Online ISSN: 2515-8260

Keywords : Image processing


Plant Disease Identifer Using K-Means and GLSM in Convolution Neural Network

S.P. Vijaya Vardan Reddy; T. Suresh; K. Naresh Kumar Thapa; V. Ramkumar; S. Mahabhoob Basha; Deepika. Y

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1354-1360

Produces from agriculture which feeds the entire population is dependent on proper farming practices. The growth of technology must pay a way for increasing the produce per acre and also help in reducing the onset of frequently affecting plant disease. Timely help in detecting the diseases coupled with solution helps in productivity and quality of the produce. This paper aims to detect the plant leaf disease based on image detection and using machine learning to identify the disease with accuracy and suggest the solution. The product must cater to the needs of urban and rural farmer and also the person with only lay man knowledge of taking photo. This project mainly focuses on leaf disease like Anthracnose, Bacterial Blight, Cercospora, Alternaria Altermata diseases in the Pomegranate, Indian Beech, Tobacco, and Bitter Gourd leaves. This project aims to identify the disease even with lesser region of Interest and predict the leaf diseases using Convolutional Neural Network Algorithm

Automated Diagnosis of Malarial Parasite in Red Blood Cells

Mr K.P.K Devan; Dr G. S. Anandha Mala; Deepthi Salunkey. K; Grace Cynthia. R; Madhumitha. J

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2718-2725

The traditional system for detecting the infection has been the manual process of diagnosing the stained slides under a microscope. This manual process might consume more time for producing the results and the availability of medical experts is not always assured. Considering this as the primary concern we proposed a strategy which limits the human error while recognizing the presence of malarial parasite in the blood sample by using Image Processing. Hence by automating the diagnosis process, results can be acquired relatively quicker and more accuracy can be expected. The technologies and techniques to patently extract the required features and efficiently classify the infected samples are surveyed. This paper presents a survey of various approaches to automate the detection and classification of infected and uninfected cells.

Real time object detection using Image Processing

Dr .S.Joshua Kumaresan; Shaik Shameem; M. Priyadharshini; Mr. Vinodh James; R.Lakshmi Priya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2403-2411

Object detection plays an important role in real time applications. It is used in many applications such as surveillance monitoring, human machine interaction, army base etc. The main aim of this paper is to detect the object and to detect the colour of the object using Image processing technique. Pi camera. Raspberry pi 11 kit interfaced with pi camera is used for detection of object. Raspbian os with python coding is used for object detection and colour recognition.

Recognition of the Old and Soiled Indian Paper Currency using Image Processing

Vidhika D. Sirwani; V. Rohith

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5080-5089

In the paper, a system is proposed which is used to identify the old and soiled Indian paper currency notes. When the new currency notes are introduced and put into circulation, they get passed from person to person. As the time passes by, these notes gets soiled, dirtier, and also get wrinkled. Identification and recognition of such notes inside the automated teller machines (ATMs) becomes difficult. Thus in the paper, a system is developed to handle such soiled, old and dirty Indian paper currency notes. The system works on three denominations of Indian paper currency which are 50, 200 and 500 Indian paper currency.

Detection and Identification of Potato Plant Leaf Diseases using Convolution Neural Networks

N. ANANTHI; K. KUMARAN; MADHUSHALINI. V; GANESH MOORTHI. S; HARISH. P

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2753-2762

Crops suffering from various diseases can be a big turndown for crop yield. This can affect effective crop production, if left unnoticed. Hence, it is extremely important to examine the plant diseases in its initial stages so that felicitous actions can be taken by the farmers at the nick of time, to avoid further losses. It focuses on the method which is based on image processing way for identification of diseases of leaf in a plant .so let’s introduce a system which uses convolutional neural networks that helps farmers to identify any possible plant disease by loading a leaf image in to the system. The system consists of a collection of algorithms which identifies the type of disease with which the leaf is affected by a disease. Input image given by the user goes through many pre-processing steps to identify the disease and results are returned back to the user on a user interface.

A REVIEW ON VARIOUS SEGMENTATION TECHNIQUES IN IMAGE PROCESSSING

K. Jeevitha; A. Iyswariya; V. RamKumar; S. Mahaboob Basha; V. Praveen Kumar

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1342-1348

Due to the advancement of computer technology image-processing techniques have become increasingly important in a wide variety of applications. Image segmentation plays a important role in image processing. Image segmentation refers to partition of an image into different regions that are similar and different in some characteristics like color, intensity or texture. Different algorithms and techniques have been developed for image segmentation. This paper investigates and compiles some of the technologies used for image segmentation. The various segmentation techniques like Edge Detection, Threshold, Region based, Feature Based Clustering and Neural Network Image Segmentation were discussed in this paper

A NOVEL METHOD OF EXTRACTING DATA IN STONE INSCRIPTION

Mrs.R.REENA ROY; ARUN KUMAR .P; HABIB DHULFIKHAR. H; CAROLINE. J

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2668-2680

Documents of the ancient time gift several opposition for standalone gesture recognition systems, among them, the division and classification steps. Fastidiously gloss wordings square measure is required to coach a system. In some eventualities, written document square measure solely offered at the subdivision level. During this activity, we have a tendency to demonstrate the way to train the system with few tagged information. We have a tendency to additionally propose a model-based social control theme that considers the variability within the writing scale at the popularity section. We have a tendency to apply this approach to the publically offered browse dataset. Our system achieved the competitor result. Humans have distinctive handwriting designs that prove to be an obstacle for handwriting recognition algorithms. To date, multiple researches are done to acknowledge these totally different handwriting designs, most notable mistreatment the synthetic neural network (ANN) with back propagation algorithms that has additionally been verified to relinquish adequately high accuracies. By mistreatment real time method image capturing, this technique and algorithmic rule will be enforced to use multiple written entry information for faculties and universities, wherever the written information of a regular score sheet from totally different people will be transferred to a computer program.