plant disease recognition method is proposed based on plant images abstract

G.bhadra is pursuing a B.Tech degree from MLR Institute of Technology in Hyderabad. He has received prizes for sports, quizzes and science projects in school. In college, he has received certificates for programming in C and Java. He is interested in working in an organization where he can enhance his knowledge and take on challenges. His hobbies include browsing the internet, playing cricket, listening to music, travelling and video gaming.

BRAIN COMPUTER INTERFACE Documentation

The document summarizes a technical seminar on brain-computer interfaces (BCI). It begins with certificates of completion and declarations. It then discusses the different types of BCIs, including invasive BCIs implanted in the brain, partially-invasive BCIs implanted in the skull, and non-invasive EEG-based BCIs. The document outlines how BCI works, involving signal acquisition, preprocessing, classification, and using the signals to control external devices. Limitations and applications are discussed, along with the present and future of BCI technology. The seminar provides an overview of BCI systems and their potential to enhance human-computer interaction.

FUN AND FOOD PPT

Currently there are no proper managed system which can give all the information’s easily at one place, if a person wants to know about the current event happening around him, then he have to use a pc to search related information and there are no proper website exists which provides all the information at the same place, then if he gets the event location even though he have to suffer a lot to reach that place, in the stadium after taking the seat if the person wants to eat something or order something he need to go to the food court nearby him which again need a lot of time in the searching process in the main time they may miss a lot of stuff in the show . This is what the existing unmanaged system look like. The Fun and Food application can manage all those things in a very good and efficient way, The application is very powerful and efficient that it can locate user’s required position and track all the nearby fun and food zones currently available, if user selects any zone the app will automatically provide the minimum detail about the zone i.e., minimum cost, entry fee such kind of detail are being displayed in the app. We will get the zones on the base of location which will be a viewed in a list which contains the entire fun and food zones at that particular location.

Analysis of historical movie data by BHADRA

Recommendation system provides the facility to understand a person's taste and find new, desirable content for them automatically based on the pattern between their likes and rating of different items. In this paper, we have proposed a recommendation system for the large amount of data available on the web in the form of ratings, reviews, opinions, complaints, remarks, feedback, and comments about any item (product, event, individual and services) using Hadoop Framework.

Braincomputerinterface ppt

This document provides an overview of brain-computer interfaces (BCI). It discusses the human brain and electroencephalography. It describes two approaches to BCI - pattern recognition based on mental tasks and operant conditioning based on self-regulation of EEG signals. The document outlines the hardware, software, and basic working process of BCI systems. It also covers feedback types, drawbacks, innovators in the field, and applications of BCI technologies. The conclusion evaluates experiments with an adaptive brain interface system.

5G NETWORK AND INTERNET OF THINGS doc

5th generation mobile networks or 5th generation wireless systems, abbreviated 5G, are the proposed next telecommunications standards beyond the current 4G/IMT-Advanced standards. An initial chip design by Qualcomm in October 2016, the Snapdragon X50 5G modem, supports operations in the 28 GHz band, also known as millimetre wave (mmW) spectrum. With 800 MHz bandwidth support, it is designed to support peak download speeds of up to 35.46 gigabits per second. 5G planning aims at higher capacity than current 4G, allowing a higher density of mobile broadband users, and supporting device-to-device, ultra reliable, and massive machine communications. 5G research and development also aims at lower latency than 4G equipment and lower battery consumption, for better implementation of the Internet of things INTERNET OF THINGS  . The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction

Smart card

This document presents a method for detecting plant leaf diseases using deep learning and image processing techniques. The method uses the AlexNet convolutional neural network model to analyze images of leaves from a dataset. The images are preprocessed, augmented, and classified by AlexNet to identify different leaf diseases. A graphical user interface is also proposed to provide preventative measures for the detected leaf diseases. The study aims to help farmers identify diseases early to minimize crop loss and improve agricultural efficiency through automatic disease detection.

IRJET- Semi-Automatic Leaf Disease Detection and Classification System for So...

This document presents a semi-automatic system for detecting and classifying leaf diseases in soybean plants. The system uses image processing and machine learning techniques. It first segments leaf images into clusters using k-means clustering. It then extracts color and texture features from the clusters. Support vector machines are used to classify leaves as healthy or diseased, and to further classify diseased leaves into categories like downy mildew or leaf blight. The system achieves acceptable average accuracy levels that are better than existing methods. It provides a way to identify leaf diseases early in an automated manner to improve crop yields and food security.

Plant Disease Detection System

The document describes a plant disease detection system that uses machine learning and computer vision techniques to identify and classify plant diseases from images of affected plants. It discusses how traditional visual inspection methods are time-consuming and inaccurate. The proposed system uses algorithms like VGG19 and CNN to analyze images of diseased plants captured by devices like drones and smartphones. It trains models on datasets of images labeled with diseases. The system is shown to accurately detect diseases with over 75% accuracy. It has the potential to help farmers and gardeners identify diseases early and improve plant health and agricultural productivity. Future work may include expanding the dataset and exploring additional deep learning models.

Plant Disease Detection System

The document describes a plant disease detection system that uses machine learning and computer vision techniques to identify and classify plant diseases from images of affected plants. It discusses how traditional visual inspection methods are time-consuming and inaccurate. The proposed system applies algorithms like VGG19 and CNN to analyze images of diseased plants captured using devices like drones and smartphones. It then evaluates the images to detect disease indicators and identify the specific disease. The system achieved 75.4% accuracy in testing and can help farmers and gardeners quickly and easily monitor plant health to treat diseases early. This can help improve agricultural productivity and sustainability. The document also reviews related works and provides details of the proposed system's methodology, algorithms, evaluation process and conclusions.

Diagnosis and treatment of crops

Aaraf chowdhury

This document discusses the development of an image processing algorithm to identify crop diseases in early stages. It proposes using a novel algorithm based on detecting "hot spots" in images combined with statistical inference methods to identify diseases under field conditions using mobile devices. The algorithm will be designed and validated using an accurately segmented dataset of images showing different crop diseases and unaffected leaf regions for comparison. A link to the dataset is provided.

IRJET- AI Based Fault Detection on Leaf and Disease Prediction using K-means ...

This document discusses using k-means clustering and image processing techniques to detect faults and diseases on leaves. It aims to identify problem areas on leaves, calculate the ratio of faulty to normal areas, and predict the disease type. The document provides background on the importance of increasing food production despite challenges from crop diseases. It also reviews related work using image segmentation, feature extraction, and algorithms like k-means clustering, neural networks and support vector machines to analyze leaf images for disease detection. The proposed method uses k-means clustering on MATLAB to identify problem areas on leaves and calculate fault ratios to determine if leaves can be cured.

A Novel Machine Learning Based Approach for Detection and Classification of S...

This document presents a novel machine learning approach for detecting and classifying sugarcane plant diseases using discrete wavelet transform (DWT). Existing methods use MATLAB and support vector machines with limited accuracy. The proposed method uses DWT for image segmentation to identify affected plant regions. It acquires images, pre-processes for noise reduction, segments using edge detection, extracts features from regions of interest, classifies diseases, and detects diseases based on image analysis and classification. The goal is to more accurately detect diseases early to control crop damage and losses. A dataset of 13 sugarcane diseases is used to evaluate the approach.

IRJET - E-Learning Package for Grape & Disease Analysis

This document presents a proposed e-learning system for detecting and classifying diseases in grape leaves using convolutional neural networks (CNNs). The system would involve taking images of grape leaves, pre-processing the images, extracting features using CNNs, and classifying the diseases. The researchers developed an algorithm using this process that could successfully detect and classify examined grape leaf diseases with 91% accuracy. The proposed system is intended to help farmers efficiently identify grape leaf diseases.

IRJET- Farmer Advisory: A Crop Disease Detection System

1. The document presents a system for early detection of crop diseases using remote sensing images. The system involves training a model on images of healthy and diseased crops, and then using the model to monitor crops and identify diseases. 2. When a disease is detected, the system will immediately notify farmers with an early alert message, allowing them to take timely action. This approach aims to reduce crop losses from diseases. 3. The system is described as using KNN and Canny Edge algorithms for disease identification and has two phases - training and monitoring/identification. Its significant aspect is the early detection of diseases as they begin to spread on crop leaves.

IRJET- Detection of Plant Leaf Diseases using Image Processing and Soft-C...

This document presents a method for detecting plant leaf diseases using image processing and soft computing techniques. It involves taking images of plant leaves using a digital camera, pre-processing the images, segmenting the images to identify infected regions, extracting features from the infected regions, and classifying the disease based on the features. The method was tested on various plant leaf image datasets with an accuracy of 63% and was able to identify diseases for tomatoes, corn, grapes, peaches and peppers. The automatic detection technique can help identify diseases at an early stage with less time and effort compared to manual detection methods.

Fruit Disease Detection And Fertilizer Recommendation

This document discusses a proposed system for fruit disease detection and fertilizer recommendation using image processing and convolutional neural networks (CNNs). It begins with an introduction to the importance of detecting fruit diseases early to prevent economic losses. It then reviews several existing related works that use techniques like CNNs, k-nearest neighbors, support vector machines, and image processing methods. The proposed system would capture images using a camera, preprocess the images, train a CNN model on a dataset of diseased and healthy fruit images to classify new images, and provide fertilizer/pesticide recommendations. The system is broken down into modules for the frontend user interface, data collection and preprocessing, model building using CNNs, and a backend for analysis and recommendations.

Mini Pro PPT.pptx

ToMuCh

This document describes a project to detect fruit diseases using artificial neural networks and image processing techniques. It proposes a system that uses OpenCV, k-means clustering, and a neural network to identify diseases by analyzing images of fruit. The system would have modules for creating and preprocessing datasets to train a model, as well as modules for users to register, upload images of fruit, and receive predictions of potential diseases. The goal is to build an accurate and low-cost solution to assist farmers in identifying diseases early and improving crop yields.

Fruit Disease Detection and Classification

This document proposes and experimentally validates a solution for detecting and classifying fruit diseases from images. The proposed approach uses K-means clustering for image segmentation, extracts features from the segmented image, and classifies the images using a Support Vector Machine (SVM). The experimental results show the proposed solution can accurately detect and automatically classify fruit diseases. It is intended to help farmers identify diseases early to improve crop management and reduce economic losses from diseases.

Updated_Review2_An Improved Convolutional Neural Network Model for Detection....

pammi113011

The document proposes an improved convolutional neural network model to detect diseases affecting the leaves, trunk, and overall health of arecanut plants. It involves collecting over 700 images of healthy and diseased arecanut plants to create a training dataset. An 80:20 split is used for training and testing the CNN model, which aims to distinguish between healthy and diseased plants and offer recommended remedies. The system would provide disease detection and remedy suggestions through a web application to help farmers.

Plant Diseases Prediction Using Image Processing

This document discusses a system for predicting plant diseases using image processing. Specifically, it focuses on predicting diseases that affect tomato plant leaves. The system uses convolutional neural network techniques to analyze images of tomato leaves and predict whether they have diseases like Late blight, bacterial, or viral infections. It discusses implementing various steps like pre-processing images, feature extraction, and using a CNN classifier to classify images as having a specific disease or being healthy. The goal is to help farmers quickly and accurately identify plant diseases from leaf images to improve crop management and reduce economic losses.

Plant Leaf Disease Detection and Classification Using Image Processing

The document summarizes a research paper on detecting and classifying plant leaf diseases using image processing techniques. It begins by discussing the importance of identifying plant diseases early. It then provides an overview of traditional identification methods and their limitations. Next, it describes how image processing can be used to extract features from leaf images and classify diseases using machine learning algorithms. The paper evaluates several studies that have achieved accuracy ranging from 80-99.8% using different approaches. It also discusses challenges like variable image quality and limited datasets, and potential solutions. Finally, it presents results showing accuracy of 95-99% for different techniques depending on the dataset and diseases studied.

DOC-20230807-WA0000..pptx

ayaz9565

EARLY BLIGHT AND LATE BLIGHT DISEASE DETECTION ON POTATO LEAVES USING CONVOLU...

This document proposes a method to detect early blight and late blight diseases on potato leaves using convolutional neural networks. It involves preprocessing leaf images, extracting features, and training a classifier to identify disease patterns and provide predictions. The proposed system is evaluated on performance metrics like accuracy, precision, and recall. Convolutional neural networks have achieved high accuracy in plant disease classification compared to traditional methods by automating feature extraction from images. This technique allows for early detection of diseases, helping farmers prevent crop damage and losses.

Plant Disease Detection and Severity Classification using Support Vector Mach...

This document discusses a study that used support vector machines (SVM) and convolutional neural networks (CNN) to detect plant diseases and classify their severity using images. The researchers trained their models on a dataset containing images of four plant species with different diseases. SVM was used for disease detection, achieving 80-90% accuracy. CNN models like DenseNet and EfficientNet were used to classify disease severity. The goal of the study was to help farmers identify plant diseases early to mitigate losses and improve food security.

IRJET - Plant Disease Detection using Decision Tree Algorithm and Automated D...

This document describes a system to detect plant diseases using image processing and automatically cure the disease. The system uses a decision tree algorithm to classify input leaf images into different disease categories with greater than 85% accuracy. Textural features are extracted from the images using a GLCM algorithm. If a disease is detected, the system will automatically spray fertilizers or pesticides to cure it, reducing the need for human labor. The system is designed to help farmers identify plant diseases early and treat them accurately to minimize crop losses and maximize yields.

Worldranking universities final documentation

With the upcoming data deluge of semantic data, the fast growth of ontology bases has brought significant challenges in performing efficient and scalable reasoning. Traditional centralized reasoning methods are not sufficient to process large ontologies. Distributed searching methods are thus required to improve the scalability and performance of inferences. This paper proposes an incremental and distributed inference method for large-scale ontologies by using Map reduce, which realizes high-performance reasoning and runtime searching, especially for incremental knowledge base. By constructing transfer inference forest and effective assertion triples, the storage is largely reduced and the search process is simplified and accelerated. We propose an incremental and distributed inference method (IDIM) for large-scale RDF datasets via Map reduce. The choice of Map reduce is motivated by the fact that it can limit data exchange and alleviate load balancing problems by dynamically scheduling jobs on computing nodes. In order to store the incremental RDF triples more efficiently, we present two novel concepts, i.e., transfer inference forest (TIF) and effective assertion triples (EAT). Their use can largely reduce the storage and simplify the reasoning process. Based on TIF/EAT, we need not compute and store RDF closure, and the reasoning time so significantly decreases that a user’s online query can be answered timely, which is more efficient than existing methods to our best knowledge. More importantly, the update of TIF/EAT needs only minimum computation since the relationship between new triples and existing ones is fully used, which is not found in the existing literature. In order to store the incremental RDF triples more efficiently, we present two novel concepts, transfer inference forest and effective assertion triples. Their use can largely reduce the storage and simplify the searching process.

mobile-jammer

This document appears to be a technical seminar report submitted by a student named N.DEEKSHITH for their bachelor's degree in computer science engineering. The report discusses mobile jammers and was conducted under the guidance of an associate professor named Mr. P.DAYAKAR at MLR Institute of Technology in Hyderabad, India. The report includes an abstract, introduction, working of mobile jammers, advantages and disadvantages.

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The document describes a plant disease detection system that uses machine learning and computer vision techniques to identify and classify plant diseases from images of affected plants. It discusses how traditional visual inspection methods are time-consuming and inaccurate. The proposed system applies algorithms like VGG19 and CNN to analyze images of diseased plants captured using devices like drones and smartphones. It then evaluates the images to detect disease indicators and identify the specific disease. The system achieved 75.4% accuracy in testing and can help farmers and gardeners quickly and easily monitor plant health to treat diseases early. This can help improve agricultural productivity and sustainability. The document also reviews related works and provides details of the proposed system's methodology, algorithms, evaluation process and conclusions.

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Deep learning for Precision farming: Detection of disease in plants

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Right now, in most of the countries, inside the people ’s wallet, they probably have a the couple of credit cards, an identification card, automatic machine teller cards (ATM card), and maybe a few other plastic cards. Without realizing it, these plastic cards havebecome a very important part of their life. Although smart card technology improves security and convenient but it is not used in a wide range in Middle East countries. User acceptance is vital for further development of any fresh technology and smart card technology as well. One of the factors that can effect on the acceptance of smart card technology is users’ awareness. The goal of this study is to present a general overview of smart card technology and identify the smart card’s benefits, features and characteristics and moreover, the level of users’ knowledge and awareness about smart card technology will be evaluated. In order to achieve this goal, a survey was conducted among the international students of University Technology Malaysia to measure their awareness of smart technology

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This document is a mini project report submitted in partial fulfillment of the requirements for a Bachelor of Technology degree in Computer Science and Engineering. It describes a project to create a "College Phone Book" application, with the goal of storing contact information for students and faculty at the college. The report includes sections on introduction, literature survey, requirements analysis, implementation, system design, coding, system testing, screenshots, limitations and future enhancements, and conclusion. It was created by four students under the guidance of an associate professor.

Parent communication register android application Coding

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In this new era of Science and Technology, computer is one of the most important components in our life. Works can be done in a better way by the help of computer. The Main aim of our project is to automate the attendance in the form of Android Mobile Application. Our intention is to establish a good communication between Student Mentor and parent.We have seen over the years that the process of manual attendance is being carried out across almost all educational institutions. The process is not only time consuming but also sometimes yield inefficient results in the false marking and calculation of attendance. We need not maintain pen and paper based attendance registers. Following this thought, we have proposed a Parent communication register android application APP by which we notify parents via SMS OR a call Notification system which is implemented on Android mobile application.This Android application will give the students attendance information and SMS notification feature whereby every parent will be periodically notified regarding his/her child attendance. Our system primarily focuses on building an efficient and user friendly Android mobile application. The application will be installed on the Mentor phone which runs android OS. It intends to provide an interface to the professor who will require a user id and password to carry out the task. Apart from that, the application would support strong user authentication and quick transmission of data.

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PURPOSE OF THIS PROJECT: This project is mainly used to decrease the time constrain to find all fun and food zones near to the user location.The main advantage of this application is the user can view all the fun and food zones at one place,now we have so many websites and applications which gives information only about food or fun individually.To overcome this disadvantage we developed an application which gives all the details about both fun and food zones based on user specified location so we Entitled this project as ‘FUN AND FOOD’ it is used to provide all fun and food zones near to location specified by the user.The user can view minimum details of nearest fun and food zones and user can also view the details of respective fun and food service provider.

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