R. Congo - A Case Report" is awarded Best Article for Vol 14 issue 03
A Study by Maryam MJ et al. entitled "Evaluation Serum Chemerin and Visfatin Levels with Rheumatoid Arthritis: Possible Diagnostic Biomarkers" is awarded Best Article for Vol 14 issue 02
A Study by Shanthan KR et al. entitled "Comparison of Ultrasound Guided Versus Nerve Stimulator Guided Technique of Supraclavicular Brachial Plexus Block in Patients Undergoing Upper Limb Surgeries" is awarded Best Article for Vol 14 issue 01
A Study by Amol Sanap et al. entitled "The Outcome of Coxofemoral Bypass Using Cemented Bipolar Hemiarthroplasty in the Treatment of Unstable Intertrochanteric Fracture of Femur in a Rural Setup" is awarded Best Article Award of Vol 13 issue 24
A Study by Manoj KP et al. entitled "A Randomized Comparative Clinical Trial to Know the Efficacy of Ultrasound-Guided Transversus Abdominis Plane Block Against Multimodal Analgesia for Postoperative Analgesia Following Caesarean Section" is awarded Best Article Award of Vol 13 issue 23
A Study by Karimova II et al. entitled "Changes in the Activity of Intestinal Carbohydrases in Alloxan-Induced Diabetic Rats and Their Correction with Prenalon" is awarded Best Article of Vol 13 issue 22
A Study by Ashish B Roge et al. entitled "Development, Validation of RP-HPLC Method and GC MS Analysis of Desloratadine HCL and It’s Degradation Products" is awarded Best Article of Vol 13 issue 21
A Study by Isha Gaurav et al. entitled "Association of ABO Blood Group with Oral Cancer and Precancer – A Case-control Study" is awarded Best Article for Vol 13 issue 20
A Study by Amr Y. Zakaria et al. entitled "Single Nucleotide Polymorphisms of ATP-Binding Cassette Gene(ABCC3 rs4793665) affect High Dose Methotrexate-Induced Nephrotoxicity in Children with Osteosarcoma" is awarded Best Article for Vol 13 issue 19
A Study by Kholis Ernawati et al. entitled "The Utilization of Mobile-Based Information Technology in the Management of Dengue Fever in the Community Year 2019-2020: Systematic Review" is awarded Best Article for Vol 13 issue 18
A Study by Bhat Asifa et al. entitled "Efficacy of Modified Carbapenem Inactivation Method for Carbapenemase Detection and Comparative Evaluation with Polymerase Chain Reaction for the Identification of Carbapenemase Producing Klebsiella pneumonia Isolates" is awarded Best Article for Vol 13 issue 17
A Study by Gupta R. et al. entitled "A Clinical Study of Paediatric Tracheostomy: Our Experience in a Tertiary Care Hospital in North India" is awarded Best Article for Vol 13 issue 16
A Study by Chandran Anand et al. entitled "A Prospective Study on Assessment of Quality of Life of Patients Receiving Sorafenib for Hepatocellular Carcinoma" is awarded Best article for Vol 13 issue 15
A Study by Rosa PS et al. entitled "Emotional State Due to the Covid – 19 Pandemic in People Residing in a Vulnerable Area in North Lima" is awarded Best Article for Vol 13 issue 14
A Study by Suvarna Sunder J et al. entitled "Endodontic Revascularization of Necrotic Permanent Anterior Tooth with Platelet Rich Fibrin, Platelet Rich Plasma, and Blood Clot - A Comparative Study" is awarded Best Article for Vol 13 issue 13
A Study by Mona Isam Eldin Osman et al. entitled "Psychological Impact and Risk Factors of Sexual Abuse on Sudanese Children in Khartoum State" is awarded Best Article for Vol 13 issue 12
A Study by Khaw Ming Sheng & Sathiapriya Ramiah entitled "Web Based Suicide Prevention Application for Patients Suffering from Depression" is awarded Best Article for Vol 13 issue 11
A Study by Purushottam S. G. et al. entitled "Development of Fenofibrate Solid Dispersions for the Plausible Aqueous Solubility Augmentation of this BCS Class-II Drug" is awarded Best article for Vol 13 issue 10
A Study by Kumar S. et al. entitled "A Study on Clinical Spectrum, Laboratory Profile, Complications and Outcome of Pediatric Scrub Typhus Patients Admitted to an Intensive Care Unit from a Tertiary Care Hospital from Eastern India" is awarded Best Article for Vol 13 issue 09
A Study by Mardhiah Kamaruddin et al. entitled "The Pattern of Creatinine Clearance in Gestational and Chronic Hypertension Women from the Third Trimester to 12 Weeks Postpartum" is awarded Best Article for Vol 13 issue 08
A Study by Sarmila G. B. et al. entitled "Study to Compare the Efficacy of Orally Administered Melatonin and Clonidine for Attenuation of Hemodynamic Response During Laryngoscopy and Endotracheal Intubation in Gastrointestinal Surgeries" is awarded Best Article for Vol 13 issue 07
A Study by M. Muthu Uma Maheswari et al. entitled "A Study on C-reactive Protein and Liver Function Tests in Laboratory RT-PCR Positive Covid-19 Patients in a Tertiary Care Centre – A Retrospective Study" is awarded Best Article of Vol 13 issue 06 Special issue Modern approaches for diagnosis of COVID-19 and current status of awareness
A Study by Gainneos PD et al. entitled "A Comparative Evaluation of the Levels of Salivary IgA in HIV Affected Children and the Children of the General Population within the Age Group of 9 – 12 Years – A Cross-Sectional Study" is awarded Best Article of Vol 13 issue 05 Special issue on Recent Advances in Dentistry for better Oral Health
A Study by Alkhansa Mahmoud et al. entitled "mRNA Expression of Somatostatin Receptors (1-5) in MCF7 and MDA-MB231 Breast Cancer Cells" is awarded Best Article of Vol 13 issue 06
A Study by Chen YY and Ghazali SRB entitled "Lifetime Trauma, posttraumatic stress disorder Symptoms and Early Adolescence Risk Factors for Poor Physical Health Outcome Among Malaysian Adolescents" is awarded Best Article of Vol 13 issue 04 Special issue on Current Updates in Plant Biology to Medicine to Healthcare Awareness in Malaysia
A Study by Kumari PM et al. entitled "Study to Evaluate the Adverse Drug Reactions in a Tertiary Care Teaching Hospital in Tamilnadu - A Cross-Sectional Study" is awarded Best Article for Vol 13 issue 05
A Study by Anu et al. entitled "Effectiveness of Cytological Scoring Systems for Evaluation of Breast Lesion Cytology with its Histopathological Correlation" is awarded Best Article of Vol 13 issue 04
A Study by Sharipov R. Kh. et al. entitled "Interaction of Correction of Lipid Peroxidation Disorders with Oxibral" is awarded Best Article of Vol 13 issue 03
A Study by Tarek Elwakil et al. entitled "Led Light Photobiomodulation Effect on Wound Healing Combined with Phenytoin in Mice Model" is awarded Best Article of Vol 13 issue 02
A Study by Mohita Ray et al. entitled "Accuracy of Intra-Operative Frozen Section Consultation of Gastrointestinal Biopsy Samples in Correlation with the Final Histopathological Diagnosis" is awarded Best Article for Vol 13 issue 01
A Study by Badritdinova MN et al. entitled "Peculiarities of a Pain in Patients with Ischemic Heart Disease in the Presence of Individual Combines of the Metabolic Syndrome" is awarded Best Article for Vol 12 issue 24
A Study by Sindhu Priya E S et al. entitled "Neuroprotective activity of Pyrazolone Derivatives Against Paraquat-induced Oxidative Stress and Locomotor Impairment in Drosophila melanogaster" is awarded Best Article for Vol 12 issue 23
A Study by Habiba Suhail et al. entitled "Effect of Majoon Murmakki in Dysmenorrhoea (Usre Tams): A Standard Controlled Clinical Study" is awarded Best Article for Vol 12 issue 22
A Study by Ghaffar UB et al. entitled "Correlation between Height and Foot Length in Saudi Population in Majmaah, Saudi Arabia" is awarded Best Article for Vol 12 issue 21
A Study by Siti Sarah Binti Maidin entitled "Sleep Well: Mobile Application to Address Sleeping Problems" is awarded Best Article for Vol 12 issue 20
A Study by Avijit Singh"Comparison of Post Operative Clinical Outcomes Between “Made in India” TTK Chitra Mechanical Heart Valve Versus St Jude Mechanical Heart Valve in Valve Replacement Surgery" is awarded Best Article for Vol 12 issue 19
A Study by Sonali Banerjee and Mary Mathews N. entitled "Exploring Quality of Life and Perceived Experiences Among Couples Undergoing Fertility Treatment in Western India: A Mixed Methodology" is awarded Best Article for Vol 12 issue 18
A Study by Jabbar Desai et al. entitled "Prevalence of Obstructive Airway Disease in Patients with Ischemic Heart Disease and Hypertension" is awarded Best Article for Vol 12 issue 17
A Study by Juna Byun et al. entitled "Study on Difference in Coronavirus-19 Related Anxiety between Face-to-face and Non-face-to-face Classes among University Students in South Korea" is awarded Best Article for Vol 12 issue 16
A Study by Sudha Ramachandra & Vinay Chavan entitled "Enhanced-Hybrid-Age Layered Population Structure (E-Hybrid-ALPS): A Genetic Algorithm with Adaptive Crossover for Molecular Docking Studies of Drug Discovery Process" is awarded Best article for Vol 12 issue 15
A Study by Varsha M. Shindhe et al. entitled "A Study on Effect of Smokeless Tobacco on Pulmonary Function Tests in Class IV Workers of USM-KLE (Universiti Sains Malaysia-Karnataka Lingayat Education Society) International Medical Programme, Belagavi" is awarded Best article of Vol 12 issue 14, July 2020
A study by Amruta Choudhary et al. entitled "Family Planning Knowledge, Attitude and Practice Among Women of Reproductive Age from Rural Area of Central India" is awarded Best Article for special issue "Modern Therapeutics Applications"
A study by Raunak Das entitled "Study of Cardiovascular Dysfunctions in Interstitial Lung Diseas epatients by Correlating the Levels of Serum NT PRO BNP and Microalbuminuria (Biomarkers of Cardiovascular Dysfunction) with Echocardiographic, Bronchoscopic and HighResolution Computed Tomography Findings of These ILD Patients" is awarded Best Article of Vol 12 issue 13 
A Study by Kannamani Ramasamy et al. entitled "COVID-19 Situation at Chennai City – Forecasting for the Better Pandemic Management" is awarded best article for  Vol 12 issue 12
A Study by Muhammet Lutfi SELCUK and Fatma entitled "Distinction of Gray and White Matter for Some Histological Staining Methods in New Zealand Rabbit's Brain" is awarded best article for  Vol 12 issue 11
A Study by Anamul Haq et al. entitled "Etiology of Abnormal Uterine Bleeding in Adolescents – Emphasis Upon Polycystic Ovarian Syndrome" is awarded best article for  Vol 12 issue 10
A Study by entitled "Estimation of Reference Interval of Serum Progesterone During Three Trimesters of Normal Pregnancy in a Tertiary Care Hospital of Kolkata" is awarded best article for  Vol 12 issue 09
A Study by Ilona Gracie De Souza & Pavan Kumar G. entitled "Effect of Releasing Myofascial Chain in Patients with Patellofemoral Pain Syndrome - A Randomized Clinical Trial" is awarded best article for  Vol 12 issue 08
A Study by Virendra Atam et. al. entitled "Clinical Profile and Short - Term Mortality Predictors in Acute Stroke with Emphasis on Stress Hyperglycemia and THRIVE Score : An Observational Study" is awarded best article for  Vol 12 issue 07
A Study by K. Krupashree et. al. entitled "Protective Effects of Picrorhizakurroa Against Fumonisin B1 Induced Hepatotoxicity in Mice" is awarded best article for issue Vol 10 issue 20
A study by Mithun K.P. et al "Larvicidal Activity of Crude Solanum Nigrum Leaf and Berries Extract Against Dengue Vector-Aedesaegypti" is awarded Best Article for Vol 10 issue 14 of IJCRR
A study by Asha Menon "Women in Child Care and Early Education: Truly Nontraditional Work" is awarded Best Article for Vol 10 issue 13
A study by Deep J. M. "Prevalence of Molar-Incisor Hypomineralization in 7-13 Years Old Children of Biratnagar, Nepal: A Cross Sectional Study" is awarded Best Article for Vol 10 issue 11 of IJCRR
A review by Chitra et al to analyse relation between Obesity and Type 2 diabetes is awarded 'Best Article' for Vol 10 issue 10 by IJCRR. 
A study by Karanpreet et al "Pregnancy Induced Hypertension: A Study on Its Multisystem Involvement" is given Best Paper Award for Vol 10 issue 09

List of Awardees

A Study by Ese Anibor et al. "Evaluation of Temporomandibular Joint Disorders Among Delta State University Students in Abraka, Nigeria" from Vol 13 issue 16 received Emerging Researcher Award


A Study by Alkhansa Mahmoud et al. entitled "mRNA Expression of Somatostatin Receptors (1-5) in MCF7 and MDA-MB231 Breast Cancer Cells" from Vol 13 issue 06 received Emerging Researcher Award


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Disclaimer: International Journal of Current Research and Review (IJCRR) provides platform for researchers to publish and discuss their original research and review work. IJCRR can not be held responsible for views, opinions and written statements of researchers published in this journal.



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International Journal of Current Research and Review (IJCRR) provides platform for researchers to publish and discuss their original research and review work. IJCRR can not be held responsible for views, opinions and written statements of researchers published in this journal

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