Wheat Leaf Disease Classification Using CNN Convolutional Neural Network | Wheat Leaf Disease Detection Using Python Project With Source Code

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 using cnn convolutional neural network in python.

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Prof. Roshan P. Helonde

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Malaria Detection Using Deep Learning CNN | Malaria Parasite Classification Using Python Project | Final Year Major Projects

ABSTRACT

             Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. It is preventable and curable.  Malaria is a serious disease which is caused by the parasite of the genus plasmodium. It poses a global problem and warrants an automatic evaluation process because conventional microscopy which is considered the gold standard has proven to be inefficient and its results are hard to store and reproduce. In conventional microscopy the blood of a malaria infected patient is placed in a slide and is observed under a microscope. This is a time consuming and tiring process even with the involvement of an expert technician. In this study we propose a computerized diagnosis which will help in immediate detection of the disease so that proper treatment can be provided to the malaria patient. We propose the usage of image processing techniques to automate the process of parasite detection in blood samples of patients. The proposed system is robust and it is unaffected by exceptional circumstances and achieves high percentages of accuracy. This project is develop in python.

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Prof. Roshan P. Helonde

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Image Encryption And Watermarking Using DWT Algorithm | Image Encryption And Watermarking Using Matlab Final Year Project Source Code

ABSTRACT

          The use of Internet technology has led to the availability of different multimedia data in various formats. The unapproved customers misuse multimedia information by conveying them on various web objections to acquire cash deceptively without the first copyright holder’s intervention. Due to the rise in cases of COVID-19, lots of patient information are leaked without their knowledge, so an intelligent technique is required to protect the integrity of patient data by placing an invisible signal known as a watermark on the medical images. In this project encryption and watermarking is proposed using discrete wavelet transform algorithm on both standard and medical images. The project addresses the use of digital rights management in medical field applications such as encrypting and embedding the watermark in medical images. The various quality parameters are used to figure out the evaluation of the developed method. This project is developed in matlab.

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Prof. Roshan P. Helonde

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Email: roshanphelonde@rediffmail.com

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Video Steganography Using Matlab Project With Source Code | Secret Key Based Video Steganography Using Matlab IEEE Based Major Project Code

ABSTRACT

            Information security has become the area of concern as a result of widespread use of communication medium over the internet. This project focuses on the data security approach when combined with encryption and steganography techniques for secret communication by hiding it inside the multimedia files. The high results are achieved by providing the security to data before transmitting it over the internet. The files such as images, audio, video contains collection of bits that can be further translated into images, audio and video. The files composed of insignificant bits or unused areas which can be used for overwriting of other data. This Project explains the proposed algorithm using video steganography for enhancing data security. The Steganography, Cryptography and Digital Watermarking techniques can be used to obtain security and privacy of data. The steganography is the art of hiding data inside another data such as cover medium by applying different steganography techniques. While cryptography results in making the data human unreadable form called as cipher thus cryptography is scrambling of messages. Whereas the steganography results in exploitation of human awareness so it remains unobserved and undetected or intact. It is possible to use all file medium, digital data, or files as a cover medium in steganography. Generally steganography technique is applied where the cryptography is ineffective.

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Prof. Roshan P. Helonde

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Email: roshanphelonde@rediffmail.com

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Banana Fruit Grade Classification Using CNN Convolutional Neural Network | Fruit Grade Detection Using Matlab Project With Source Code | Final Year Major Project

ABSTRACT

         Many technological advancements have been developed for precise learning in every field. It is very important to analyze the data in order to extract some useful information. To standardize the quality of bananas it is essential to determine grade of bananas. This project present a Convolutional Neural Network architecture to classify the grade of banana fruits correctly. It learns a set of image features based on a data-driven mechanism and offers a deep indicator of banana’s grade. The computer vision techniques can potentially provide an automated and non-destructive tool for the classification of grading banana. In the field of artificial intelligence, recent advances in deep learning have led to breakthroughs in long-standing tasks such as vision-related problems of feature extraction, image segmentation, and image classification. Among all these techniques, convolutional neural network (CNN) is one of the most successful methods and has acquired a broad application in image classification. The process of transforming photos into the appropriate digital image data in order to extract particular information is known as image processing. Image processing refers to a strategy or approach for processing photos or images by modifying the chosen image data to get accurate information. Processing a picture is made simple by image processing. Utilizing tried-and-true image processing technologies helps increase fruit grading system. This project is developed in matlab using convolutional neural network in image processing.

PROJECT OUTPUT

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

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Email: roshanphelonde@rediffmail.com

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Papaya Fruit Disease Detection Using Image Processing | Papaya Fruit Disease Classification Using Python IEEE Project Based Final Year Major Projects

ABSTRACT

         The diseases are a major problem faced by all the farmers including fruit farmers. It is a threat for large farmlands because these diseases spread throughout the land and make the fruits inedible, which at the end impact badly on the farmer’s income. Hence early disease detection is very important for the farmers to prevent or to control the propagation of the diseases. The traditional method of fruit disease detection and identification is naked eye observation. Even if this method is sufficient for a home gardener, it is a very inefficient one that requires experience and expertise. As a solution for this problem several computerized approaches are being developed using Machine Learning and Image Processing techniques in the resent researches. In our proposed work, we considered Papaya fruit, as it is a very popular fruit cultivation in Sri Lanka. In this study we have implemented a computerized model for papaya disease identification using image processing. Among various diseases of papaya fruit, anthracnose, black spot, powdery mildew, phytophthora and ringspot were chosen. This intelligent system can easily detect the diseases and we are getting high accuracy up to 99 % to predict the papaya diseases.

PROJECT OUTPUT

PROJECT DEMO VIDEO

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Prof. Roshan P. Helonde

Mobile: +917276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Yoga Pose Classification Using Image Processing | Yoga Pose Detection Using Deep Learning Python Project With Source Code | Final Year Major Projects

ABSTRACT

            Yoga is an ancient art with a long history associated with India. It helps in making a person physically fit and provides mental peace at the same time. With the introduction of Covid-19, it is difficult to perform yoga in classes and if performed without guidance it may cause some serious injuries. Here we develop a system that identifies different yoga poses performed by users. This project is developed in python.

PROJECT OUTPUT

PROJECT DEMO VIDEO

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Yoga Pose Classification Using Deep Learning | Yoga Pose Detection Using Python Project With Source Code | Final Year Major Projects Codes

ABSTRACT

            Yoga is an ancient art with a long history associated with India. It helps in making a person physically fit and provides mental peace at the same time. With the introduction of Covid-19, it is difficult to perform yoga in classes and if performed without guidance it may cause some serious injuries. Here we develop a system that identifies different yoga poses performed by users. This project is developed in python using deep learning technique.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Image Watermarking Using DWT and DCT Matlab Project With Source Code | DWT DCT Based Image Watermarking Using Matlab Final Year Major Project

ABSTRACT

             The authenticity & copyright protection are two major problems in handling digital multimedia. The Image watermarking is most popular method for copyright protection by discrete Wavelet Transform (DWT) which performs 2 Level Decomposition of original (cover) image and watermark image is embedded in Lowest Level (LL) sub band of cover image. Inverse Discrete Wavelet Transform (IDWT) is used to recover original image from watermarked image. And Discrete Cosine Transform (DCT) which convert image into Blocks of M bits and then reconstruct using IDCT. In this project we have compared watermarking using DWT-DCT methods performance analysis on basis of PSNR, Similarity factor of watermark and recovered watermark. This project is developed in matlab.

PROJECT OUTPUT

PROJECT DEMO VIDEO

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

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Email: roshanphelonde@rediffmail.com

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Detection of Hard Exudates And Soft Exudates In Fundus Images Using Matlab | Hard And Soft Exudates Segmentation Using Matlab Final Year Major Project

ABSTRACT

           Diabetic retinopathy DR is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. Diabetic retinopathy is a complication of diabetes that is caused by damage to the small blood vessels of the retina. However, early detection and prompt treatment could prevent the vision loss caused by DR. Premature detection of DR is a challenging task, because patients with DR will have no symptoms until vision loss develops. Hence, person with diabetes should have a comprehensive retina screening once in every year. The DR starts with a mild non-proliferative diabetic retinopathy, which usually does not affect the vision. When vision is affected, it is the result of leaking of fluid in the blood vessels called exudates. Exudates can be hard exudates yellow spots seen in the retina and soft exudates pale yellow or white areas with ill-defined edges. An intelligent computer-aided DR detection system is developed for detecting the exudates in the fundus images. An automated tool can be used for assisting the ophthalmologists in the detection process, which gives attention to diabetic patients in receiving effective treatment. The screening tool utilizes intelligent computer algorithms to automatically analyze the features of DR and detect early signs of pathology.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Papaya Fruit Diseases Detection Using Convolutional Neural Network CNN | Papaya Fruit Diseases Classification Using Python Project Final Year IEEE Project Project Code

ABSTRACT

         The diseases are a major problem faced by all the farmers including fruit farmers. It is a threat for large farmlands because these diseases spread throughout the land and make the fruits inedible, which at the end impact badly on the farmer’s income. Hence early disease detection is very important for the farmers to prevent or to control the propagation of the diseases. The traditional method of fruit disease detection and identification is naked eye observation. Even if this method is sufficient for a home gardener, it is a very inefficient one that requires experience and expertise. As a solution for this problem several computerized approaches are being developed using Machine Learning and Image Processing techniques in the resent researches. In our proposed work, we considered Papaya fruit, as it is a very popular fruit cultivation in Sri Lanka. In this study we have implemented a computerized model for papaya disease identification using Convolutional Neural Network (CNN). Among various diseases of papaya fruit, anthracnose, black spot, powdery mildew, phytophthora and ringspot were chosen. This intelligent system can easily detect the papaya diseases. This project is developed in python.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +917276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Potato Leaf Disease Detection Using Image Processing | Potato Leaf Disease Classification Using Matlab Project With Source Code | Final Year Major Project

ABSTRACT

          Agriculture has been an essential food source. According to related statics, over 60% of the total earth population mainly depend on agriculture’s sources for their primary feed. Unfortunately, one of the disaster problems that affect badly on agriculture production is plant diseases. There are about 25% of agriculture production lost annually because of plant diseases. Late and Early Blight diseases are one of the most destructive diseases that infect potato crop. Although, the late and inaccurate detection of plant diseases increases the losing percentage for the crop. The main approach of our proposed system is to detect early the plant diseases to decrease the plant’s production losses by using a diagnosis and detection system based on the image processing. We used image processing techniques to extract the diseases features from the input images of the supported input images for classification purposes. This project is developed in matlab.

PROJECT OUTPUT

PROJECT DEMO VIDEO

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Prof. Roshan P. Helonde 

Mobile: +91-7276355704 

WhatsApp: +91-7276355704 

Email: roshanphelonde@rediffmail.com

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Grape Leaf Disease Detection Using Image Processing | Grape Leaf Disease Classification Using Matlab Project With Source Code | Final Year Major Project

ABSTRACT

         Grapes is basically a sub-tropical plant having excellent pulp content, rich color and is highly beneficial to health. Generally, it is very time-consuming and laborious for farmers of remote areas to identify grapes leaf diseases due to unavailability of experts. Though experts are available in some areas, disease detection is performed by naked eye which causes inappropriate recognition. An automated system can minimize these problems. The disease on the grape plant usually starts on the leaf and then moves onto the stem, root and the fruit. Once the disease reaches the fruit the whole plant gets destroyed. The approach is to detect the disease on the leaf itself in order to save the fruit. In our proposed system we have used image processing technique to detect grape leaf disease. Image of the grape leaf are pre-captured from camera of a mobile phone. This project is developed in matlab.

PROJECT OUTPUT

PROJECT DEMO VIDEO

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Apple Leaf Diseases Detection Using CNN Convolutional Neural Network | Apple Plant Disease Using Classification Using Python Project With Source Code Final Year Major Projects

 ABSTRACT

          Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimize losses to productivity caused by it. Plant diseases are a severe cause of crop losses in the agriculture globally. Detection of diseases in plants is difficult and challenging due to the lack of expert knowledge. Deep learning-based models provide promising ways to identify plant diseases using leaf images. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This project proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient images and designing a novel architecture of a deep convolutional neural network based on image processing to detect apple leaf diseases. This project is developed in python using convolutional neural network.

PROJECT OUTPUT

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Plant Disease Detection And Pesticide Suggestion Using Deep Learning | Plant Leaf Disease Classification Using Python Final Year Major Projects Codes

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 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 disease occurred in leaves in an accurate way with up to 98 percent accuracy . This project plant disease detection using deep learning cnn in image processing done in python. This project is implemented in python.

PROJECT OUTPUT

PROJECT DEMO VIDEO

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Pomegranate Fruit Disease Detection Using CNN Convolutional Neural Network | Pomegranate Fruit Disease Classification Using Matlab Project With Source Code Major Project

ABSTRACT

            Diseases in pomegranate fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this project, a solution for the detection and classification of pomegranate fruit diseases is proposed and experimentally validated. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of pomegranate fruit diseases using Convolutional Neural Network. 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 disease in fruits, high variance of defect types. 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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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Leukemia Blood Cancer Detection Using Image Processing | Leukemia Blood Cancer Detection Using Matlab Final Year Major Project Code

ABSTRACT

             Leukemia Blood cancer is the most prevalent and it is very much dangerous among all type of cancers. Early detection of blood cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose blood cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the technician. To obviate these problems, image processing techniques is use in this study as promising modalities for detection of Leukemia blood cancer. The accuracy of the diagnosis of blood cancer by using image processing will be yield a slightly higher rate of accuracy then other traditional methods and will reduce the effort and time. We first discuss the preliminary of cell biology required to proceed to implement our proposed method. This project presents a new automated approach for blood cancer detection and analysis from a given images of patient’s cancer affected blood sample. This project is developed in matlab.

PROJECT OUTPUT

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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OMR Answer Sheet Evaluation & Finding Exam Score Using Image Processing | OMR Answer Sheet Detection Using Matlab Project with Source Code

ABSTRACT

           This project aims to develop Image processing based Optical Mark Recognition sheet scanning system. Today we find that lot of competitive exams are been conducted as entrance exams. These exams consists of MCQs. The students have to fill the right box or circle for the appropriate answer to the respective questions. During the inspection or examining phase normally a stencil is provided to the examiner to determine the right answer to the questions. This is a manual process and a lot of errors can occur in the manual process such as counting mistake and many more. To avoid this mistakes OMR system is used. In this system OMR answer sheet will be scanned and the scanned image of the answer sheet will be given as input to the software system. Using Image processing we will find the answers marked to each of the questions. Summation of the marks & displaying of total marks will be also implemented. The implementation is done using Matlab

        In today’s modern world of technology when everything is computerized, the Evaluation exercise of examining and assessing the educational system has become absolute necessity. Today, more emphasis is on objective exam which is preferred to analyze scores of the students since it is simple and requires less time in the examining objective answer-sheet as compared to the subjective answer-sheet. This project proposes a new technique for generating scores of multiple-choice tests which are done by developing a technique that has software based approach with computer & scanner which is simple, efficient & reliable to all with minimal cost. Its main benefit to work with all available scanners, In addition no special paper & color required for printing for marksheet. To recognize & allot scores to the answer marked by of the student’s.

PROJECT OUTPUT

PROECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Skin Cancer Detection Using Convolutional Neural Network CNN | Melanoma Skin Cancer Classification Using Matlab Final Year Major Projects

ABSTRACT

          Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures for developing automated skin lesion segmentation. 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 is a hazardous fact as a symptom of human skin cancer 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 and are observed to be highly accurate. The visual results are presented in the project. This project will generate results faster than the traditional method, making this application an efficient and dependable system for dermatological cancer detection. Furthermore, this can also be used as a reliable real time teaching tool for medical students in the dermatology stream. This project is developed in matlab.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +917276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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OMR Answer Sheet Evaluation Using Matlab Project With Source Code Final Year Major Project

ABSTRACT

           This project aims to develop Image processing based Optical Mark Recognition sheet scanning system. Today we find that lot of competitive exams are been conducted as entrance exams. These exams consists of MCQs. The students have to fill the right box or circle for the appropriate answer to the respective questions. During the inspection or examining phase normally a stencil is provided to the examiner to determine the right answer to the questions. This is a manual process and a lot of errors can occur in the manual process such as counting mistake and many more. To avoid this mistakes OMR system is used. In this system OMR answer sheet will be scanned and the scanned image of the answer sheet will be given as input to the software system. Using Image processing we will find the answers marked to each of the questions. Summation of the marks & displaying of total marks will be also implemented.

        In today’s modern world of technology when everything is computerized, the Evaluation exercise of examining and assessing the educational system has become absolute necessity. Today, more emphasis is on objective exam which is preferred to analyze scores of the students since it is simple and requires less time in the examining objective answer-sheet as compared to the subjective answer-sheet. This project proposes a new technique for generating scores of multiple-choice tests which are done by developing a technique that has software based approach with computer & scanner which is simple, efficient & reliable to all with minimal cost. Its main benefit to work with all available scanners, In addition no special paper & color required for printing for marksheet. To recognize & allot scores to the answer marked by of the student’s. The implementation is done using Matlab.

PROJECT OUTPUT 

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Mango Leaf Disease Detection Using CNN Convolutional Neural Network | Mango Plant Disease Classification Using Python Final Year Major Projects

ABSTRACT

            Mango also called as The King of Fruits is one of the important fruit crops cultivated in different countries around the world. India produces about 40% of the global mango production and ranks first among the worlds mango producing countries. It is estimated that, pests and diseases destroy approximately 30 - 40% of the crop yield. The identification of plant diseases plays a vital role in taking disease control measures in order to improve the quality and quantity of crop yield. Automation of plant diseases is very much beneficial as it reduces the monitoring work in large farms. Leaves being the food source for plants, the early and accurate detection of leaf diseases is important. 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, etc. Disorders may also impact the plant in the absence of effective case and control measures. 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. This project is developed in python using Convolutional Neural Network.

PROJECT OUTPUT

PROJECT DEMO VIDEO

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Rice Leaf Disease Detection Using Image Processing | Rice Plant Disease Classification Using 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.

PROJECT OUTPUT

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Wheat Leaf Disease Detection Using Image Processing | Wheat Plant Disease Classification Using Python Project With Source Code | Final Year Major Project

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.

PROJECT OUTPUT

PROJECT DEMO VIDEO

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Palmprint Recognition Using Image Processing | Palmprint Classification Using Matlab Project With Source Code | Final Year Major Project

ABSTRACT

                 Palm  print  authentication  is  one  of  the  modern  bio-metric techniques, which employs the vein pattern  in  the  human palm  to  verify  the  person.  The merits  of  palm  vein  on classical  bio-metric  (e.g.  fingerprint,  iris,  face)  are  a  low risk  of  falsification,  difficulty  of  duplicated  and  stability. In  this  Project,  a  new  method  is  proposed  for  personal verification  based  on  palm  Print  features.  In  the propose method,  the  palm  vein  images  are  firstly  enhanced  and then  the  features  are extracted  by  using  bank  of  Gabor filters. Bio-metric   technology   refers   to   a pattern recognition system  which  depends  on  physical  or  behavioral  features for the  person  identification. This project is developed in matlab using image processing.

PROJECT OUTPUT

PROJECT DEMO VIDEO

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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Image Encryption and Decryption Using AES Algorithm | Secret Key Based AES Image Encryption and Decryption Using Matlab Project Source Code | Final Year Major 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

Mobile: +91-7276355704

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Email: roshanphelonde@rediffmail.com

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Medical Image Encryption and Decryption Using Matlab Project With Source Code | Medical Data Encryption and Decryption Using Matlab | Final Year Projects

ABSTRACT

             In recent years mostly all the health centers and hospitals use the wireless networks and internet for biomedical information exchanging, the secure of this information in not verified and cannot be grantee in such environment, the personality of patient and for security concerns inside such institutions there is a need for encryption system that can easily encrypt the biomedical data and it can be shared with other centers via internet without and concerns about privacy. Our system based on advanced encryption standard with encryption and decryption taking to consideration the criticality of data that been encrypted. Medical image security is very important issue in new world technologies with the internet of things revolution everything is connected to the internet and need to protected and authenticated, our project can encrypt the medical images for popular people or the critical situation patient that can help to protect the patients privacy by merging many techniques. This project is developed in matlab.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Cotton Leaf Disease Detection and Pesticide Suggestion Using Image Processing | Cotton Plant Disease Classification Using Python Project With Source Code | Final Year Major Project

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 color image of a diseased leaf then we can proceed with applying 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 using image processing in python.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +917276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Lung Cancer Detection Using Image Processing | Lung Cancer Classification Using Matlab Project With Source Code | Final Year Project

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 efficiency for lung cancer detection. The aim of this project is to design a lung cancer detection system based on analysis of ct image of lung using digital image processing. 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 lungs. This method will improve the efficiency for lung nodule detection. The aim of this project is to detect a lung cancer detection system based on analysis of lung images using digital image processing. Lung Cancer Detection done using image processing. This project is developed in matlab.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

Read More