This document describes a study that developed and tested two algorithms for classifying weeds and crops in digital images. The first algorithm used intensity thresholds to classify plants, but required manual input. The second algorithm used area thresholding to automatically segment and classify plants based on size. Forty-one sample images were tested, finding an average of 13% weed coverage. The area thresholding approach provided automated classification but some crop plants were misclassified as weeds if their size was below the threshold. The overall goal was to develop an automated system for identifying weeds to help optimize herbicide application in agriculture.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
This document describes a restaurant billing system project created by students at Geetanjali College of Engineering and Technology. The system was created to automate billing processes and gather customer and order data. It allows customers to view menus, place orders, and pay bills. The system includes modules for registration, login, order selection and placement, billing, and payment. It also covers hardware and software requirements to run the POS system like receipt printers, barcode scanners, and payment devices. Flowcharts provide overviews of the registration, login, ordering, and payment processes. The project aims to increase efficiency, speed, and accuracy for restaurant transactions.
THIS PPT HELP STUDENT FOR THE JAVA BASED MINIPROJECT AND ALSO HELP TO PEOPLE WHO HAD A STORE OF GROCERY AND HELP TO MANAGED THEIR STORE THROUGH REFFER OF PPT
This document summarizes a project titled "Placement Management System" submitted by Mehul Ranavasiya and Devashish Vaghela towards fulfilling requirements for a Bachelor of Technology degree. The project was developed under the guidance of Dr. Madhuri Bhavsar and aims to develop a web-based system for managing student and company information related to training and placement activities. The document includes sections on introduction, system analysis, design, testing, future enhancements, and bibliography.
This document outlines the steps in a fitness app workflow:
1) Users register an account and their information is verified by an admin. 2) Daily, users plan their diet and exercise routine. 3) Their calorie intake and workout progress is tracked. 4) Payment information is collected and transactions are recorded. 5) Users can view a report on their progress and provide feedback to help improve the app.
Multi-label Classification with Meta-labelsAlbert Bifet
The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach suffers from class imbalance and a restricted hypothesis space that negatively affects its predictive performance, and can easily be outperformed by methods which learn labels together. A number of methods have grown around the label powerset approach, which models label combinations together as class values in a multi-class problem. We describe the label-powerset-based solutions under a general framework of \emph{meta-labels}. We provide theoretical justification for this framework which has been lacking, by viewing meta-labels as a hidden layer in an artificial neural network. We explain how meta-labels essentially allow a random projection into a space where non-linearities can easily be tackled with established linear learning algorithms. The proposed framework enables comparison and combination of related approaches to different multi-label problems. Indeed, we present a novel model in the framework and evaluate it empirically against several high-performing methods, with respect to predictive performance and scalability, on a number of datasets and evaluation metrics. Our deployment of an ensemble of meta-label classifiers obtains competitive accuracy for a fraction of the computation required by the current meta-label methods for multi-label classification.
The document describes the design and implementation of an online examination system using PHP and MySQL. It includes sections on requirements analysis, database design, implementation, and problems encountered. The system has three modules - one for administrators, one for students, and one for a super administrator. Entity relationship diagrams and data flow diagrams are provided to illustrate the database and system design. The goal is to allow students to take exams online and obtain results immediately in a more efficient manner than traditional paper-based exams.
Grocery Store Application in Python.pptxRaju406964
This document summarizes a grocery store management application project created by students. The project allows users to view product details, place orders, and view customer details. It was created using Python, HTML, CSS, JavaScript, and SQL for the backend and frontend. The project aims to make purchasing and managing grocery store inventory and customers more efficient. Key features include adding and viewing products and customers, and processing orders. Flowcharts and screenshots of the application interface are provided.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
This document describes a restaurant billing system project created by students at Geetanjali College of Engineering and Technology. The system was created to automate billing processes and gather customer and order data. It allows customers to view menus, place orders, and pay bills. The system includes modules for registration, login, order selection and placement, billing, and payment. It also covers hardware and software requirements to run the POS system like receipt printers, barcode scanners, and payment devices. Flowcharts provide overviews of the registration, login, ordering, and payment processes. The project aims to increase efficiency, speed, and accuracy for restaurant transactions.
THIS PPT HELP STUDENT FOR THE JAVA BASED MINIPROJECT AND ALSO HELP TO PEOPLE WHO HAD A STORE OF GROCERY AND HELP TO MANAGED THEIR STORE THROUGH REFFER OF PPT
This document summarizes a project titled "Placement Management System" submitted by Mehul Ranavasiya and Devashish Vaghela towards fulfilling requirements for a Bachelor of Technology degree. The project was developed under the guidance of Dr. Madhuri Bhavsar and aims to develop a web-based system for managing student and company information related to training and placement activities. The document includes sections on introduction, system analysis, design, testing, future enhancements, and bibliography.
This document outlines the steps in a fitness app workflow:
1) Users register an account and their information is verified by an admin. 2) Daily, users plan their diet and exercise routine. 3) Their calorie intake and workout progress is tracked. 4) Payment information is collected and transactions are recorded. 5) Users can view a report on their progress and provide feedback to help improve the app.
Multi-label Classification with Meta-labelsAlbert Bifet
The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach suffers from class imbalance and a restricted hypothesis space that negatively affects its predictive performance, and can easily be outperformed by methods which learn labels together. A number of methods have grown around the label powerset approach, which models label combinations together as class values in a multi-class problem. We describe the label-powerset-based solutions under a general framework of \emph{meta-labels}. We provide theoretical justification for this framework which has been lacking, by viewing meta-labels as a hidden layer in an artificial neural network. We explain how meta-labels essentially allow a random projection into a space where non-linearities can easily be tackled with established linear learning algorithms. The proposed framework enables comparison and combination of related approaches to different multi-label problems. Indeed, we present a novel model in the framework and evaluate it empirically against several high-performing methods, with respect to predictive performance and scalability, on a number of datasets and evaluation metrics. Our deployment of an ensemble of meta-label classifiers obtains competitive accuracy for a fraction of the computation required by the current meta-label methods for multi-label classification.
The document describes the design and implementation of an online examination system using PHP and MySQL. It includes sections on requirements analysis, database design, implementation, and problems encountered. The system has three modules - one for administrators, one for students, and one for a super administrator. Entity relationship diagrams and data flow diagrams are provided to illustrate the database and system design. The goal is to allow students to take exams online and obtain results immediately in a more efficient manner than traditional paper-based exams.
Grocery Store Application in Python.pptxRaju406964
This document summarizes a grocery store management application project created by students. The project allows users to view product details, place orders, and view customer details. It was created using Python, HTML, CSS, JavaScript, and SQL for the backend and frontend. The project aims to make purchasing and managing grocery store inventory and customers more efficient. Key features include adding and viewing products and customers, and processing orders. Flowcharts and screenshots of the application interface are provided.
This painting was originally from the tomb of Nebamun, a wealthy scribe from Thebes who collected grain. The painting depicts Nebamun hunting in the marshes with his wife Hatshepsut, daughter, and cat. Ancient Egyptians believed paintings in tombs would allow people to continue activities after death. The painting was meant to honor Nebamun and allow him to be remembered.
This document discusses uninformed search techniques for problem solving. It describes generate and test, breadth-first search (BFS), depth-first search (DFS), and depth-limited iterative deepening search (DFID). BFS explores all nodes at each depth level before moving to the next level, while DFS explores nodes as deeply as possible before backtracking. DFID combines the completeness of BFS and memory efficiency of DFS by performing iterative deepening DFS searches.
This document is a project report submitted by Pragnya Dash to fulfill the requirements for a Bachelor of Technology degree in Information Technology from the International Institute of Information Technology in Bhubaneswar, India. The report details the development of an online shopping system under the guidance of Prof. Sabyasachi Patra. It includes chapters on project analysis, feasibility study, software requirements specification, selected software, design considerations, testing, implementation and future improvements. The selected software for developing the system includes Microsoft Visual Studio, .NET Framework, C# and ASP.NET.
This document discusses informed search algorithms for artificial intelligence. It covers iterative deepening A* (IDA*), recursive best-first search (RBFS), and simplified memory bounded A* (SMA*). IDA* improves on A* by using iterative deepening with a cutoff value based on path cost rather than depth. RBFS replaces node path costs with the best child cost on backtracking. SMA* works like A* until memory is full, then drops the highest-cost node to expand new nodes without recomputing explored areas.
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.
DCOM and CORBA are distributed object computing architectures that allow objects to communicate remotely. Both use a three-layer architecture - a top layer for programming interfaces, a middle remoting layer, and a bottom wire protocol layer. Key differences are that DCOM supports multiple interfaces per object while CORBA inherits all interfaces from a common base class, and DCOM's wire protocol is tied to RPC while CORBA's is not. Overall they provide similar distributed object capabilities with some differences in implementation details.
This document contains answers to multiple questions about image processing concepts. For question 22a, the kernel formed by the outer product of vectors v and wT is determined to be separable. For question 22b, it is explained that a separable kernel w can be decomposed into two simpler kernels w1 and w2 such that w = w1 * w2. This allows the convolution to be computed more efficiently in two steps by first convolving w1 with the image and then convolving the result with w2, requiring fewer operations than a direct convolution with w.
This document discusses various point processing and gray level transformation techniques used in image enhancement. It describes point processing as operating directly on pixel intensity values individually to alter them using transformation functions. The document outlines several basic gray level transformations including linear, logarithmic and power law. It also discusses piecewise linear transformations such as contrast stretching, intensity level slicing, and bit plane slicing. These transformations are used to enhance images by modifying their brightness, contrast and emphasis on certain gray levels.
The document discusses artificial neural networks and classification using backpropagation, describing neural networks as sets of connected input and output units where each connection has an associated weight. It explains backpropagation as a neural network learning algorithm that trains networks by adjusting weights to correctly predict the class label of input data, and how multi-layer feed-forward neural networks can be used for classification by propagating inputs through hidden layers to generate outputs.
Final Year Project Report on Self Tacit Zone (Location Based Android App)Parthik Poshiya
This document is a project report for an Android application called Self Tacit Zone. It was created by Parthik Poshiya and Keyur Hudka to fulfill the requirements for a Bachelor of Engineering degree. The report includes an introduction that outlines the problem summary, aim and objectives, problem specifications, literature review, tools and technology used, and a prior art search. It also covers the design, analysis, implementation, and testing of the Self Tacit Zone application.
This document proposes a mechanism to detect credit card fraud in online transactions using a Hidden Markov Model. The model would classify users as having low, medium, or high spending habits and flag transactions as potentially fraudulent if a user makes a payment outside their normal spending category. The mechanism was implemented using HTML, CSS, JavaScript, PHP, and MySQL and could help reduce fraud by adding an additional layer of security validation for online payments. However, it may not detect all fraudulent transactions accurately as the Hidden Markov Model requires at least 10 prior transactions to properly classify users.
The document discusses digital image processing and provides an overview of key concepts. It defines digital and analog images and explains how digital images are represented by pixels. It outlines fundamental steps in digital image processing like image acquisition, enhancement, restoration, morphological processing, segmentation, representation, compression and object recognition. It also discusses applications in areas like remote sensing, medical imaging, film and video effects.
This document provides an overview of an online food delivery system project. It describes using the Rational Unified Process (RUP) model to implement the system in an iterative and incremental way. Key elements include functional requirements like online ordering and payment, non-functional requirements like security, and UML diagrams to model the system. Testing strategies include unit, integration, system, and acceptance testing.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
This document describes a project on plant disease detection and classification using deep learning. The objectives are to automatically detect plant diseases as early as symptoms appear on leaves in order to increase crop productivity. Deep learning techniques like convolutional neural networks (CNNs) are implemented using libraries like TensorFlow and Keras. Two CNN models, VGG16 and VGG19, are compared for classifying diseases in a dataset of 38 classes and 87k images of 14 crop species. The system achieved over 95% accuracy on validation. Future work involves developing a mobile app and integrating disease recommendations to help farmers.
Case-based reasoning is a problem-solving process that uses specific examples of previously experienced problems, called cases, to solve new problems. There are four main processes in case-based reasoning: retrieve, reuse, revise, and retain. Retrieve involves finding past cases similar to the new problem. Reuse means applying solutions from similar past cases to the new problem. Revise re-tests solutions to see if they work or need adjusting for the new problem. Retain stores the new experience so future problems can retrieve and reuse it. The document provides an example of using case-based reasoning to solve a new problem with Android TV software where video will not play. It describes applying each step of the process to find and apply a past
“Bus Tracking Application” is an application for Smart phones that works on Android Operating system. This application uses the GPS function. This application at a specific pickup point will send the current location of the bus to students when they request. This app generate predictions of bus arrivals at stops along the route. This application uses a variety of technologies to track the locations of buses in real time
This document describes an online exam project created using J2EE. It was submitted as a thesis project to fulfill requirements for an industrial training program. The project aims to automate exam assessment and provide instant results and reports to reduce workload. It allows multiple choice questions and sending score notifications via email. Future enhancements could include additional question types and improved reusability, extensibility, and portability.
Weed and crop segmentation and classification using area thresholdingeSAT Journals
Abstract In the agricultural industry, the weed and crop identification and classification are major technical and economical importance. Two classification algorithms are focused in this paper. And the better classification algorithm has been selected to classify weed and crop from the images. There are three main parts of proposed system are segmentation, classification and error calculation. The developed algorithm based on area thresholding has been tested on weeds and various locations. Forty one sample images have been tested and the result of some weed coverage rate is illustrated. Moreover, the misclassification rate is also computed. An algorithm has been done to automate the tasks of segmentation and classification. The overall process is implemented in MATLAB. Keywords - Objects segmentation, Image processing, Plant classification, Area Thresholding
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
This painting was originally from the tomb of Nebamun, a wealthy scribe from Thebes who collected grain. The painting depicts Nebamun hunting in the marshes with his wife Hatshepsut, daughter, and cat. Ancient Egyptians believed paintings in tombs would allow people to continue activities after death. The painting was meant to honor Nebamun and allow him to be remembered.
This document discusses uninformed search techniques for problem solving. It describes generate and test, breadth-first search (BFS), depth-first search (DFS), and depth-limited iterative deepening search (DFID). BFS explores all nodes at each depth level before moving to the next level, while DFS explores nodes as deeply as possible before backtracking. DFID combines the completeness of BFS and memory efficiency of DFS by performing iterative deepening DFS searches.
This document is a project report submitted by Pragnya Dash to fulfill the requirements for a Bachelor of Technology degree in Information Technology from the International Institute of Information Technology in Bhubaneswar, India. The report details the development of an online shopping system under the guidance of Prof. Sabyasachi Patra. It includes chapters on project analysis, feasibility study, software requirements specification, selected software, design considerations, testing, implementation and future improvements. The selected software for developing the system includes Microsoft Visual Studio, .NET Framework, C# and ASP.NET.
This document discusses informed search algorithms for artificial intelligence. It covers iterative deepening A* (IDA*), recursive best-first search (RBFS), and simplified memory bounded A* (SMA*). IDA* improves on A* by using iterative deepening with a cutoff value based on path cost rather than depth. RBFS replaces node path costs with the best child cost on backtracking. SMA* works like A* until memory is full, then drops the highest-cost node to expand new nodes without recomputing explored areas.
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.
DCOM and CORBA are distributed object computing architectures that allow objects to communicate remotely. Both use a three-layer architecture - a top layer for programming interfaces, a middle remoting layer, and a bottom wire protocol layer. Key differences are that DCOM supports multiple interfaces per object while CORBA inherits all interfaces from a common base class, and DCOM's wire protocol is tied to RPC while CORBA's is not. Overall they provide similar distributed object capabilities with some differences in implementation details.
This document contains answers to multiple questions about image processing concepts. For question 22a, the kernel formed by the outer product of vectors v and wT is determined to be separable. For question 22b, it is explained that a separable kernel w can be decomposed into two simpler kernels w1 and w2 such that w = w1 * w2. This allows the convolution to be computed more efficiently in two steps by first convolving w1 with the image and then convolving the result with w2, requiring fewer operations than a direct convolution with w.
This document discusses various point processing and gray level transformation techniques used in image enhancement. It describes point processing as operating directly on pixel intensity values individually to alter them using transformation functions. The document outlines several basic gray level transformations including linear, logarithmic and power law. It also discusses piecewise linear transformations such as contrast stretching, intensity level slicing, and bit plane slicing. These transformations are used to enhance images by modifying their brightness, contrast and emphasis on certain gray levels.
The document discusses artificial neural networks and classification using backpropagation, describing neural networks as sets of connected input and output units where each connection has an associated weight. It explains backpropagation as a neural network learning algorithm that trains networks by adjusting weights to correctly predict the class label of input data, and how multi-layer feed-forward neural networks can be used for classification by propagating inputs through hidden layers to generate outputs.
Final Year Project Report on Self Tacit Zone (Location Based Android App)Parthik Poshiya
This document is a project report for an Android application called Self Tacit Zone. It was created by Parthik Poshiya and Keyur Hudka to fulfill the requirements for a Bachelor of Engineering degree. The report includes an introduction that outlines the problem summary, aim and objectives, problem specifications, literature review, tools and technology used, and a prior art search. It also covers the design, analysis, implementation, and testing of the Self Tacit Zone application.
This document proposes a mechanism to detect credit card fraud in online transactions using a Hidden Markov Model. The model would classify users as having low, medium, or high spending habits and flag transactions as potentially fraudulent if a user makes a payment outside their normal spending category. The mechanism was implemented using HTML, CSS, JavaScript, PHP, and MySQL and could help reduce fraud by adding an additional layer of security validation for online payments. However, it may not detect all fraudulent transactions accurately as the Hidden Markov Model requires at least 10 prior transactions to properly classify users.
The document discusses digital image processing and provides an overview of key concepts. It defines digital and analog images and explains how digital images are represented by pixels. It outlines fundamental steps in digital image processing like image acquisition, enhancement, restoration, morphological processing, segmentation, representation, compression and object recognition. It also discusses applications in areas like remote sensing, medical imaging, film and video effects.
This document provides an overview of an online food delivery system project. It describes using the Rational Unified Process (RUP) model to implement the system in an iterative and incremental way. Key elements include functional requirements like online ordering and payment, non-functional requirements like security, and UML diagrams to model the system. Testing strategies include unit, integration, system, and acceptance testing.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
This document describes a project on plant disease detection and classification using deep learning. The objectives are to automatically detect plant diseases as early as symptoms appear on leaves in order to increase crop productivity. Deep learning techniques like convolutional neural networks (CNNs) are implemented using libraries like TensorFlow and Keras. Two CNN models, VGG16 and VGG19, are compared for classifying diseases in a dataset of 38 classes and 87k images of 14 crop species. The system achieved over 95% accuracy on validation. Future work involves developing a mobile app and integrating disease recommendations to help farmers.
Case-based reasoning is a problem-solving process that uses specific examples of previously experienced problems, called cases, to solve new problems. There are four main processes in case-based reasoning: retrieve, reuse, revise, and retain. Retrieve involves finding past cases similar to the new problem. Reuse means applying solutions from similar past cases to the new problem. Revise re-tests solutions to see if they work or need adjusting for the new problem. Retain stores the new experience so future problems can retrieve and reuse it. The document provides an example of using case-based reasoning to solve a new problem with Android TV software where video will not play. It describes applying each step of the process to find and apply a past
“Bus Tracking Application” is an application for Smart phones that works on Android Operating system. This application uses the GPS function. This application at a specific pickup point will send the current location of the bus to students when they request. This app generate predictions of bus arrivals at stops along the route. This application uses a variety of technologies to track the locations of buses in real time
This document describes an online exam project created using J2EE. It was submitted as a thesis project to fulfill requirements for an industrial training program. The project aims to automate exam assessment and provide instant results and reports to reduce workload. It allows multiple choice questions and sending score notifications via email. Future enhancements could include additional question types and improved reusability, extensibility, and portability.
Weed and crop segmentation and classification using area thresholdingeSAT Journals
Abstract In the agricultural industry, the weed and crop identification and classification are major technical and economical importance. Two classification algorithms are focused in this paper. And the better classification algorithm has been selected to classify weed and crop from the images. There are three main parts of proposed system are segmentation, classification and error calculation. The developed algorithm based on area thresholding has been tested on weeds and various locations. Forty one sample images have been tested and the result of some weed coverage rate is illustrated. Moreover, the misclassification rate is also computed. An algorithm has been done to automate the tasks of segmentation and classification. The overall process is implemented in MATLAB. Keywords - Objects segmentation, Image processing, Plant classification, Area Thresholding
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
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
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
This document describes a new methodology called Extreme Software Estimation (XSoft Estimation) for accurately estimating software projects. XSoft Estimation uses COSMIC-Full Function Points (FFP) to measure software size and then applies a model of Development Effort = Size * Variable to estimate effort, cost, and schedule. The methodology was tested on 5 projects measuring their size in CFP units and comparing actual development time between expert and skilled teams, different programming languages and layers. The results showed expert teams and some languages/layers took significantly less time than others for the same sized functionality. XSoft Estimation aims to improve on past methods by basing estimates directly on measured functionality using COSMIC FFP.
A comprehensive review on performance of aodv and dsdv protocol using manhatt...eSAT 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
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.
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 novel method for detecting and characterizing low velocity impact (lvi) in ...eSAT 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.
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.
Efficient document compression using intra frame prediction tecthniqueeSAT 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.
Enhanced equally distributed load balancing algorithm for cloud computingeSAT 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 remote monitoring system for a three phase 10-kva switchable distribution t...eSAT 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
Effect of modulus of masonry on initial lateral stiffness of infilled frames ...eSAT 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.
Compressive strength variability of brown coal fly ash geopolymer concreteeSAT Publishing House
The document summarizes research investigating the compressive strength variability of geopolymer concrete made with brown coal fly ash as a binder. Testing of six mixes of geopolymer concrete found a large range in 28-day compressive strengths, from 43.81 MPa to 7.21 MPa. Additional chemical analysis found significant variability in the chemical composition of samples from the same brown coal fly ash source, particularly in the silicon dioxide and aluminum oxide contents. This variability is believed to contribute to the variability in compressive strengths and suggests the need for pretreatment and refinement of brown coal fly ash to produce more consistent geopolymer concrete.
Behaviour of bituminous concrete modified with polyethylene glycol for blade ...eSAT 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
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
Software testing effort estimation with cobb douglas function a practical app...eSAT 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.
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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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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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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IRJET- AI Based Fault Detection on Leaf and Disease Prediction using K-means ...IRJET Journal
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.
Plant Monitoring using Image Processing, Raspberry PI & IOTIRJET Journal
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Weed and crop segmentation and classification using area thresholding
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 375
WEED AND CROP SEGMENTATION AND CLASSIFICATION USING
AREA THRESHOLDING
Su Hnin Hlaing1
, Aung Soe Khaing2
1
Demonstrator, Department of Electronic Engineering, Mandalay Technological University, Myanmar
2
Associate Professor, Department of Electronic Engineering, Mandalay Technological University, Myanmar
Abstract
In the agricultural industry, the weed and crop identification and classification are major technical and economical
importance. Two classification algorithms are focused in this paper. And the better classification algorithm has been selected
to classify weed and crop from the images. There are three main parts of proposed system are segmentation, classification and
error calculation. The developed algorithm based on area thresholding has been tested on weeds and various locations. Forty
one sample images have been tested and the result of some weed coverage rate is illustrated. Moreover, the misclassification
rate is also computed. An algorithm has been done to automate the tasks of segmentation and classification. The overall
process is implemented in MATLAB.
Keywords - Objects segmentation, Image processing, Plant classification, Area Thresholding
--------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
As weeds are frequently distributed non -uniformly within a
field, weeding is a very hard work [1]. Every year a large
amount of herbicide is used for removing weeds from
agricultural fields which is not only expensive but also a
source of environmental pollution. Moreover, both costly
and time consuming is caused due to hand labor [4].
Therefore, weed control is a necessary management practice
in agricultural systems, which is critical to sustain crop
productivity and quality [1]. Farmers need alternatives for
weed control due to the desire to reduce chemical use and
production costs [7]. For instance when growing vegetables
such as carrots, onions and cabbage the weeds can be
removed by special harrows but it cannot support for
economic system [3].
Nowadays, uniform spraying is the most common method
for herbicides application. However, this method is
inefficient and cost-ineffective as weed distribution is
usually non-uniform and highly aggregated in clumps within
the arable field [1]. There could be many parts of the field
that have none or insignificant volume of weeds. This
property of the weed distributions makes the development of
site-specific management feasible [8]. In this management,
the amount of herbicides applied is reduced through
spraying only the weed infested area of a field, where
different selective herbicides with corresponding application
rates are applied to control weed differently [5]. The
machine vision based approach uses shape, texture, color
and location based features individually of jointly to
discriminate between weed and crop [11]. In the other
research, Caltrans sprays roadside plant material with
herbicide to prevent the weeds from becoming a fire hazard
during the summer. The first step in identifying weeds
within an image involves classifying the pixels [12]. The
pixels shall be classified using a point operation. The
surrounding pixels will not bias a pixel‟s classification. The
purpose of segmenting the image into plant and background
pixels is to detect the amount of plant material within a
specific area [12]. If the amount of plant material reaches a
specific threshold, the area is targeted for herbicidal spray
application [12]. The spray threshold is set too close to the
background misclassification rate, then herbicide will be
wasted spraying background. Therefore, a larger
misclassification rate limits the smallest plant that can be
detected without targeting the background for spray [12].
A system that could make use of the spatial distribution
information in real-time and apply only the necessary
amounts of herbicide to the weed-infested area would be
much more efficient and minimize environmental damage.
Therefore, a high spatial resolution, real-time weed
infestation detection system seems to be the solution for
site-specific weed management.
In this paper, three main parts presented for weed and crop
classification. It has got (1) Image acquisition and gray
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transformation, (2) Image segmentation and noise removal,
and (3) Classification of weed and crop [2]. Two
classification methods are studied in this research. They are
classification based on intensity method and area
thresholding classification method. The first method is
depended on the intensity of the images [13]. However, this
algorithm cannot operate to classify weed and crop
automatically. So, the area thresholding method is used to
get reliable classified images. These methods are executed
depending on size of the plants. By using digital camera, the
input image is obtained. Most of images are needed to
change from the color images into grayscale images for easy
and fast processing. The segmentation step implemented by
using thresholding method. In the classification part, the
areas of segmented image are then compared with the
selected threshold for classification of weeds and crop [6].
2. MATERIALS AND METHODS
The first algorithm consists of five fractions: (i)
preprocessing, (ii) Binarization using Otsu‟s thresholding,
(iii) Marker control watershed segmentation, (iv) Gray
transformation and (v) classification based on intensity. The
simple weed and crop images are used to test the system.
Firstly, the color images are converted to gray scale images
for easy and fast processing [13]. Median filter is applied to
the gray image to reduce the amount of data. Otsu‟s method
is used to filtered image for converting black and white
image. This method chooses the optimal threshold to
minimize the intraclass variance of the black and white
pixels [14]. Then, binarized image is divided into different
regions according to watershed segmentation method. The
sobel operator is applied on the binary image to find the
gradient magnitude. By using this magnitude, estimate the
watershed transform that divide different regions. The
segmented image is changed into RGB segmented image to
distinct region. To classify weed and crop, the RGB image
is converted into gray image. Finally, the intensity value is
defined by manually to extract weed plant from the image.
The results image is described in Fig- 3. The intensity values
of the images can change due to light, dark, weather
condition and camera situation. Therefore, these values must
be defined for every weed and crop images. However, the
next algorithm can detect without manual extraction as can
be seen their results image.
The area thresholding classification system includes the five
steps. They are Excess Green Gray transformation,
segmentation, label the image, removing the unwanted data
and classification based on area thresholding. The system
block diagram of the system is illustrated in Fig-1, which
consists of three main steps:
1) The Excess green gray transformation 2G-R-B is
executed to easy and fast processing for
segmentation stage.
2) Background and plants of the image is separated
according to the gray index.
3) Extraction of weed and crop from the segmented
image by area thresholding.
And then the detailed algorithms of the system are also
described into step by step.
2.1 Image Acquisition
In this research, the weed and crop color images are
acquired through the digital camera. The sample images are
captured in the fields. Images are obtained at different times
of a day. Moreover, weeds and crop with varying canopy
size were selected to increase the difficulty of the
classification problem. The weed and crop images are taken
at an angle 45 degree with the ground in natural lighting
conditions with digital camera. The camera is mounted in
the height of 2.15m from the ground. The resolution of the
camera is set to 3648×2736 pixels during image capturing.
In the experimental analysis, all the images were resized to a
resolution of 320×240 pixels in order to reduce the
computation time [10]. Fig-2 illustrates sample weed and
crop images taken from the fields.
Input Image
Gray Transformation (ExG)
Binarization
Filtering
Labeling
Area Thresholding
Output Detected Image
Fig-1: Block diagram of the system
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(a) (b)
(c) (d)
Fig-2: The original images of the crop and some weed that
captured by using camera: (a) rape plant, (b) Lanchon, (c)
Pigweed and (d) Kyaut kut.
Fig-3: Sample result of classification based on intensity
method: Original image (up); Detected weed image (bottom-
left); Detected crop image (bottom-right).
2.2 Gray Transformation
The gray transformation processing is to turn the color
image to the gray image. The purpose of gray
transformation is to reduce the amount of color data in the
image so as to speed up the following processing. The color
difference between plants and background in the color
images should be kept as well as possible in the gray image.
Equation (1) is used for gray transformation in image
processing. Assuming the coordinate (x, y) is the pixel
point.
EXG(x, y) =2g-r-b (1)
Where, r= , g= , b=
In this equation R, G and B are the three components of
pixel color in RGB color space; ExG is the transformation
result, a gray value. The intensity information is highlighted
and discarded most color information in the color image
using equation (1). The pixel values in red channel and blue
channel color space are always less than the in green color.
Therefore, the Excess green feature is used to extract the
distinct color channel. The result of this algorithm is
illustrated in Fig- 4.
(a) The original input image
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(b) Result of the Green minus red image
(c) Result of the green minus blue image
Fig-4: Gray transformation
2.3 Segmentation
To remove background of the image, different techniques
such as thresholding-based segmentation, edge-based
segmentation, Color-based segmentation and watershed
segmentation can be used. In this study, gray removal
segmentation method is used for the segmentation task. All
the segmentation steps are done depending on the binary
image. Therefore, the grayscale image is converted into
binary image from the preprocessing stage. Thresholding
based on gray image is used to change the binary image. By
using the following equation
(2)
For all pixels in the original image the absolute values of
green minus red and green minus blue are calculated. These
give measurements of the pixels distance to the grayscale
line. If both of these distance values are greater than
threshold (T), the pixel is classified as plant (P). If none or
only one is greater than T, the pixel is classified as
background (Bg). The number of T is equivalent to the
threshold in the above mentioned index methods and has to
be chosen based on the available image material. For the
image of this paper T=20 has proven to give a very good
result. This method of removing pixels of high „greyness‟
can be seen as calculating two separate indices and then
requiring that a pixel is on the correct side of the threshold
in both cases to be classified as P. The result of binary
images includes the small noise as misclassified plant pixels.
Therefore each pixel of the segmented images is labeled
with a value according to the component it is assigned to.
After that the property of the region is defined into area.
Then the value of region is firmed to find the minimum area.
And then the minimum area of the region is removed from
the image as shown in fig-5. The 40 pixels of minimum
areas are discarded in this filtering stage.
(a)
(b)
Fig-5: Result of segmented (a) binary image and (b) filtered
image
2.4 Classification
The final segmented image is used as the input of the weed
and crop classification stage. Weed detection is executed
depended on their areas. Firstly, the segmented binary image
is labeled using 8-connected components. In this way, it can
easy to evaluate the areas of the objects. And then, a
threshold value is selected to classify weed plants and crop
plants. Therefore, threshold value is set at 6000 for this
research. The crop plants are mostly large the weed plants in
related to the testing. Therefore, the weed plant is detected
when the object areas are less than threshold value. In the
other way, the remaining objects are classified as crop
plants. The areas of the individual objects are calculated
according to the equation (3). In this equation, F (j, k) is
binary segmented image, xj and yk are scaled coordinates in
row and column and J and K are row and column of binary
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image. The detected weed and crop images are obtained by
comparing the threshold value and their areas.
(3)
The desired points of the image (xj and yk) are obtained
using the following equation:
xj =j+1/2
yk = k+1/2
The thresholding equation is illustrated in equation (4). The
optimal threshold value is selected at 6000.
(4)
(a) Result of detected weed image
(b) Result of detected crop image
(d) Result of classified image
Fig-6: Result of weed and crop classified images
If the sizes of the crop plants are equal to the weed plants,
the classification algorithm can cause the misclassification.
Therefore, those plants are misclassified results as can be
seen in Fig. 6. This original image consists of two crop
plants and four weeds plants. Among the two crop plants,
the area of the one crop plant is less than the system
threshold value. So, the misclassified image is obtained as
can be seen the Fig-7. (b).
(a) Original weed and crop image
(b) Detected weeds image with one crop is
misclassified as weed
(c) Detected crop image
(d) Misclassified image
Fig-7: Result of misclassified weed and crop images.
),()()(
1
),(
1 1
kjFyx
KJ
nmM n
k
J
j
K
k
m
jnm
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Table- 1: The Experimental Results of Area Thresholding
Num
Total
Area
Weed
Area
Plants
Coverage
Detected
weed
Coverage
1 21986 6574 28.60% 9.19%
2 9029 17260 22.50% 12.60%
3 9069 17406 22.70% 12.60%
4 10985 2891 14.30% 3.80%
In the Table1, plants coverage and detected weed coverage
rate are defined as:
(5)
(6)
In Table 1, data describes that the percent of the plants
coverage and detected weed coverage of the results images.
According to the above equations SUMw is the sum of all
weed pixels and Zone Area is the pixel count in the whole
image, and is equal to the row × column of the image pixels.
As a result, almost of 13% of weed coverage is found in the
sample images.
er= (7)
Where, er = error rate or misclassification rate
Nw = number of detected weeds
Nm = number of misclassified crop
Above the error equation (7) is used to compute the
misclassification rate. As the sample results is illustrated in
Table II. For example, the first image in this table has six
weed plants and three crop plants. But the number of output
detected weed plants are nine. So, all the crop plants are
defined as weed plants. Therefore, 33.3% error rate is
obtained when these misclassified plants is evaluated
according to the equation (7). The remaining error rate can
be generated in the same way.
3. RESULTS AND DISCUSSION
This paper presents two main algorithms for weed and crop
classification system. Fig-6 and 7 describe the classified
result images. The plants with blue color are defined as
weeds and the red plants are classified as crops. The given
algorithm processes two types of images for weed and crop.
The algorithm produced reliable classified images to be
sprayed. In the field, almost the images that captured
according to image acquisition step can be classified to give
to the spray system. By using the resultant image, the weed
position can determined according to the pixels of left to
right and top to bottom. Although the system could classify
weed to be sprayed, darker images cause more errors in the
segmentation and also in later steps of the algorithm. When
the weed and crop plants are overlapped, this method cannot
classify weed and crop. The 41 sample images have been
used to test in this study. Among them, 7 images are found
the misclassification. Thus the proposed algorithm is
suitable for weed and crop segmentation and classification.
It will support to get the reliable results in real-time
application.
Table-2: Error Calculations of misclassified Weed and Crop
Images for the Area Thresholding Algorithm
No.
Number
of
detected
weeds
Number of
misclassified
crop
Missed
weeds
Weed mis-
classifications
rate
1 9 3 0 33.30%
2 6 1 0 17%
3 5 1 0 20%
4 4 1 0 25%
5 5 0 0 0%
In this Table 2, error calculations have been executed by
manually counting the number of misclassifications in the
randomly chosen result images. The missed weeds in this
Table II are weeds that were not fully marked in red or blue.
The weed misclassifications shows the objects classified as
weeds that are actually part of crop plant. Both weed area is
greater than threshold value and crop area is less than
threshold value can cause the misdetection. Although the
second system has some drawback, the better classification
results could generated with small error.
4. CONCLUSIONS
In this research, segmentation method and classification
based on area thresholding method are developed. Excess
green gray transformation (ExG) and area thresholding
algorithms are combined to obtain the exactly classified
images. The system shows an effective and reliable
classification of images captured by a camera. The image
segmentation algorithm is very useful method in the image
processing and it is very helpful for the subsequent
processing. When the plants are separated from each other
in the images, the results have been shown to be better. Also
the lighting conditions are important to be able to make a
reliable analysis.
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ACKNOWLEDGMENT
The authors wish to thank to the head of Department of
Electronic Engineering, Mandalay Technological
University. The author would like to express special
appreciation and heartfelt thanks to her supervisor, Dr. Aung
Soe Khaing, Department of Electronic Engineering,
Mandalay Technological University for giving
understanding, helpful guidance, suggestion and directions
throughout the preparation of work. The author is also
sincerely thankful to all her teachers, Department of
Electronic Engineering, Mandalay Technological
University.
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Hossain Bari, and Eman Hossain, “Automated Weed
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[2]. Irshad Ahmad, Abdul Muhamin, Muhammad.Islam
and Shahid Nawaz,”Weed Classification using
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[3]. Siddiqi M., Sulaiman S., Faye I., and Ahmad I., “A
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[4]. Hossein Nejati, Zohreh Azimifar and Mohsen
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[5]. Mahammad Hameed Siddiqi, Sungyoung. Lee, Young-
Koo Lee, “Efficient Algorithm for Real-Time Specific
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[6]. S.Kiani and A.Jafari, “Crop Detection and Positioning
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[7]. Sajad KIANI, “Discriminating the Corn Plants from
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[8]. Tian L., Reid J., and Gerhards R., “Development of a
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issue 4, pp.893-900, 1999.
[9] DIAO ZHIHUA, WANG HUAN, SONG YINMAO,
and WANG YUNPENG, “Image Segmentation
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[12]. Chris Gliever, EEC206 Project Report, “Color
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[13]. Irshad Ahmad, Muhammad, Iram Fatima, Sungyoung
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BIOGRAPHIES
Su Hnin Hlaing received her
Bachelor of Engineering in
Electronic Communication
Technology from the Mandalay
Technological University,
Myanmar in 2008. She is now
master student in the Mandalay
Technological University,
Myanmar. Her research interests
include image processing, weed
and crop segmentation and
classification.
Aung Soe Khaing received his
PhD in Electronic Engineering
from Mandalay Technological
University, Mandalay,
Myanmar, in 2011. He was
doing research on Spatial
Frequency Analysis of the
Human Brain at the Institute of
Biomedical Engineering and
Informatics, Technical
University Ilmenau, Germany.
He is now Associate Professor at
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Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 382
Department of Electronic
Engineering, Mandalay
Technological University,
Mandalay, Myanmar. His
research interests include
computer based
Electrocardiogram (ECG)
system, biomedical signal and
image processing,
bioinstrumentation and
telemedicine.