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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Types of Lung Cancer Detection Using Convolutional Neural Network (CNN) Matlab Project With Source Code

 ABSTRACT

             Lung cancer prevalence is one of the highest of cancers. One of the first steps in lung cancer diagnosis is sampling of lung images. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Types of lung cancer diagnosis in lung images using Convolutional Neural Network (CNN). 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 lung images. The aim of this project is to develop type of lung cancer detection system based on analysis of lung image using digital image processing. Lung images are classified for detecting types of lung cancer like Adenocarcinoma, Squamous Cell Carcinoma, Normal Lung and Large Cell Carcinoma using Convolutional Neural Network (CNN).

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Prof. Roshan P. Helonde

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Fingerprint Recognition Using Image Processing | Matlab Project With Source Code

 ABSTRACT

        Recently, monitoring and security have become an essential and important affair because the number of counterfeiters and hacker are increased for the conventional methods like Personal Identification Number (PIN) and passwords. The traditional methods suffer from some type of contraventions and breaches for example the unauthorized user can arrive to important data in a dedicated system to delete, change, or even steal it. For averting whole these concerns; the modern community directs to more credibility methods recently utilize the biometric-technologies. Biometrics provides more secure way of person authentication, they are difficult to be stolen and replicated. Biometrics method can be depicted as an automate technique to recognize person automatically based on his or her behavioral and/or physiological features. This technology has possessed a big amount of concern and care for security in almost all aspects of our daily life since person cannot forget or lose their physiological features in the way that they might lose password or an identity card. Biometric technologies have been developed for automatic recognizing of human identity depending on person special biological features, such as face, Iris, speech and fingerprint. The online security of authentication systems is not only a substitution of secret codes and passwords, but it is also related to securing and monitoring the system in different level of potential applications. This project was analyzed and evaluation Uni-modal biometric system based on fingerprint identification system. 

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Prof. Roshan P. Helonde

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Multifocus Medical Image Fusion Matlab Project With Source Code | Final Year Project Code

ABSTRACT

           The multi-focus image fusion method can fuse more than one focused image to generate a single image with more accurate description. The purpose of image fusion is to generate one image by combining information from many source images of the same scene. In this project, a multi-focus image fusion method is proposed with a hybrid pixel level obtained in the spatial and transform domains. The proposed method is implemented on multi-focus source images. The proposed method performance is evaluated in terms of PSNR, SNR and SSIM. The results show that the fusion quality of the proposed algorithm is better than obtained by several other fusion methods.

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Prof. Roshan P. Helonde

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Top 10 B.Tech Project With Source Code | Top 10 B.E Project With Source Code | Final Year Projects Image Processing Project With Source Code

 

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Prof. Roshan P. Helonde

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Python Code for Leukemia Blood Cancer Detection Using Neural Network CNN || Final Year Project Codes

 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 rate 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 photograph of patient’s cancer affected blood sample. The proposed method is using image improvement, image segmentation for segmenting the different cells of blood, edge detection and final decision of blood cancer using Convolutional Neural Network (CNN) .

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Prof. Roshan P. Helonde

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Python Code On Paddy Leaf Disease Detection and Pesticide Suggestion Using CNN | Final Year Project Codes

 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 and suggest pesticide to recover from disease in paddy leafs. Paddy leaf Diseases Classification done using Convolutional Neural Network (CNN) classifiers and then suggesting pesticide respectively. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Types OF Blood Cell Detection Using Convolutional Neural Network (CNN) Python Project With Source Code

 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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Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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