Cotton Leaf Disease Detection and Classification Using CNN Convolution Neural Network Matlab Project With Source Code

ABSTRACT

               India is the second largest in population and many crops Indian farmers can cultivate and Most of the farmers cultivate cotton in large numbers but the cotton leaf disease is the major problem in the past few decades and that results in a loss of crops, their productivity and money as well. General observation by farmers may be time-consuming, expensive and sometimes inaccurate. The Cotton leaf Disease Detection and identifying the disease at an early stage is a very difficult task for the farmers. If the infection or disease on the crops was not identified by the farmers at the initial level then it will be harmful to the crops as well as for farmers. The main purpose of farming is to yield healthy crops with none disease present. It’s very difficult to visually presume the health of cotton leaf. To beat this problem, a machine learning based approach is proposed which can assess the image of the leaf of the plant and detect the disease and therefore the quality of the cotton using machine learning approach. For availing this user got to upload the image then with the assistance of image processing we can get a digitized colour image of a diseased leaf then we can proceed with applying CNN Convolution Neural Network to predict cotton leaf disease. Every disease on a crop has different features which are extracted at each layer of the convolution network. The goal of this application is to develop a system that recognizes cotton crop diseases. During this user has got to upload a picture on the system, Image processing starts with the digitized color image of the diseased leaf. Finally by applying the CNN disease are often predicted. The detection of plant disease may be a vital factor to stop a significant outbreak. Most plant diseases are caused by fungi, bacteria, and viruses. Traditionally farmer visually checks the disease. This project presents an approach for careful detection of diseases and timely handling to stop the crops from heavy losses. The diseases on cotton are a critical issue that creates the sharp decrease within the production of cotton. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Gender Recognition from Speech Audio Signal Using Matlab Project With Source Code Final Year Project

 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. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Diabetic Retinopathy Detection Using CNN Convolutional Neural Network Matlab Project With Source Code | Final Year Project

ABSTRACT

           Diabetic Retinopathy (DR) is a chronic health disease which requires early detection and treatment. It is important to identify DR using an intelligent system for faster prediction since manual examination and detection of the disease are unreliable and highly prone to error. Therefore, various researchers and medical experts have adopted and approached for advanced feature extraction and image classification, for early DR detection. Diabetic Retinopathy is a consequence of diabetes that affects the eyes. Damaged blood vessels in the retina, a light-sensitive tissue, are the primary cause of DR. If the patient has a long-term case  of diabetes and  the blood sugar  level is  not regulated consistently, the odds of this  issue developing in the eye increase.  Diabetic  Retinopathy is  one  of  the most  common causes  of  blindness  in  the Western  countries. Preventing Diabetic Retinopathy has  found to be quite beneficial when people with  diabetes are  monitored regularly. This  process is  shown to be essential if Diabetic Retinopathy is discovered in its early stages due to the availability of treatment. Diabetic Retinopathy, the main cause of blindness among working-age adults, affects millions of individuals. Diabetic  Retinopathy  is  a  medical  disorder  in  which  diabetes  mellitus  causes  damage  to  the  retina.  Diabetic Retinopathy  is diagnosed  using  colored  fundus  images,  which  requires  trained clinicians  to recognize  the  presence  and importance  of  several tiny  characteristics,  making  it a  time-consuming  task.  We present  a convolutional neural network CNN based  technique to  detect diabetic retinopathy in fundus images in this project. This project is developed in matlab.

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Lung Nodule Detection Using CNN Convolutional Neural Network Matlab Project With Source Code Final Year Project

 ABSTRACT

        Lung nodule prevalence is one of the highest of cancers. One of the first steps in lung nodule diagnosis is sampling of lung tissues. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of nodule cells in the chest. Lung nodule 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 nodule in patients. Hence, there is need for a system that is capable for detecting lung nodule automatically from images of lungs. This method will improve the efficiency for lung nodule detection. The aim of this project is to detect a lung nodule detection system based on analysis of lung images using digital image processing. Lung images parameters extracted and classified using convolutional neural network (CNN). This method is implemented to detection of lung nodule of lung samples images in matlab.

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Prof. Roshan P. Helonde

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Currency Recognition Using Image Processing Matlab Project Source Code | Final Year Project Code

 ABSTRACT

             The Reserve Bank is the one which issue bank notes in India. Reserve Bank, changes the design of bank notes from time to time. 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. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Wall Crack Detection Using Image Processing Matlab Project With Source Code | Final Year Project

 ABSTRACT

            Engineering structures like concrete surface, beams are often subjected to fatigue stress, cyclic loading, that leads to the cracks that usually initiate at the microscopic level on the structure’s surface. The cracks on the structure reduce local stiffness and cause material discontinuities. Early detection allows preventive measures to be taken to prevent damage and possible failure. Crack detection is the process of detecting the crack on the wall surface using image processing techniques. For fast and reliable surface defect analysis, Automatic crack detection is developed instead of the slower subjective traditional human inspection procedures. Thereby a safer survey methodology is adapted. Automatic crack detection is very effective for Non-destructive testing. Cracks on the concrete surface are one of the earliest indications of degradation of the structure which is critical for the maintenance as well the continuous exposure will lead to the severe damage to the environment. Manual inspection is the acclaimed method for the crack inspection. In the manual inspection, the sketch of the crack is prepared manually, and the conditions of the irregularities are noted. Since the manual approach completely depends on the specialist’s knowledge and experience, it lacks objectivity in the quantitative analysis. So, automatic image-based crack detection is proposed in this project. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Medical Image Fusion Using DWT Discrete Wavelet Transform Matlab Project With Source Code | Final Year Project

ABSTRACT

            Different medical imaging techniques such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI) provide different perspectives for the human body that are important in the physical disorders or diagnosis of diseases .To derive useful information from multimodality medical image data medical image fusion has been used. In the medical field different radiometric scanning techniques can be used to evaluate and examine the inner parts of the body. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide as much details as possible for the sake of diagnosis. The objective of image fusion is to merge information from two images of the same image. The resultant image after image fusion is more suitable for human and machine perception and further helpful for image-processing tasks such as segmentation, feature extraction and object recognition. This project mainly presents image fusion using wavelet method for multispectral data and high-resolution data conveniently, quickly and accurately in MATLAB. Wavelet toolbox with abundant functions, provide a quick and convenient platform to improve image visibility. The work covers the selection of wavelet function, the use of wavelet based fusion algorithms on CT and MRI medical images, implementation of fusion rules and the fusion image quality evaluation. Matlab Results show that effectiveness of Medical Image Fusion with Wavelet Transform on preserving the feature information for the test images.

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Prof. Roshan P. Helonde

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Plant Disease Detection Using CNN Convolutional Neural Network Matlab Project With Source Code Final Year Project

 ABSTRACT

            Today's better technologies have enabled people to provide the adequate nutrition and food needed to meet the needs of the world's growing population. If we talk about India unequivocally, 70% of the Indian people is directly or by suggestion related to the cultivating territory, which remains the greatest region in the country. If we explore the broader Picture According to Research Conducted by 2050 overall yield creation can augment by at any rate half putting more weight on the inside and out pushed and cultivating Sector. The greater part of the Farmers is poor and have no inclination in development which may incite hardships more essential than half because of pets and sicknesses of plant. Vegetables and fruits are common items and the principal agricultural things. Powerful dependence on engineered pesticides achieves the high substance content which creates in the earth, air, water, and shockingly in our bodies antagonistically influence the environment. At present, the conventional technique of visual inspection in humans by visual inspection makes it impossible to characterize plant diseases. Advances in computer vision models offer fast, normalized, and accurate answers to these problems. Early Disease Detection and pets are important for better yield and quality of crops. With Reduction in Quality of the agricultural Product, Disease Plant can lead to the huge Economic Losses to the Individual farmers. In country like India whose major Population is involved in Agriculture It is very important to find the disease at early stages. Faster and precise prediction of plant disease could help reducing the losses. Image  processing  techniques involves in this project are  image  acquisition,  image  preprocessing,  image  segmentation and classification done using CNN Convolutional Neural Network. This project is developed in matlab software. Development of  automatic detection  system using advanced  computer technology  such as  image processing help to support  the  farmers in  the identification  of diseases at an early or initial stage and provide useful information for its control.

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Prof. Roshan P. Helonde

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

 ABSTRACT

           Agriculture is the main backbone for most of the developing/developed countries; agriculture production itself is the main feed for ever growing populations and it is the major source of income for the rural people/farmers especially in India. In India farmers are called “the backbone of India”. The main aim of the proposed system is to detect, classify the diseases in paddy leafs. Paddy leaf diseases detection done using image processing and cnn techniques. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging. The spread of plant pests and diseases has increased dramatically in recent years. Globalization, trade and climate change, as well as reduced resilience in production systems due to decades of agricultural intensification, have all played a part. Plant pathogens can be fungal, bacterial, viral or nematodes and can damage plant parts above or below the ground.

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Prof. Roshan P. Helonde

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Image Encryption Using AES Algorithm Matlab Project With Source Code | AES Image Encryption and Decryption | Final Year Project

 ABSTRACT

           In today’s world data security is the major problem which is to be face. In order to secure data during communication, data storage and transmission we use Advance encryption standard(AES). AES is a symmetric block cipher intended to replace DES for commercial applications. The AES algorithms use to secure data from unauthorized user. The available AES algorithm is used for text data as well as for image data. In this project an image is given as input to AES encryption algorithm which gives encrypted output. This encrypted output is given as input to AES decryption algorithm and original image is regained as output. The AES algorithm for image encryption and decryption which synthesizes and simulated with the help of matlab.

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Prof. Roshan P. Helonde

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Mango Leaf Disease Detection Using Convolutional Neural Network Python Project With Source Code | Final Year Project Code

 ABSTRACT

            The mango fruit is popular because of its wide range of adaptability, high nutritional value, different variety, delicious taste and excellent flavor. The fruit contains vitamin A and vitamin C in a rich extent. The crop is prone to diseases like Powdery mildew, Anthracnose, Red Rust, Golmich, etc. Disorders may also impact the plant in the absence of effective case and control measures. These include change of form, biennial bearing, fall of fruit, black top, clustering, etc. The farmer must consult and take professional support for the prevention / control of diseases and crop disorder. New techniques of detecting mango disease are required to promote better control to avoid this crisis. By considering this, project describes image recognition which provides cost effective and scalable disease detection technology. This project further describes new convolutional neural network models which give an opportunity for easy deployment of this technology.

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Prof. Roshan P. Helonde

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Traffic Sign Recognition Using Deep Learning CNN Matlab Project With Source Code | Final Year Project Code

 ABSTRACT

               Traffic sign recognition is an important but challenging task, especially for automated driving and driver assistance. Its accuracy depends on two aspects: feature exactor and classifier. Current popular algorithms mainly use convolutional neural networks (CNN) to execute feature extraction and classification. Such methods could achieve impressive results but usually on the basis of an extremely huge and complex network. What’s more, since the fully-connected layers in CNN form a classical neural network classifier, which is trained by conventional gradient descent-based implementations, the generalization ability is limited. The performance could be further improved if other favorable classifiers are used in matlab.

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Prof. Roshan P. Helonde

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Malaria Detection From Blood Cell Using Deep Learning CNN Matlab Project With Source Code

ABSTRACT

            Malaria is an ancient disease present majorly in the tropical countries having a huge social, economic, and health burden. In recent times, as a result of climatic changes due to global warming, it is predicted to have unexpected effects on Malaria. Both increase and fluctuation in temperature affects the vector and parasite life cycle. An efficient diagnostics is essential for the proper medication and cure. Major clinical diagnostics to identify RBCs affected by malaria is based on microscopic inspection of blood smears, treated with reagents, which stains the malarial parasite. Malaria is one of the most common diseases caused by mosquitoes and is a great public health problem worldwide. Currently, for malaria diagnosis the standard technique is microscopic examination of a stained blood film. We propose use of  Neural Networks for the diagnosis of the disease in the blood cell. For this purpose features parameters are computed from the data obtained by the digital holographic images of the blood cells and is given as input which classifies the cell as the infected one or not. This project is developed in matlab using deep learning Convolutional Neural Network.

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Prof. Roshan P. Helonde

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Rice Leaf Disease Detection Using Image Processing Matlab Project with Source Code | Final Year Project Code

ABSTRACT

                Agriculture plays an important role in the economic growth of every country and so it is necessary to ensure its development. The spread of various diseases in rice plants has increased in recent years. There is a variety of plant pathogens such as viral, bacterial, fungal and these can damage different plant parts above and below the ground. Agriculture is the primary source of livelihood for about more than 50% of the Indian population and rice is one of the major food grains of India. It is observed that rice plant diseases are the major contributors to reduce the production & quality of food. Identification of such diseases may improve the production quality. This project gives an idea about deep learning algorithm which is used to detect deadly diseases in rice plants. Much research has been done to automate the rice plant disease detection process using images of the leaf. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Fruit Recognition Using Deep Learning CNN Matlab Project With Source Code | Fruit Classification Using Matlab Project

ABSTRACT

                 The fruit classification process is commercially important. Fruit production at harvest time is quite high. Classification of fruits according to their types and characteristics is usually done by hand and eye. This method can cause huge losses in terms of time, cost and labor. In the proposed study, fruit recognition is carried out by using image processing methods. In the project, the classification process Convolutional Neural Networks (CNN) deep learning model is use. This project is developed on Matlab platform.

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Prof. Roshan P. Helonde

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Melanoma Skin Cancer Detection Using Deep Learning CNN Matlab Project With Source Code | Final Year Project

  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. The image analyzing results are visually examined by the skin specialist.

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Prof. Roshan P. Helonde

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