Plant Disease Detection Using Image Processing Matlab Project With Source Code | Final Year Project Code

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 ABSTRACT

            The detection and classification of plant diseases are the crucial factors in plant production and the reduction of losses in crop yield. This project proposes an approach for plant leaf disease detection and classification on plants using image processing. The plant disease diagnosis is restricted by person’s visual capabilities as it is microscopic in nature. Due to optical nature of plant monitoring task, computer visualization methods are adopted in plant disease recognition. The aim is to detect the symptoms of the disease occurred in leaves in an accurate way. Once the captured image is pre-processed, the various properties of the plant leaf such as intensity, color and size are extracted and sent to with Convolutional  Neural Network CNN for classification. 

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Prof. Roshan P. Helonde
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Facial Expression Based Emotion Recognition Using Image Processing Matlab Project With Source Code | Final Year Project Code

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  ABSTRACT

            This project objective is to introduce needs and applications of facial expression recognition. Between Verbal & Non-Verbal form of communication facial expression is form of non-verbal communication but it plays pivotal role. It express human perspective or filling & his or her mental situation. A big research has been addressed to enhance Human Computer Interaction (HCI) over two decades. This project includes introduction of facial emotion recognition system, Application, comparative study of popular face expression recognition techniques & phases of automatic facial expression recognition system. Emotional aspects have huge impact on Social intelligence like communication understanding, decision making and also helps in understanding behavioral aspect of human. Emotion play pivotal role during communication. Emotion recognition is carried out in diverse way, it may be verbal or non-verbal .Voice (Audible) is verbal form of communication & Facial expression, action, body postures and gesture is non-verbal form of communication. While communicating only 7% effect of message is contributes by verbal part as a whole, 38% by vocal part and 55% effect of the speaker’s message is contributed by facial expression. For that reason automated & real time facial expression would play important role in human and machine interaction. Facial expression recognition would be useful from human facilities to clinical practices. Analysis of facial expression plays fundamental roles for applications which are based on emotion recognition like Human Computer Interaction (HCI), Social Robot, Animation, Alert System & Pain monitoring for patients.

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Prof. Roshan P. Helonde
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Coffee Leaf Disease Detection Using Image Processing Matlab Project With Source Code | Final Year Project

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 ABSTRACT

           An automatic coffee plant disease recognition system is required since coffee is an important commodity in the world economy and its productivity and quality are affected by diseases such as Coffee Leaf Miner, Coffee Leaf Rust, Coffee Leaf Spot and Coffee Phoma Leaf Rust. This project aims to apply computational methods to recognize main diseases in coffee leaves, with the purpose to implement an expert system to assist coffee producers in disease diagnosis during its initial stages. Since these diseases are shapeless, it inspires a texture attribute extraction approach for pattern recognition. The results were compared with a deep learning convolutional neural network applied directly to the same collection of images, 

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Prof. Roshan P. Helonde
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Lung Cancer Detection and Classification Using Deep Learning CNN Matlab Project With Source Code Final Year Project

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  ABSTRACT

             Lung cancer prevalence is one of the highest of cancers. One of the first steps in lung cancer diagnosis is sampling of lung image. These tissue samples are then analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from images of lung. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this project is to design a lung cancer detection system based on analysis image of lung using digital image processing. Lung Cancer Detection and classification done using deep learning CNN (convolutional neural network).

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Prof. Roshan P. Helonde
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Fruit Disease Detection Using Image Processing Matlab Project With Source Code | Fruit Disease Identification Using Matlab Project

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 ABSTRACT

            Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this project, a solution for the detection and classification of fruit diseases is proposed and experimentally validated. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. An early detection of fruit diseases can aid in decreasing such losses and can stop further spread of diseases. A lot of work has been done to automate the visual inspection of the fruits by machine vision with respect to size and color. However, detection of defects in the fruits using images is still problematic due to the natural variability of skin color in different types of fruits, high variance of defect types, and presence of stem/calyx. To know what control factors to consider next year to overcome similar losses, it is of great significance to analyze what is being observed. This project is developed in matlab.

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Brain Tumor Detection Using Deep Learning CNN Python Project With Source Code | Brain Tumor Detection Using Python Project

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 ABSTRACT

          Brain tumors are the most common issue in children. Approximately 3,410 children and adolescents under age 20 are diagnosed with primary brain tumors each year. Brain tumors, either malignant or benign, that originate in the cells of the brain. The conventional method of detection and classification of brain tumor is by human inspection with the use of medical resonant brain images. But it is impractical when large amounts of data is to be diagnosed and to be reproducible. And also the operator assisted classification leads to false predictions and may also lead to false diagnose. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. In this work we used Brain Tumor Detection Using Deep Learning Convolutional Neural Network CNN.

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Wheat Leaf Disease Detection Using Deep Learning CNN Matlab Project With Source Code || Wheat Leaf Disease Detection Using Image Processing

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 ABSTRACT

                    Now-a-days wheat plants are getting infected by different types of diseases very rapidly. It is must to come up with new system to single out diseases. It is must to design and implement such a system that can easily find out the diseases infected by plants. In India many crops are cultivated, out of which wheat being one of the most important food grain that this country cultivates and exports. Thus it can be seen that wheat forms a major part of the Indian agricultural system and India’s economy. Hence, maintenance of the steady production of above stated crop is very important. The main idea of this project is to provide a system for detecting wheat leaf diseases. The given system will find the disease on leaf image of a wheat plant through image processing this project is develop in python. Former algorithms are used for extracting vital information from the leaf and the latter is used for detecting the disease that it is infected with. This Project is developed in matlab.

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Prof. Roshan P. Helonde
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Forgery Signature Recognition Using Image Processing Matlab Project With Source Code | Forgery Signature Detection Using Matlab

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 ABSTRACT

         The fact that the signature is widely used as a means of personal identification tool for humans require that the need for an automatic verification system. Forgery can be performed either Offline or Online based on the application. However human signatures can be handled as an image and recognized using image processing. With modern computers, there is need to develop fast algorithms for forgery signature recognition. There are various approaches to forgery signature recognition with a lot of scope of research. Forgery signature recognition is a technology that can improve security in our day to day transaction held in society. This project presents a novel approach for offline forgery signature recognition. In this project forgery signature verification done using Image Processing is projected, where the signature is written on a paper are obtained using a scanner or a camera captured and presented in an image format. For authentication of signature, the proposed method is based on geometrical and statistical feature extraction and then the entire database. The extracted features of investigation forgery signature are compared with the previously trained features of the reference signature. This technique is suitable for various applications such as bank transactions, passports with good authentication results etc.

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Banana Leaf Disease Identification Using Image Processing Matlab Project With Source Code | Banana Leaf Disease Detection

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ABSTRACT

             Disease diagnosis and classification in banana crop using image processing technique is an interesting and useful application for farmers to identify, analyze and manage plant pathogens within fields as effectively and automatically at minimum cost. Major banana diseases express their symptoms on leaf area in their earlier stage of infection. These disease can be analyzed and classified automatically through computer vision and machine vision systems that use image processing techniques for information interpretation. This project shows various disease identified on banana plant leaf using Image Processing Matlab Project With Source Code.

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Medicinal Leaf Recognition Using Image Processing Matlab Project With Source Code || Final Year Projects

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 ABSTRACT

            Image processing is the recent growing technique in the world. It refers to the processing of digital images by means of a digital computer. Images play a major role in human perception. Image analysis is between image processing and computer vision. There are no clear boundaries for in continuum with image processing and computer vision. The useful paradigms for computerized process in determining the image is classified in to three types are low-level process: involve primitive operation such as image pre processing to reduce noise, image enhancement and image sharpening, mid-level: image segmentation and high-level: making sense of image recognized. Here image processing technique is used for medicinal purpose by extracting the features of herbal leaf and authenticating it medicinal qualities. Leaves play the major role for the classification of plants. The sample leaves are taken from various places, plants and shape. The image is captured and further work is carried out. Comparison of test sample image with reference not only requires an experienced but is subjective and prone to human errors. By applying advanced technique of image processing and utilizing the capabilities of the recent advanced computing and data/image storage facilities. The aim of the work is to classify and authenticate the medicinal plant materials and herbs widely used for Indian herbal medicinal preparation. The quality and authenticity of these leaves are to be ensured for the preparation of herbal medicines. The medicinal plant leaves are thoroughly screened, analyzed and compared with the database to give the correct measures of the texture to which category the leaf belongs to.

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Prof. Roshan P. Helonde
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Types of Blood Cell Detection Using Convolutional Neural Network (CNN) Matlab Project With Source Code | Final Year Projects

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 ABSTRACT

          Blood cells are important parts of our immune system and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. Types of blood cell are Lymphocytes, Monocytes, Eosinophils and Neutrophils. In this project a computer-aided automated system used that can easily identify and locate blood cell types in blood images has been proposed. Diseases such as bordetella pertussis, hepatitis, viruses, brucella, leukemia increase lymphocytes in the blood whereas diseases such as HIV, rubeola, poliovirus, chickenpox, tuberculosis reduce the amount of lymphocytes. Listeriosis and malaria as well as bacterial and viral infections are some of the diseases that increase the number of monocytes. Allergic diseases, atopic diseases and parasites are factors that increase eosinophil value. Neutrophils show an increase in blood in cases of hormonal causes, metabolic disorders, hemolysis and bleeding. In addition; bacteria, fungi, exotoxin and endotoxin also cause the increase of neutrophils. For classification of these types of blood cells we have used convolutional neural networks (CNN).

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Fingernail Disease Detection Using Image Processing Matlab Project Source Code | Final Year Project Code

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 ABSTRACT

          This project gives idea to predict diseases using the color of the nail at early stage of diagnosis. The main aim of our project is to analyze the disease without causing harm to humans. In earlier traditional system of disease detection, doctors observe the nails of patients and will predict the disease. Many diseases can be identified by analyzing nails of patients. But it is difficult for human eyes to differentiate the slight changes in color. So it is less accurate and time consuming. Our proposed system can be quite useful to overcome this issue since it is fully computer based. The input to the proposed system is image of nail. The system will process the nail image and will extract the nail’s features to diagnose the disease. Human nail consist of various features, our proposed system uses nail color changes to diagnose the disease. Here, first training set data is prepared from nail images of patients with specific diseases. This training data set is compared with extracted feature from input nail image to obtain the result. In our experiment, we found that training set data are correctly matched with color feature of nail image results. It is focused on the system of image recognition on the basis of color analysis. The proposed system is based on the algorithm which automatically extracts only nails area from scanned back side of palm (Region of Interest). These selected pixels are processed for further analysis using median filters. The system is fully computer based, so even small discontinuities in color values are observed, and we can detect color changes in the initial stage of disease.

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Breast Cancer Detection Using Deep Learning CNN Matlab Project With Source Code

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 ABSTRACT

        The World Health Organization's International agency for Research on Cancer in Lyon, France, estimates that more than 150 000 women worldwide die of breast cancer each year. Organ chlorines are considered a possible cause for hormone-dependent cancers . Detection of early and subtle signs of breast cancer requires high-quality images and skilled mammographic interpretation. In order to detect early onset of cancers in breast screening, it is essential to have high-quality images. Radiologists reading mammograms should be trained in the recognition of the signs of early onset of, which may be subtle and may not show typical malignant features. Mammography screening programs have shown to be effective in decreasing breast cancer mortality through the detection and treatment of early onset of breast cancers. In this project we have used deep learning convolutional neural network for classification of breast cancer like Benign Cancer, Malignant Cancer and Normal Breast.

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Prof. Roshan P. Helonde 
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Lung Cancer Detection Using Deep Learning CNN Matlab Project With Source Code Final Year Project

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 ABSTRACT

             Lung cancer prevalence is one of the highest of cancers. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Lung Cancer Detection done using deep learning CNN.

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Prof. Roshan P. Helonde
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Glaucoma Detection Using Image Processing Matlab Project With Source Code | Final Year Project Code

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 ABSTRACT

                Computational techniques have great impact in the field of Medicine and Biology. These techniques help the medical practitioners to diagnose any abnormality in advance and provide fruitful treatment. Retinal image analysis has been an ongoing area of research. Automated retinal image analysis aid the ophthalmologists in detecting abnormalities in the retinal structures namely optic disc, blood vessels, thus diagnosing sight threatening retinal diseases such as Glaucoma and Retinopathy. Glaucoma is the major cause of blindness in working population. Glaucoma is characterized by increased intra-ocular pressure inside the eye leading to changes in the optic disc and optic nerve. It does not reveal its symptoms until later stage. Hence, regular screening of the patients is required to identify the disease, thus demanding high labor, time and expertise. Thus, computational techniques are sought for their analysis. In this project, identification of Glaucoma is carried out through computational techniques namely image processing. As the changes in the profile of optic disc act as a biomarker for the onset of the disease, optic disc is segmented through image processing techniques. Optic disc is the brightest part portrayed as oval structure in the retinal fundus image. It encompasses optic cup, which is the brightest central part, optic rim, the surrounding pale part and the blood vessels. All these structures are segmented and their properties are elicited. Then, properties of the disc, cup and blood vessels within optic disc are mined to design a learning model for prediction of Glaucoma.

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Prof. Roshan P. Helonde
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Emotion Recognition From Facial Expression Using Image Processing Matlab Project With Source Code | Final Year Project Code

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 ABSTRACT

            This project objective is to introduce needs and applications of facial expression recognition. Between Verbal & Non-Verbal form of communication facial expression is form of non-verbal communication but it plays pivotal role. It express human perspective or filling & his or her mental situation. A big research has been addressed to enhance Human Computer Interaction (HCI) over two decades. This project includes introduction of facial emotion recognition system, Application, comparative study of popular face expression recognition techniques & phases of automatic facial expression recognition system. Emotional aspects have huge impact on Social intelligence like communication understanding, decision making and also helps in understanding behavioral aspect of human. Emotion play pivotal role during communication. Emotion recognition is carried out in diverse way, it may be verbal or non-verbal .Voice (Audible) is verbal form of communication & Facial expression, action, body postures and gesture is non-verbal form of communication. While communicating only 7% effect of message is contributes by verbal part as a whole, 38% by vocal part and 55% effect of the speaker’s message is contributed by facial expression. For that reason automated & real time facial expression would play important role in human and machine interaction. Facial expression recognition would be useful from human facilities to clinical practices. Analysis of facial expression plays fundamental roles for applications which are based on emotion recognition like Human Computer Interaction (HCI), Social Robot, Animation, Alert System & Pain monitoring for patients.

PROJECT OUTPUT


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Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
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Soybean Plant Diseases Detection Using Image Processing Python Project Source Code || Final Year Project Code

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 ABSTRACT

          India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This project proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this project, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases like Soybean Rust, Powdery Mildew, Frogeye Leaf Spot, Downy Mildew, etc.

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Prof. Roshan P. Helonde
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Vegetable Leaf Recognition Using Image Processing Python Project With Source Code Final Year Project Code

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 ABSTRACT

            Recognizing plants is a vital problem especially for biologists, agricultural researchers, and environmentalists. Plant recognition can be performed by human experts manually but it is a time consuming and low-efficiency process. Automation of plant recognition is an important process for the fields working with plants. This project presents an approach for plant recognition using leaf images. In this study, the proponents demonstrated the development of the system that gives users the ability to identify vegetables based on photographs of the leaves taken with a high definition camera.  At the heart of this system is a modernize process of identification, so as to automate the way of identifying the vegetable plants through leaf image and digital image processing. The output parameters are used to compute well documented metrics for the statistical and shape. Base on the study, the following conclusion are drawn: The system can extract the physical parameters from the leaf’s image that will be used in identifying Vegetable`s. From the extracted leaf parameters, the system provides the statistical analysis and general information of the identified leaf. 

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Prof. Roshan P. Helonde
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Currency Recognition Using Image Processing Matlab Project With Source Code | Final Year Project Source Code

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ABSTRACT

           The appearance of the currency is part of this development and it is affected directly, where there is exploited in incorrect form by copying the currency in a manner similar to the reality. Therefore, it became necessary to implement a proposal for being a suitable as solution not inconsistent with the different cultures, time and place. This clear through add the watermarks inside currency, which is difficult to be copied. At the same time, this watermarks may be visible to the naked eye so can easily inferred or it is invisible. However the high resolution imaging devices can copy these additions. In this research, we have proposed a system to distinguish the currencies by the program that working a submission inferred to the watermark by feature extraction determined the type of currency. In addition to, it determined category of the currency. Benefit of it, is reducing as much as possible the spread of counterfeit currency and this system can be used by any user wants to make sure of the currency.

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Prof. Roshan P. Helonde
Mobile: +91-7276355704
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Skin Disease Detection Using Deep Learning CNN | Skin Disease Classification Using Deep Learning CNN Matlab Project With Source Code

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ABSTRACT

              Skin diseases are hazardous and often contagious, especially melanoma, eczema, and impetigo. These skin diseases can be cured if detected early. The fundamental problem with it is, only an expert dermatologist is able to detect and classify such disease. Sometimes, the doctors also fail to correctly classify the disease and hence provide inappropriate medications to the patient. Our research proposes a skin disease detection method based on Deep Learning CNN Techniques. Our system is Personal Computer based so can be used even in remote areas. The patient needs to provide the image of the infected area and it is given as an input to the application. Image Processing and deep learning cnn techniques process it and deliver the accurate output.

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Prof. Roshan P. Helonde
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Wheat Plant Leaf Disease Detection Using Image Processing Python Project With Source Code

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 ABSTRACT

                    Now-a-days wheat plants are getting infected by different types of diseases very rapidly. It is must to come up with new system to single out diseases. It is must to design and implement such a system that can easily find out the diseases infected by plants. In India many crops are cultivated, out of which wheat being one of the most important food grain that this country cultivates and exports. Thus it can be seen that wheat forms a major part of the Indian agricultural system and India’s economy. Hence, maintenance of the steady production of above stated crop is very important. The main idea of this project is to provide a system for detecting wheat leaf diseases. The given system will find the disease on leaf image of a wheat plant through image processing this project is develop in python. Former algorithms are used for extracting vital information from the leaf and the latter is used for detecting the disease that it is infected with. This Project is developed in python.

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Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
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Gender Recognition from Speech Audio Signal Matlab Project With Source Code Final Year Project Code

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 ABSTRACT

             Signal is a physical quantity that varies with respect to the independent variable like time, space, etc. Signal values can be represented in zero’s and one’s. Processing of digital signal by using digital computer is called as Digital Signal Processing. According to Webster’s dictionary, speech is the expression or communication throughout in speakers. Speech is the most important thing to express our thoughts. Speech signal is used to communicate among people. It not only consists of the information but also carries the information regarding the particular speaker. From which the speaker is male or female can be recognized. The meaning of Gender Recognition (GR) is recognizing the gender of the person whether the speaker is male or female. The Information about gender, age, ethnicity, and emotional state are the important ingredients that give rich behavioral information. Such information can be obtained from the speech signal. In this project, an unknown speaker is compared to a database of some known speakers. The best matching system is taken as the recognition decision. From the Recognition decision we conclude whether the given voice sample is generated by a male or female.

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Prof. Roshan P. Helonde
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Wheat Leaf Disease Detection Using Image Processing Python Project With Source Code

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ABSTRACT

                    Now-a-days wheat plants are getting infected by different types of diseases very rapidly. It is must to come up with new system to single out diseases. It is must to design and implement such a system that can easily find out the diseases infected by plants. In India many crops are cultivated, out of which wheat being one of the most important food grain that this country cultivates and exports. Thus it can be seen that wheat forms a major part of the Indian agricultural system and India’s economy. Hence, maintenance of the steady production of above stated crop is very important. The main idea of this project is to provide a system for detecting wheat leaf diseases. The given system will find the disease on leaf image of a wheat plant through image processing this project is develop in python. Former algorithms are used for extracting vital information from the leaf and the latter is used for detecting the disease that it is infected with. 

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Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
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Kidney Stone Detection Using Image Processing Python Project With Source Code | Final Year Project Code

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ABSTRACT

            Nowadays, kidney stone has become a major problem and if not detected at an early stage then it may cause complications and sometimes surgery is also needed to remove the stone. So, to detect the stone and that too precisely paves the way to image processing because through image processing there is a tendency to get the precise results and it is an automatic method of detecting the stone. This project presents a technique for detection of kidney stones using image processing. This project is develop in python.

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Prof. Roshan P. Helonde
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Walnut Disease Detection Using Image Processing Python Project With Source Code | Final Year Project Code

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 ABSTRACT

         We present a system where image analysis studies aiming at automated detection of disease that may be present in walnut by using image processing. In this work, we propose a convolution image processing model that has been used images of walnut. The project's aim is to build a fully automated walnut disease detection using image processing in this categories into two category one is healthy walnut and secondly walnut affected with disease. This project is developed in python.

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Prof. Roshan P. Helonde
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Currency Recognition Using Image Processing Matlab Project With Source Code | IEEE Based Project

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 ABSTRACT

           The appearance of the currency is part of this development and it is affected directly, where there is exploited in incorrect form by copying the currency in a manner similar to the reality. Therefore, it became necessary to implement a proposal for being a suitable as solution not inconsistent with the different cultures, time and place. This clear through add the watermarks inside currency, which is difficult to be copied. At the same time, this watermarks may be visible to the naked eye so can easily inferred or it is invisible. However the high resolution imaging devices can copy these additions. In this research, we have proposed a system to distinguish the currencies by the program that working a submission inferred to the watermark by feature extraction determined the type of currency. In addition to, it determined category of the currency. Benefit of it, is reducing as much as possible the spread of counterfeit currency and this system can be used by any user wants to make sure of the currency.

PROJECT OUTPUT

PROJECT VIDEO

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Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
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Melanoma Skin Cancer Detection Using Image Processing Python Project With Source Code | Final Year Project

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 ABSTRACT

         Skin cancer also known as melanoma it is one of the deadliest form of cancer if not recognized in time. Since the pigmented areas/moles of the skin can be nicely observed by simple, non-invasive visual inspection the clinical protocols of its recognition also consider several visual features. Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, melanoma classification for skin cancer diagnosis. Activation functions play an important role in the performance of deep neural networks for image recognition problems as well as medical image classification. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration on cheeks is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this research, extract the information of cheek’s discoloration lesion by measuring the pixel number of lesion on skin.

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Prof. Roshan P. Helonde
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WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
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Types of Brain Tumor Detection Using CNN Python Project With Source Code | IEEE Based Projects

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 ABSTRACT

            A tumor can be defined as a mass which grows without any control of normal forces. Real time diagnosis of tumors by using more reliable algorithms has been an active of the latest developments in medical imaging and detection of brain tumor in MR scan images. Hence image segmentation is the fundamental problem used in tumor detection. Image segmentation can be defined as the partition or segmentation of a digital image into similar regions with a main aim to simplify the image under consideration into something that is more meaningful and easier to analyze visually. Brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Magnetic Resonance Image (MRI) is the commonly used device for diagnosis. In MR images, the amount of data is too much for manual interpretation and analysis. During the past few years, brain tumor segmentation in Magnetic Resonance Imaging(MRI) has become an emergent research area in the field of medical imaging system. Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. Image processing is an active research area in which medical image processing is a highly challenging field. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions from the medical images. 

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Prof. Roshan P. Helonde
Mobile: +91-7276355704
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Email: roshanphelonde@rediffmail.com
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Skin Disease Identification Using Image Processing Matlab Project With Source Code | Final Year Project

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 ABSTRACT

         Skin cancer also known as melanoma it is one of the deadliest form of cancer if not recognized in time. Since the pigmented areas/moles of the skin can be nicely observed by simple, non-invasive visual inspection the clinical protocols of its recognition also consider several visual features. Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, melanoma classification for skin cancer diagnosis. Activation functions play an important role in the performance of deep neural networks for image recognition problems as well as medical image classification. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration on cheeks is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this research, different imaging techniques like preprocessing method, segmentation and morphological operations are used to analyze and extract the information of cheek’s discoloration lesion by measuring the pixel number of lesion on skin. The image analyzing results are visually examined by the skin specialist.

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Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
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Types Of Medicinal Leaf Recognition Using Image Processing Python Project With Source Code || IEEE Based Projects

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 ABSTRACT

            Image processing is the recent growing technique in the world. It refers to the processing of digital images by means of a digital computer. Images play a major role in human perception. Image analysis is between image processing and computer vision. There are no clear boundaries for in continuum with image processing and computer vision. The useful paradigms for computerized process in determining the image is classified in to three types are low-level process: involve primitive operation such as image pre processing to reduce noise, image enhancement and image sharpening, mid-level: image segmentation and high-level: making sense of image recognized. Here image processing technique is used for medicinal purpose by extracting the features of herbal leaf and authenticating it medicinal qualities. Leaves play the major role for the classification of plants. The sample leaves are taken from various places, plants and shape. The image is captured and further work is carried out. Comparison of test sample image with reference not only requires an experienced but is subjective and prone to human errors. By applying advanced technique of image processing and utilizing the capabilities of the recent advanced computing and data/image storage facilities and the use of computer techniques for analyzing the shape, texture, color, aspect ratio, vein structure, entropy, compactness and so on. The aim of the work is to classify and authenticate the medicinal plant materials and herbs widely used for Indian herbal medicinal preparation. The quality and authenticity of these leaves are to be ensured for the preparation of herbal medicines. The medicinal plant leaves are thoroughly screened, analyzed and compared with the database to give the correct measures of the texture to which category the leaf belongs to.

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Prof. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com
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WhatsApp: +917276355704
Email: roshanphelonde@rediffmail.com

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