Kapil dikshit ppt
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This document outlines a proposed plant leaf disease detection system using image processing on Android mobile phones. The system aims to help farmers easily and cost-effectively detect plant diseases, identify severity levels, and receive treatment suggestions. It will use algorithms like blob detection and HSV color modeling to analyze leaf images and determine diseases. The Android app is intended to provide an affordable solution to identify a variety of disease types and inform farmers in their local language.
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Detection of plant diseases
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
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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.
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1. The document proposes an IoT-based system using image processing and convolutional neural networks to detect and prevent crop diseases.
2. It involves taking images of crop leaves, extracting features using color filtering and segmentation, training a CNN model on the images, and using the model to identify diseases and provide remedies to farmers.
3. The system aims to help farmers detect diseases early without needing an expert, in order to reduce crop losses and improve agricultural productivity.
Pesticide Spraying Agricultural Robot
The document describes the development of an agricultural robot for pesticide spraying. The robot uses image processing techniques to detect plant diseases and then spray the appropriate pesticide. It is controlled wirelessly by an operator via an Android app. A webcam on the robot streams live video to the operator's PC. Images are analyzed using a MATLAB algorithm to identify diseases. If detected, the operator can command the robot to spray pesticides from onboard tanks using submersible pumps and nozzles. The system aims to help farmers more accurately detect and treat diseases early, reducing crop losses and increasing yields.
EVALUATION OF ANTIMICROBIAL SUSCEPTIBILITY TESTING
SEMI-AUTOMATED PROGRAM DEVELOPMENT FOR THE EVALUATION OF ANTIMICROBIAL SUSCEPTIBILITY TESTING
image analysis.pptx
This document discusses using image analysis and machine learning algorithms to detect plant diseases. It describes preprocessing images to extract features, selecting relevant features, training a random forest classifier on different plant datasets, and achieving an average accuracy of 93%. A web application was developed and deployed to demonstrate the model's ability to detect diseases from input leaf images in real-time. Future work could involve integrating sensors and remote sensing for automated disease detection.
IRJET - Plant Leaf Disease Detection and Automated Medicine using IoT
This document proposes a system to detect plant leaf diseases and automatically apply medicine using IoT. The system would use an agricultural robot equipped with image processing capabilities. It would capture images of plant leaves, identify any diseases present, and spray the appropriate pesticides. This aims to help farmers by continuously monitoring crops and identifying issues early, reducing workload and increasing yields. The system would analyze leaf images using techniques like segmentation and clustering to classify diseases. An SVM algorithm would then determine the proper treatment. This automated approach could help farmers by quickly detecting and treating plant diseases without constant physical monitoring of fields.
Plant disease detection system using image processing
This document describes a study that used image processing and convolutional neural networks to develop a system for detecting plant diseases from images of plant leaves. The researchers created a model using the PlantVillage dataset of over 55,000 photos of leaves with 38 different disease labels. They augmented the data and used a CNN architecture with convolutional, pooling, ReLU, and fully connected layers to achieve 95.3% accuracy in classifying disease labels, outperforming conventional detection methods. The goal was to help farmers efficiently identify diseases early and apply appropriate treatments to prevent crop loss and economic impacts.
COMPARATIVE ANALYSIS OF DIFFERENT MACHINE LEARNING ALGORITHMS FOR PLANT DISEA...
COMPARATIVE ANALYSIS OF DIFFERENT MACHINE LEARNING ALGORITHMS FOR PLANT DISEA...International Research Journal of Modernization in Engineering Technology and Science
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.The International Journal of Engineering and Science (The IJES)
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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.
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.
A Deep Learning Method for Plant Disease Diagnosis and Detection in Smart Agr...
Creating and training a CNN model from scratch is a tedious process, this model can be used to detect and classification of other plant disease too, by simply training the model using respected datasets
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Any company that uses labor on a large scale for general knowledge process work, where people are performing high-volume, highly transactional process functions, will boost their capabilities and save money and time with robotic process automation software.
Foliage Measurement Using Image Processing Techniques
Automatic detection of fruit and leaf diseases is essential to automatically detect the symptoms of diseases as early as they appear on the growing stage. This system helps to detect the diseases on fruit during farming , right from plan and easily monitoring the diseases of grapes leaf and apple fruit. By using this system we can avoid the economical loss due to various diseases in agriculture production. K-means clustering technique is used for segmentation. The features are extracted from the segmented image and artificial neural network is used for training the image database and classified their performance to the respective disease categories. The experimental results express that what type of disease can be affected in the fruit and leaf .
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1.project is based on the embedded system.
2.In the early years the only way to communicate.
3.that is operate on simple power supply using the 12v adapter.
IRJET- Crop Pest Detection and Classification by K-Means and EM Clustering
This document proposes a method for crop pest detection and classification using digital image processing techniques. The method uses K-means and EM clustering algorithms to segment cropped images based on color, then extracts features from the segmented regions. Support vector machines (SVM) are used to classify the pest types. The key steps are: 1) preprocessing images, 2) segmenting using K-means and EM clustering on color features, 3) extracting features from segmented regions, 4) classifying pest types using SVM. The goal is to automatically detect and identify crop pests, which could help farmers monitor fields and control pests early to increase crop yields.
IRJET- An Expert System for Plant Disease Diagnosis by using Neural Network
This document describes a proposed system to diagnose plant diseases using neural networks and image processing. The system would take an image of a plant leaf using a smartphone, extract features from the image like color, texture, and edges using preprocessing and segmentation algorithms. It would then use a support vector machine algorithm and the extracted features to predict the plant disease. It would also recommend pesticides and their costs to treat the predicted disease to help farmers identify effective treatment options. The goal is to develop an automated system to help identify plant diseases from images in order to benefit large-scale crop monitoring and disease detection.
IRJET- An Expert System for Plant Disease Diagnosis by using Neural Network
This document describes a proposed system to diagnose plant diseases using neural networks and image processing. The system would take an image of a plant leaf using a smartphone, extract features from the image like color, texture, and edges using preprocessing and segmentation algorithms. It would then use a support vector machine algorithm and the extracted features to predict the plant disease. It would also recommend pesticides and their costs to treat the predicted disease to help farmers identify effective treatment options. The goal is to develop an automated system to help identify plant diseases from images in order to benefit large-scale crop monitoring and disease detection.
Plant Disease Doctor App
The document describes a mobile application called Plant Disease Doctor App that uses convolutional neural networks and deep learning to identify plant diseases from images. The app allows users to take photos of diseased plant leaves or import images and receives a disease diagnosis along with management tips. The system was trained on a dataset of over 20,000 images of 15 plant species with 5 diseases each. It aims to make disease identification easier, faster and less reliant on experts physically examining plants. The application architecture involves users uploading images, a CNN model analyzing them and returning results, which are then matched to information from a database for display.
Similar to Kapil dikshit ppt (20)
IRJET- IoT based Preventive Crop Disease Model using IP and CNN
IRJET- IoT based Preventive Crop Disease Model using IP and CNN
EVALUATION OF ANTIMICROBIAL SUSCEPTIBILITY TESTING
EVALUATION OF ANTIMICROBIAL SUSCEPTIBILITY TESTING
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IRJET - Plant Leaf Disease Detection and Automated Medicine using IoT
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Plant disease detection system using image processing
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The International Journal of Engineering and Science (The IJES)
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IRJET - Plant Disease Detection using Decision Tree Algorithm and Automated D...
EARLY BLIGHT AND LATE BLIGHT DISEASE DETECTION ON POTATO LEAVES USING CONVOLU...
EARLY BLIGHT AND LATE BLIGHT DISEASE DETECTION ON POTATO LEAVES USING CONVOLU...
A Deep Learning Method for Plant Disease Diagnosis and Detection in Smart Agr...
A Deep Learning Method for Plant Disease Diagnosis and Detection in Smart Agr...
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Robotic process automation (RPA) is the application of technology that allows...
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IRJET- Crop Pest Detection and Classification by K-Means and EM Clustering
IRJET- An Expert System for Plant Disease Diagnosis by using Neural Network
IRJET- An Expert System for Plant Disease Diagnosis by using Neural Network
IRJET- An Expert System for Plant Disease Diagnosis by using Neural Network
IRJET- An Expert System for Plant Disease Diagnosis by using Neural Network
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Kapil dikshit ppt
- 1. Plant Leaf Disease Detection System
- 2. INDEX Introduction Problem statement Purpose Scope Exiting system Literature survey Proposed system Proposed system architecture Product function ER – diagram Activity diagram(UML) Sequence diagram(UML) Blob Detection algorithm HSV Color model algorithm Hardware and software requirement Conclusion
- 3. INTRODUCTION Plants have become an important source of energy, and are a fundamental piece in the puzzle to solve the problem of global warming. There are several diseases that affect plants with the potential to cause devastating economic, social and ecological losses. In this context, diagnosing diseases in an accurate and timely way is of the utmost importance.
- 4. PROBLEM STATEMENT To make an efficient use of Image Processing by using Android Mobile which reduces time and Cost for Farmer to detect the disease and suggest the fertilizers for Plant .
- 5. PURPOSE There are many issues to farmers regarding diseases of plants, many times they do not get proper guidance to detect and cure diseases of plants. Proposed system helps user in detection and prevention of plant diseases with the use of Android application, which is very useful, simple and efficient technology can be used by any user facing problem related to plant disease.
- 6. SCOPE In Agriculture field all the peoples: Farmer SHOPKEEPERS OF FERTILIZER STUDENTS IN AGRICULTURE FIELD SCIENTIST
- 7. EXISTING SYSTEM Existing System: 1) Detects insects of only one color. 2) Little Image processing algorithms used. 3) Traditional approaches were used. 4) Lower Accuracy
- 8. LITERATURE SURVEY There are several types of Plant diseases symptoms like: • Bacterial Disease, • Fungal Disease and • Viral Disease
- 10. PROPOSED SYSTEM OBJECTIVES OF PROPOSED SYSTEM The objectives are as follows: 1.To make an efficient use of image processing techniques. 2.Provide solution with least hardware requirement. 3.To develop an Android application that is cost efficient, as android phones are widely available at low costs. 4.Minimize the use of resources as farmers can’t afford costly equipment. 5.Easy to use and accurate so that farmers can adopt the application quickly. 6.Output display on android mobile app.
- 11. PROPOSED SYSTEM ARCHITECTURE AND EXPECTED OUTPUT Features of proposed system are This system identifies infestation of any color by adjusting the intensity The Image Processing approach used by us covers farmhouse trees as well as farm trees We are introducing an android application system which will display the information in Hindi or English. Fig 1. Plant disease detection
- 12. PRODUCT FUNCTION Proposed system uses flow which includes: 1. Image is converted to HSV format. 2. Disease parts of plan leaf will be observed and HSV range will be determined. 3. Blobs(rectangle) will be drawn around detected disease area. 4. Plant disease will be determined based on detected disease area and HSV value. 5. System will able to give output in the form severity like low medium and high.
- 13. ER - DIAGRAM Fig 2. ER -diagram
- 14. ACTIVITY DIAGRAM Fig 3. ACTIVITY-diagram
- 15. SEQUENCE DIAGRAM Fig 4. SEQUENCE-diagram
- 16. USE CASE DIAGRAM fig.5 USE CASE-diagram
- 17. BLOB DETECTION ALGORITHM 1 Blob detection algorithm: A blob is a region of an image in which some properties are constant or approximately constant,.Blob is rectangular area. Applied mean filter: 1)Thresholded image using adaptive thresholding (returning a binary image). 2)Removed lines by checking connectivity, all lines are connected with above 100 pixels (for better connectivity mean filter was applied first). 3)Now since only blobs were remaining, their locations were identified (with grayvalue=255) and corresponding pixels were taken in original image.
- 18. HSV COLOR ALGORITHM HSV Color model:
- 19. HARDWARE AND SOFTWARE REQUIREMENTS Hardware : 1. Android mobile phone(4.0 & above) with camera module 2. RAM 8 GB Software : 1. Operating system : Windows version 7 2. Language : Android 3. IDE/Software with version : Android sdk, Android studio 2 4. Open CV
- 20. CONCLUSION This proposed system describes different techniques of image processing for detecting plant diseases. The disease of the plant is known at at an early stage and the cure is suggested using different languages namely Hindi or English.