Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
Plant Monitoring using Image Processing, Raspberry PI & IOTIRJET Journal
This document describes a plant monitoring system using image processing, a Raspberry Pi, and the Internet of Things. The system uses image processing techniques like segmentation, feature extraction and classification on images of plant leaves to detect diseases. Sensors connected to an Arduino board such as a humidity sensor, gas sensors and a light sensor are used to monitor environmental conditions. The Arduino and Raspberry Pi are connected to allow the sensors data to be sent to the Raspberry Pi. The Raspberry Pi then sends notifications about the plant health and environmental conditions to smartphones. This allows remote monitoring of farm conditions.
Analysis of Fungus in Plant Using Image Processing Techniquespaperpublications3
Abstract: The present work proposes a methodology for analysis of fungus in plant, using image processing techniques. The fungus kills the young seedling; it spread by air and can also infect plant. Therefore it is very important to monitor the leaf at regular intervals so as to keep track on quality of growth of plant. For analysis of fungus is focused on technique using MATLAB 7.0. The image are captured by digital camera mobile and processed using image growing, then the part of the leaf spot has been used for the classification purpose of the trait and test. The acquired image are in jpeg format and are converted to gray scale image. The gray scale images are enhanced and make noise free. The Ostu algorithm is applied to get threshold image. The pixel neighborhood is applied to enhance the pixel of leaf to show clearly the fungus area. Clustering is applied to get infected part of the leaf. RGB image is then segmented for analysis of fungus in plant. Comparative analyses of Image Edge Detection techniques are presented. It has been observed that the Canny Edge Detection algorithm is computationally more expensive compared to Sobel Edge Detection technique. The fungus infected area is 24.5951%.
Image Mining for Flower Classification by Genetic Association Rule Mining Usi...IJAEMSJORNAL
Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
IRJET- Leaf Disease Detection using Image ProcessingIRJET Journal
This document discusses a system for detecting leaf diseases in plants using image processing techniques. It involves taking images of plant leaves, pre-processing the images by removing noise and enhancing contrast, segmenting the images to identify diseased regions, extracting features like color and texture, and classifying the diseases. The system uses techniques like k-means clustering, color analysis, and neural networks. It aims to help farmers identify diseases early to control them. A proposed smart agriculture system would monitor soil moisture and temperature using sensors and detect diseases using these image processing methods to advise farmers remotely.
IRJET- Recognition of Plants using Leaf Image with Neural Network and Com...IRJET Journal
This document describes a system for recognizing plants using leaf images with neural networks and computer vision. The system first collects leaf images and performs image augmentation to increase the training data size. It then pre-processes the images by resizing them, converting to grayscale, and applying edge detection filters. The features extracted from this are used to train a neural network model to classify leaf images into different plant types. The trained model is then able to predict the plant type of new leaf images based on the features it has learned to identify during training. The system was tested on images of 5 different plant species and was able to accurately classify leaf images into the correct plant types.
Pollination based optimization for color image segmentationIAEME Publication
This document presents a new optimization method called Pollination Based Optimization (PBO) to select optimal color clusters for image segmentation. The methodology involves four steps: color space conversion, generating candidate clusters using Fuzzy K-Means clustering, using PBO to select optimal cluster centers, and segmenting the image. PBO models the natural process of pollination in plants to optimize cluster selection. The method is tested on three images and shows improved segmentation accuracy and reduced computation time compared to other methods.
The Effects of Segmentation Techniques in Digital Image Based Identification ...TELKOMNIKA JOURNAL
This paper presents the effects of segmentation techniques in the identification of Ethiopian
coffee variety. In Ethiopia, coffee varieties are classified based on their growing region. The most widely
coffee growing regions in Ethiopia are Bale, Harar, Jimma, Limu, Sidamo and Welega. Coffee beans of
these regions very in color shape and texture. We investigated various segmentation techniques for
efficient coffee beans variety identification system. Images of six different coffee beans varieties in Oromia
and Southern Ethiopia were acquired and analyzed. For this study Otsu, Fuzzy-C-Means (FCM) and Kmeans
segmentation techniques are considered. For classification of the varieties of Ethiopian coffee
beans back propagation neural network (BPNN) is used. From the experiment 94.54% accuracy is
achieved when BPNN is used on FCM segmentation technique.
Plant Monitoring using Image Processing, Raspberry PI & IOTIRJET Journal
This document describes a plant monitoring system using image processing, a Raspberry Pi, and the Internet of Things. The system uses image processing techniques like segmentation, feature extraction and classification on images of plant leaves to detect diseases. Sensors connected to an Arduino board such as a humidity sensor, gas sensors and a light sensor are used to monitor environmental conditions. The Arduino and Raspberry Pi are connected to allow the sensors data to be sent to the Raspberry Pi. The Raspberry Pi then sends notifications about the plant health and environmental conditions to smartphones. This allows remote monitoring of farm conditions.
Analysis of Fungus in Plant Using Image Processing Techniquespaperpublications3
Abstract: The present work proposes a methodology for analysis of fungus in plant, using image processing techniques. The fungus kills the young seedling; it spread by air and can also infect plant. Therefore it is very important to monitor the leaf at regular intervals so as to keep track on quality of growth of plant. For analysis of fungus is focused on technique using MATLAB 7.0. The image are captured by digital camera mobile and processed using image growing, then the part of the leaf spot has been used for the classification purpose of the trait and test. The acquired image are in jpeg format and are converted to gray scale image. The gray scale images are enhanced and make noise free. The Ostu algorithm is applied to get threshold image. The pixel neighborhood is applied to enhance the pixel of leaf to show clearly the fungus area. Clustering is applied to get infected part of the leaf. RGB image is then segmented for analysis of fungus in plant. Comparative analyses of Image Edge Detection techniques are presented. It has been observed that the Canny Edge Detection algorithm is computationally more expensive compared to Sobel Edge Detection technique. The fungus infected area is 24.5951%.
Image Mining for Flower Classification by Genetic Association Rule Mining Usi...IJAEMSJORNAL
Image mining is concerned with knowledge discovery in image databases. It is the extension of data mining algorithms to image processing domain. Image mining plays a vital role in extracting useful information from images. In computer aided plant identification and classification system the image mining will take a crucial role for the flower classification. The content image based on the low-level features such as color and textures are used to flower image classification. A flower image is segmented using a histogram threshold based method. The data set has different flower species with similar appearance (small inter class variations) across different classes and varying appearance (large intra class variations) within a class. Also the images of flowers are of different pose with cluttered background under varying lighting conditions and climatic conditions. The flower images were collected from World Wide Web in addition to the photographs taken up in a natural scene. The proposed method is based on textural features such as Gray level co-occurrence matrix (GLCM). This paper introduces multi dimensional genetic association rule mining for classification of flowers effectively. The image Data mining approach has four major steps: Preprocessing, Feature Extraction, Preparation of Transactional database and multi dimensional genetic association rule mining and classification. The purpose of our experiments is to explore the feasibility of data mining approach. Results will show that there is promise in image mining based on multi dimensional genetic association rule mining. It is well known that data mining techniques are more suitable to larger databases than the one used for these preliminary tests. Computer-aided method using association rule could assist people and improve the accuracy of flower identification. In particular, a Computer aided method based on association rules becomes more accurate with a larger dataset .Experimental results show that this new method can quickly and effectively mine potential association rules.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
IRJET- Leaf Disease Detection using Image ProcessingIRJET Journal
This document discusses a system for detecting leaf diseases in plants using image processing techniques. It involves taking images of plant leaves, pre-processing the images by removing noise and enhancing contrast, segmenting the images to identify diseased regions, extracting features like color and texture, and classifying the diseases. The system uses techniques like k-means clustering, color analysis, and neural networks. It aims to help farmers identify diseases early to control them. A proposed smart agriculture system would monitor soil moisture and temperature using sensors and detect diseases using these image processing methods to advise farmers remotely.
IRJET- Recognition of Plants using Leaf Image with Neural Network and Com...IRJET Journal
This document describes a system for recognizing plants using leaf images with neural networks and computer vision. The system first collects leaf images and performs image augmentation to increase the training data size. It then pre-processes the images by resizing them, converting to grayscale, and applying edge detection filters. The features extracted from this are used to train a neural network model to classify leaf images into different plant types. The trained model is then able to predict the plant type of new leaf images based on the features it has learned to identify during training. The system was tested on images of 5 different plant species and was able to accurately classify leaf images into the correct plant types.
Pollination based optimization for color image segmentationIAEME Publication
This document presents a new optimization method called Pollination Based Optimization (PBO) to select optimal color clusters for image segmentation. The methodology involves four steps: color space conversion, generating candidate clusters using Fuzzy K-Means clustering, using PBO to select optimal cluster centers, and segmenting the image. PBO models the natural process of pollination in plants to optimize cluster selection. The method is tested on three images and shows improved segmentation accuracy and reduced computation time compared to other methods.
The Effects of Segmentation Techniques in Digital Image Based Identification ...TELKOMNIKA JOURNAL
This paper presents the effects of segmentation techniques in the identification of Ethiopian
coffee variety. In Ethiopia, coffee varieties are classified based on their growing region. The most widely
coffee growing regions in Ethiopia are Bale, Harar, Jimma, Limu, Sidamo and Welega. Coffee beans of
these regions very in color shape and texture. We investigated various segmentation techniques for
efficient coffee beans variety identification system. Images of six different coffee beans varieties in Oromia
and Southern Ethiopia were acquired and analyzed. For this study Otsu, Fuzzy-C-Means (FCM) and Kmeans
segmentation techniques are considered. For classification of the varieties of Ethiopian coffee
beans back propagation neural network (BPNN) is used. From the experiment 94.54% accuracy is
achieved when BPNN is used on FCM segmentation technique.
Foliage Measurement Using Image Processing TechniquesIJTET Journal
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 .
IRJET- Leaf Disease Detecting using CNN TechniqueIRJET Journal
This document describes a proposed system for detecting leaf diseases using convolutional neural network (CNN) techniques. The system uses image acquisition, pre-processing including cropping, resizing and filtering, segmentation using k-means clustering, feature extraction of color, texture and shape features, and classification using CNN. The system is tested on images of mango, pomegranate, guava and sapota leaves to automatically identify diseases and recommend appropriate control methods, providing an improvement over manual identification methods.
This document summarizes a research paper that developed a plant disease identification system using image processing techniques. The system focuses on identifying chlorosis, a disease affecting the medicinal plant Solanum trilobatum caused by lack of chlorophyll. Images of plant leaves are acquired and preprocessed, then the Otsu's method and Graythresh algorithm are used to automatically calculate a threshold value to convert the image to binary and segment diseased areas. The system provides results on whether disease is present or not in the leaf. It is a low-cost and simple method that can potentially help detect disease early and protect medicinal plants.
IRJET- Recent Trends and Insight Towards Automated Identification of Plant Sp...IRJET Journal
This document discusses recent trends in automated plant species identification. It begins by outlining the main steps in automated plant identification systems: image acquisition, pre-processing, feature extraction, classification, and identification. Artificial neural networks are commonly used for classification and can learn from examples to generalize to new cases. Convolutional neural networks, a type of deep learning, have also shown promise by automatically learning discriminative features from images. Leaves are most commonly used for identification due to their abundance and planar structure, but combining features from multiple plant organs may improve accuracy. Shape, veins, color, and texture are important leaf features while flower shape and color are also potentially discriminative.
The quality identification of fruits in image processing using matlabeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
IRJET- Sugarcane Disease Detection and Controlling using Image ProcessingIRJET Journal
This document discusses using image processing to detect and control sugarcane diseases. It begins with an introduction to the importance of agriculture and identifying plant diseases. The authors then describe their methodology which involves image acquisition, pre-processing, segmentation, feature extraction using color analysis and k-means clustering, and classification. Their results show detecting diseases by pixel range analysis is more efficient than color feature extraction. The system could help farmers identify diseases early and apply the proper pesticides to increase sugarcane production. Future work includes developing a user-friendly app to provide real-time disease detection and solutions for farmers.
Abstract In this paper illustrates the improvement of a low cost machine vision system using webcams and image processing algorithms for defect detection and sorting of tomatoes The sorting decision was based on three features extracted by the different image processing algorithms. This methodology based on the color features, which used for detecting the BER from good tomatoes. Two methods were developed for decision based sorting. The color image threshold method with shape factor was found efficient for differentiating good and defective tomatoes. The overall accuracy of defect detection attained was 94 and 96.5% respectively. Comparison of the results is also presented in this paper. Keywords: Dither Image, Stem Image, Histogram, Tomato.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IRJET- Analysis of Plant Diseases using Image Processing MethodIRJET Journal
This document describes a method for detecting plant diseases using image processing techniques. The method involves capturing images of plant leaves using a digital camera, preprocessing the images by converting them to grayscale and removing noise. Edge detection algorithms like Canny and Sobel are then applied to detect edges. K-means clustering is used for image segmentation to segment unhealthy parts of leaves. The process results in an effective solution for segmenting diseased areas of leaves.
This document presents a novel approach for detecting and classifying plant leaf diseases using image processing techniques. The proposed framework includes image preprocessing, segmentation of diseased areas using k-means clustering, feature extraction of texture features using GLCM, and classification of diseases using support vector machine (SVM) and decision tree classifiers. The framework is tested on citrus leaf images to detect diseases. Future work could improve classification accuracy and apply the method to other plant species.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper that classified multi-date remote sensing images using NDVI values. It discusses how NDVI values were calculated from Terra satellite imagery using red and infrared band values. A similarity measure formula was proposed to classify images based on comparing NDVI values of unknown images to reference images. The formula measured similarity between image windows using sum of absolute differences of NDVI values. Five Terra images from different dates were classified into 20 reference classes using this approach.
This document summarizes the applications of seed image analysis in seed science research. It discusses how image analysis can be used for varietal identification, characterization, and germination testing. Seed image analysis involves acquiring digital images of seeds and using computer programs to extract quantitative data on seed characteristics like size, shape, color and texture. This data can then be used to automatically classify and identify seed varieties. The document outlines several studies that achieved 98% or higher accuracy in classifying different wheat and bean varieties using seed image analysis. It also discusses how the technique can be applied to testing seed germination and distinguishing new varieties for plant breeding programs.
IRJET- An Expert System for Plant Disease Diagnosis by using Neural NetworkIRJET Journal
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 NetworkIRJET Journal
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.
Automatic Recognition System Using Preferential Image Segmentation For Leaf A...CSEIJJournal
This document summarizes an article about developing an automatic plant recognition system using preferential image segmentation of leaf and flower images. It begins by introducing the need for automatic plant classification systems. It then describes the general process, which includes capturing leaf images, pre-processing, feature extraction, image segmentation using preferential image segmentation, and a matching process. Preferential image segmentation is used to segment objects of interest, like leaves, from an image while ignoring background portions. The document provides details on each step and evaluates preferential image segmentation for segmenting leaves from images to aid in plant identification.
This document proposes a method for stem removal of citrus fruit images using morphological image processing and thresholding. The method involves preprocessing images by resizing, converting to HSV color space, and removing noise using Gaussian filtering. Stem removal is then performed using morphological opening, distance transforms, top-hat filtering, and thresholding the grayscale values to isolate the stem pixels. The proposed stem removal process aims to accurately extract citrus fruit from images for classification.
Image Segmentation Based Survey on the Lung Cancer MRI ImagesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
Preprocessing Techniques for Image Mining on Biopsy ImagesIJERA Editor
This document discusses preprocessing techniques for image mining on biopsy images. It begins with an introduction to biomedical imaging and image mining. The key steps in image mining are described as image retrieval, preprocessing, feature extraction, data mining, and interpretation. Various preprocessing techniques are then evaluated on biopsy images, including interpolation, thresholding, and segmentation. Bicubic interpolation and Otsu thresholding produced good results for enhancing renal biopsy images. Overall, the document evaluates different preprocessing methods and their effects on biopsy images to help extract meaningful features for disease detection through image mining.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
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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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This document describes a proposed system for detecting leaf diseases using convolutional neural network (CNN) techniques. The system uses image acquisition, pre-processing including cropping, resizing and filtering, segmentation using k-means clustering, feature extraction of color, texture and shape features, and classification using CNN. The system is tested on images of mango, pomegranate, guava and sapota leaves to automatically identify diseases and recommend appropriate control methods, providing an improvement over manual identification methods.
This document summarizes a research paper that developed a plant disease identification system using image processing techniques. The system focuses on identifying chlorosis, a disease affecting the medicinal plant Solanum trilobatum caused by lack of chlorophyll. Images of plant leaves are acquired and preprocessed, then the Otsu's method and Graythresh algorithm are used to automatically calculate a threshold value to convert the image to binary and segment diseased areas. The system provides results on whether disease is present or not in the leaf. It is a low-cost and simple method that can potentially help detect disease early and protect medicinal plants.
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This document discusses recent trends in automated plant species identification. It begins by outlining the main steps in automated plant identification systems: image acquisition, pre-processing, feature extraction, classification, and identification. Artificial neural networks are commonly used for classification and can learn from examples to generalize to new cases. Convolutional neural networks, a type of deep learning, have also shown promise by automatically learning discriminative features from images. Leaves are most commonly used for identification due to their abundance and planar structure, but combining features from multiple plant organs may improve accuracy. Shape, veins, color, and texture are important leaf features while flower shape and color are also potentially discriminative.
The quality identification of fruits in image processing using matlabeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
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IRJET- Sugarcane Disease Detection and Controlling using Image ProcessingIRJET Journal
This document discusses using image processing to detect and control sugarcane diseases. It begins with an introduction to the importance of agriculture and identifying plant diseases. The authors then describe their methodology which involves image acquisition, pre-processing, segmentation, feature extraction using color analysis and k-means clustering, and classification. Their results show detecting diseases by pixel range analysis is more efficient than color feature extraction. The system could help farmers identify diseases early and apply the proper pesticides to increase sugarcane production. Future work includes developing a user-friendly app to provide real-time disease detection and solutions for farmers.
Abstract In this paper illustrates the improvement of a low cost machine vision system using webcams and image processing algorithms for defect detection and sorting of tomatoes The sorting decision was based on three features extracted by the different image processing algorithms. This methodology based on the color features, which used for detecting the BER from good tomatoes. Two methods were developed for decision based sorting. The color image threshold method with shape factor was found efficient for differentiating good and defective tomatoes. The overall accuracy of defect detection attained was 94 and 96.5% respectively. Comparison of the results is also presented in this paper. Keywords: Dither Image, Stem Image, Histogram, Tomato.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES USING IMAGE PROCESSING
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.4, August 2019
DOI : 10.5121/ijcseit.2019.9401 1
AN ANALYSIS OF SURFACE AND GROWTH
DIFFERENCES IN PLANTS OF DIFFERENT STAGES
USING IMAGE PROCESSING
Faizur Rashid and Tamirat Delesa
Department of Computer Science, College of Computing and Informatics
Haramaya University, Ethiopia
ABSTRACT
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
KEYWORDS
Image Processing, Surface of Plants (SOP), Differences of Stage (DoS), Segmentation.
1. INTRODUCTION
Image processing is a method to do some processing of observation in digital images [10]. The
processing can be applied by using different techniques of emulators in images and required
output can be gained with its characteristics. The image can be calculated by applying its function
f(x,y), where x and y are two coordinates. These coordinates are called the intensity or grayscale
of the image at that point [10] [16].
These coordinates indicate that each point is having some value of image and it can be identified
through its location for any means [1][4].
The existing method plant disease and growth is made by open eye observation during the time. It
is a time process with a large number of team members and finding the solution. It is tried here to
detect the growth on its different stages using automatic system of image processing using plant
leaves and roots. The automatic detection analysis method will help the agricultural science to
find ironic solution.
Either in the case continuous monitoring is required that has been made in beans plant on the area
of 3x3 meters soil structure to observe their root growth and development in the land of Harar
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.4, August 2019
2
Region in Ethiopia. Its growth identification is more easy using automatic system in image
processing.
1.1. Edge of Image
Edge detection is a mathematical rule and described fundamentals that help to identify the stages
of plants of their age [4][12]. Complete surface can be observed and determined by the processes
and comparison is easy to determine from the different stages of plants in image processing [6].
Edge detection and segmentation is very important area in the field of image processing that helps
to precede different levels on any images.
An edge is defined as the boundary between regions in an image, which helps with segmentation
and object recognition. Any shadow or change on the originality of the image can be easily
identified by the segmentation process with distinct change in the intensity of an image [2][3].
The growth of any plant is dependable on various resources like quality of soil, period of rain or
watering on the plant, intensity of sun, weather quality whether moisture or dry, and any deceased
or non-deceased.
The quality of edge detection is highly dependent on the light, intensity on object, edge density,
and noise on the plant. Many corrections are possible by adjustment of some certain value on the
images with edge detection and threshold value mechanism [3] [8]. Some of the applications like
Photoshop and Corel Draw help the users to adjust the image by using threshold techniques but it
is hard to identify the originality of the image in auto-adjustment mechanism. The evaluation of
categorical data was made using different techniques like Canny edge detection, skin
segmentation [10], and morphological operation to detect the growth differences of Bean plant.
2. METHODOLOGY
The methodology being proposed by the author was staged in given below diagram that has been
partly inherited [1], and shown here in Figure 2.1.
Figure 2.1 Steps of Plant Identification
The steps involved in identification of surface of plant growth in their quality and differences of
Stages (DoS) in the growth. The frame in Figure 2.1 shows the plant acquisition, filtering and
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.4, August 2019
3
segmentation process, color of plant leaf surface analysis, and Canny edge detection. Finally,
plant quality and growth were identified.
2.1. Image Acquisition
This was done with the help of Canon digital camera that loaded and saved through built in
software. The images were generally saved in .jpg format that can be transmitted into different
format like .tif, .img, .bmp, or binary manner [5].
Basically, the images were captured by the camera in RGB format by default, which was taken by
the author for its further assessment.
The images that have been obtained in normal form of RGB may not be directly used for
identification and classification due to some certain factors like noise, resolution quality artifacts,
poor light, or very contrast light.
2.2. Sample Plant Images
Almost 45 images have been tested of different plants which are not possible to display in this
small size of scripted form. The authors tried to display some of the images in integrated object.
There are way of integration in MATLAB by using imgadd() command to combine the images
into one form for object visibility in compared manner.
Figure 2.2 Combined RGB Images
The RGB image means a true color image that carry Red, Grey, and Blue color combination into
matrix form. Similar size of the matrix is required to asses and each represents the image color in
terms of pixels.
2.2.1. Field data
Images were collected in a period of 2 month from September’2018 to October’2018. Plants were
selected randomly to snap and sampled for process. Morphological status was recorded on almost
7 times for different stage of the plants. The growth estimation were observed by taking a
standard variable for different stage (S) and for plant (P), categorized in 3 levels S1, S2, S3 and
P1, P2, and P3 within 3 months period of time.
4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.4, August 2019
4
The height of stem were measured in S1 (4rd
week), S2 (7th
week), and S3 (12th
week) level for
different plant P1, P2, and P3.The measurement were taken directly through geometrical scale
from the level of soil to the top of the plant. The table below shows the growth level of the plants
in table 1.
Table 1. Growth level estimation of the plants in different stage
Plant Samples Growth level in different Stages in inch(s)
S1 (4rd
week) S2 (7th
week) S3 (12th
week)
P1 2.4 5.3 15.6
P2 2.0 4.9 13.2
P3 1.8 4.2 12.7
2.3. Grey Scale Plant Images
Grey colour images are monochromic color images which contains only one combined color of
black and white. The conversion from RGB to grey scale is required gamma extension [9][10].
Figure 2.3 shows the younger and older images of Plants.
Figure 2.3 Grey Images of Plant of 16 bit
2.4. Binary Image of Plant
As the images are considered using 1 bit, binary image becomes applicable, which is based on 1s
and 0s. Binary images are used to segmentation and pattern recognition where main task is to
binaries’ the image [10]. This Figure 2.4 is the way of changing luminance quantization of an
image using 0=black, 1=dark grey, 2=light grey and 3=white with its value.
Figure 2.4 Binary Image
5. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.4, August 2019
5
2.5. Filtering
The purpose of smoothing is to reduce noise using geometric features and improve the visual
quality of the image. Smoothing operation can be expressed as filtering. Here median filer is
being used. This filters the high intensity sharp components by computing the median of the
neighbourhood [8][13].
2.5.1. Median Filter
The set of number m, is finding the medium of two sets of numbers less and greater than m
consecutively 7][11][15]. It uses the neighbourhood value to calculate in a series of pixel and
replaces the value of the pixel in matrix of the value of image [10][11][15]. The expression of
Median Filter Is
Si j = 1/M ∑ 𝑓𝑖𝑗
(𝑛,𝑚)𝑠
Where S is containing n x m pixel whose centre is located in the range of (i, j) and M is the total
number of pixels in the image that calculate the median of the image.
The median filter is important to remove isolated random noise without blurring. Median filter
takes out little better result than other filtering process [10]. Below in figure 2.5 shows the median
filter result.
Figure 2.5 Filtered Images
3. SEGMENTATION
Segmentation allows set of raw components to be generated that is region of pixels with similar
intensity of values or set of lines obtained by isolating the edges of an image and computed by
locating regions where there is significance difference in the intensity. Different regions of image
are divided with this process [14][15]. It is used to analyse the separate feature of pre-determined
type of background. This is broadly known for two properties in an image, (i) Similarity (ii)
discontinuity.
Firstly, the image is having gray level of predetermined range in different regions in image.
Secondly, the sudden change of the value of gray level image is observed in another segmented
region, which is also known as edge detection. Edge detection is obvious to observe the edges of
image to analyse the growth of the plant. This is being applied by canny edge detector mechanism
in this work [10].
6. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.4, August 2019
6
4. CANNY EDGE DETECTOR METHOD
This is also known as Optimal Edge Detector Algorithm. Canny vision was to enhance the images
into many different forms with following properties [10]
a) Reduce the amount of noise speckles within the image and remove the very high
frequency components.
b) Creating two images, one containing the gradient magnitude G, and another containing
the orientation (G). Means, keep lower variation between original and processed image.
c) Certain minimum threshold value above gradient magnitude is detected to find major
edges.
(a) Canny edge older plant (b) Canny edge Younger plant
Canny edge detector were applied using MATLAB in older and younger plant. Code of
MATLAB is as follows,
e=edge(g,’canny’,[],1);
subplot(2,2,4);
figure, imshow(e);
Here, e will store the two dimensional grey image that stored in g. It will acquire an area that is
need to display the edged image and lastly displaying the image in cascaded manner.
5. CONCLUSION
Basically leaves of plants were analysed and entire expedients done in MATLAB version 14a.
Additionally, some of the images were cross tested in Corel Photo Paint to observe the display
quality of image output. In cross observation, concluded that MATLAB is better option to the
image output in conversion, effects or transmission. Firstly, it was observed that binary value
differences were found in the binary converted image in the matrix positions. Secondly, the points
of higher number of the detected edges approves, the higher the age and grown plant. Background
color detection was tried to observe the affected plant leaf, and observed that changed color from
the originated leaves are infected by some diseases and causes the competitive growth among the
others. This disease could be identified in other step of work with the help of specialist.
All the script of MATAB has not been written in this paper, only some of the samples have been
mentioned.
7. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.9, No.4, August 2019
7
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