Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587460
Likitha Sr, N. N
Medical images play a critical part in the doctor's ability to make the correct diagnosis and in the patient's treatment. Intelligent algorithms make it possible to swiftly recognize lesions in medical imaging, and extracting features from images is very significant. Various algorithms have been integrated into medical imaging in a number of research. The basic architecture of CNN is constructed by focusing on picture feature extraction using a convolutional neural network (CNN). The research is expanded to multi-channel input CNN for visual feature extraction in order to overcome the constraints of machine vision and human vision. Glioma tumor, meningioma tumor, pituitary tumor, and no tumor are the four classifications investigated in this study, which includes roughly 3300 MRI samples gathered from kaggel. The BrainNet that has been implemented has a 98.31 percent of training accuracy and an 87.80 percent of validation accuracy. Deep architectures such as InceptionNet, ResNet, and XceptionNet were also tested with and without transfer learning to see which strategy performed better.
{"title":"Classification and Segmentation of Brain MRI images using Deep Learning","authors":"Likitha Sr, N. N","doi":"10.1109/GCAT52182.2021.9587460","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587460","url":null,"abstract":"Medical images play a critical part in the doctor's ability to make the correct diagnosis and in the patient's treatment. Intelligent algorithms make it possible to swiftly recognize lesions in medical imaging, and extracting features from images is very significant. Various algorithms have been integrated into medical imaging in a number of research. The basic architecture of CNN is constructed by focusing on picture feature extraction using a convolutional neural network (CNN). The research is expanded to multi-channel input CNN for visual feature extraction in order to overcome the constraints of machine vision and human vision. Glioma tumor, meningioma tumor, pituitary tumor, and no tumor are the four classifications investigated in this study, which includes roughly 3300 MRI samples gathered from kaggel. The BrainNet that has been implemented has a 98.31 percent of training accuracy and an 87.80 percent of validation accuracy. Deep architectures such as InceptionNet, ResNet, and XceptionNet were also tested with and without transfer learning to see which strategy performed better.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115580440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587551
Nachiket Dunbray, R. Rane, Sparsh Nimje, Jayesh Katade, Shreyas Mavale
Diabetes is also known as diabetes mellitus is one of the world’s most prominent health hazards of the current times. It is a chronic disease in which the pancreas isn’t able to produce the right amounts of insulin for the body to absorb glucose into the body cells for energy and stays in the bloodstream in turn raising the blood glucose levels. If this is not detected and treated on time, it can affect other body organs as well and leads to organ failure thus becoming fatal.Machine learning and data mining are two emerging fields in today’s tech world. With the help of these methods, we can observe the past data behaviors and can then predict the future outcomes to a certain extent. This brings rise to the term ‘prediction models’ that we all know in today’s tech world. Keeping this in mind, we can create predictive models for diabetes prediction. This helps in the early detection of diabetes so that it can be treated at the earliest to avoid complications. By finding out the highest accuracy model, we can accurately predict whether the patient is diabetic beforehand and prevent further health issues. This research paper discusses the techniques that have been used to create a unique predictive model for the prediction of diabetes.
{"title":"A Novel Prediction Model for Diabetes Detection Using Gridsearch and A Voting Classifier between Lightgbm and KNN","authors":"Nachiket Dunbray, R. Rane, Sparsh Nimje, Jayesh Katade, Shreyas Mavale","doi":"10.1109/GCAT52182.2021.9587551","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587551","url":null,"abstract":"Diabetes is also known as diabetes mellitus is one of the world’s most prominent health hazards of the current times. It is a chronic disease in which the pancreas isn’t able to produce the right amounts of insulin for the body to absorb glucose into the body cells for energy and stays in the bloodstream in turn raising the blood glucose levels. If this is not detected and treated on time, it can affect other body organs as well and leads to organ failure thus becoming fatal.Machine learning and data mining are two emerging fields in today’s tech world. With the help of these methods, we can observe the past data behaviors and can then predict the future outcomes to a certain extent. This brings rise to the term ‘prediction models’ that we all know in today’s tech world. Keeping this in mind, we can create predictive models for diabetes prediction. This helps in the early detection of diabetes so that it can be treated at the earliest to avoid complications. By finding out the highest accuracy model, we can accurately predict whether the patient is diabetic beforehand and prevent further health issues. This research paper discusses the techniques that have been used to create a unique predictive model for the prediction of diabetes.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121621984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587565
Aniket Zope, Vandana Inamdar
In recent times the use of digital images has increased the demand for high-resolution images. The images captured are sometimes affected by noise, making visualization of the objects difficult, so the image super-resolution method is used to solve this problem. This research is based on a predefined Edge Informed Single Image Super-Resolution(EISR). The model is based on a deep learning approach that uses a convolutional neural network(CNN) and works on single image super-resolution(SISR). The first stage of the proposed model is the bi-cubic interpolation stage, followed by the Edge enhancement and Image completion stage. A qualitative comparison between the existing and proposed models on the x2 scaling factor is made.
{"title":"Edge Enhancement for Image Super-Resolution using Deep Learning Approach","authors":"Aniket Zope, Vandana Inamdar","doi":"10.1109/GCAT52182.2021.9587565","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587565","url":null,"abstract":"In recent times the use of digital images has increased the demand for high-resolution images. The images captured are sometimes affected by noise, making visualization of the objects difficult, so the image super-resolution method is used to solve this problem. This research is based on a predefined Edge Informed Single Image Super-Resolution(EISR). The model is based on a deep learning approach that uses a convolutional neural network(CNN) and works on single image super-resolution(SISR). The first stage of the proposed model is the bi-cubic interpolation stage, followed by the Edge enhancement and Image completion stage. A qualitative comparison between the existing and proposed models on the x2 scaling factor is made.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124131120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587770
Midhun Manoj, S. Ananthakrishnan, Palagati Sriharshitha, V. Pandi
This paper presents a working and Simulation of a four-axis welding robot. The Robotic aspects have many applications in industries including welding. These tasks are described according to the end effector function. This work deals with handling a robotic arm or welding arm by a master manipulator where the end effector is used to hold. For the movement of the robotic arm, a fuzzy logic controller is used, and its performance is compared to that of a PID controller. Forward kinematics deal with the problem of finding end-effector pose (position + orientation) with given joints variables using two methods: Homogeneous transformation and Denavit-Hartenberg Representation. Actuation modes include Torque and Motion which are described through simulation showing effects on working of machine due to dynamics. The model has been done based on MATLAB/Simulink software.
{"title":"Four Axis Welding Robot Control using Fuzzy Logic","authors":"Midhun Manoj, S. Ananthakrishnan, Palagati Sriharshitha, V. Pandi","doi":"10.1109/GCAT52182.2021.9587770","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587770","url":null,"abstract":"This paper presents a working and Simulation of a four-axis welding robot. The Robotic aspects have many applications in industries including welding. These tasks are described according to the end effector function. This work deals with handling a robotic arm or welding arm by a master manipulator where the end effector is used to hold. For the movement of the robotic arm, a fuzzy logic controller is used, and its performance is compared to that of a PID controller. Forward kinematics deal with the problem of finding end-effector pose (position + orientation) with given joints variables using two methods: Homogeneous transformation and Denavit-Hartenberg Representation. Actuation modes include Torque and Motion which are described through simulation showing effects on working of machine due to dynamics. The model has been done based on MATLAB/Simulink software.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123283543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587461
Supriya Katwe, N. Iyer, Moin Khan, Mathew Peters, Mahesh S. Mahale
The fundamental task in an autonomous vehicle navigation system is localization from the available sensor measurements. GPS in the vehicles locates it with error of 1 to 10 meters so localization process should be performed to avoid fatal accidents. The realization of algorithms to estimate our vehicle’s position precisely is Localization. Odometry, Kalman Filter, Particle Filter and SLAM(Simultaneous Localization And Mapping) are the techniques used in an autonomous vehicle to localize itself in the map. Among these the particle filter is widely employed in the localization of autonomous vehicles as it provides accurate position of the vehicle in the environment. This paper aims at a localization technique for autonomous vehicles or robots using Particle Filter algorithm. The position estimator is implemented using the GPS and IMU sensor measurements. The map contains specific landmarks identified such as buildings and poles which assist the vehicle to know its position accurately by matching the distance between them in the particle filtering process. The results show that this algorithm can deliver accurate vehicle positioning even in erroneous GPS data.
{"title":"Particle Filter Based Localization of Autonomous Vehicle","authors":"Supriya Katwe, N. Iyer, Moin Khan, Mathew Peters, Mahesh S. Mahale","doi":"10.1109/GCAT52182.2021.9587461","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587461","url":null,"abstract":"The fundamental task in an autonomous vehicle navigation system is localization from the available sensor measurements. GPS in the vehicles locates it with error of 1 to 10 meters so localization process should be performed to avoid fatal accidents. The realization of algorithms to estimate our vehicle’s position precisely is Localization. Odometry, Kalman Filter, Particle Filter and SLAM(Simultaneous Localization And Mapping) are the techniques used in an autonomous vehicle to localize itself in the map. Among these the particle filter is widely employed in the localization of autonomous vehicles as it provides accurate position of the vehicle in the environment. This paper aims at a localization technique for autonomous vehicles or robots using Particle Filter algorithm. The position estimator is implemented using the GPS and IMU sensor measurements. The map contains specific landmarks identified such as buildings and poles which assist the vehicle to know its position accurately by matching the distance between them in the particle filtering process. The results show that this algorithm can deliver accurate vehicle positioning even in erroneous GPS data.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122368965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587758
Sneha Goel, Arpit Mittal, Shipra, Ankush Gupta
Blogging is something where you can share your knowledge with a large network and it also serves as a means to continue your passion. In this project, we have designed a blogging application which has features like facial authentication, social media integration along with Paytm integration. It has been developed by making use of the functionalities available in the Open-Computer-Vision (Open CV) library using Python. It has used Haar-Cascades for face detection purposes and Local binary pattern histograms (LBPH) recognizer for facial recognition.
写博客是你可以在一个大的网络上分享你的知识的地方，也是你继续激情的一种方式。在这个项目中，我们设计了一个博客应用程序，它具有面部认证，社交媒体集成以及Paytm集成等功能。它是通过使用Python利用开放计算机视觉(Open computer - vision, Open CV)库中的可用功能开发的。它使用haar级联进行人脸检测，使用局部二值模式直方图(LBPH)识别器进行人脸识别。
{"title":"A Blogging Application Based on Facial Authentication","authors":"Sneha Goel, Arpit Mittal, Shipra, Ankush Gupta","doi":"10.1109/GCAT52182.2021.9587758","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587758","url":null,"abstract":"Blogging is something where you can share your knowledge with a large network and it also serves as a means to continue your passion. In this project, we have designed a blogging application which has features like facial authentication, social media integration along with Paytm integration. It has been developed by making use of the functionalities available in the Open-Computer-Vision (Open CV) library using Python. It has used Haar-Cascades for face detection purposes and Local binary pattern histograms (LBPH) recognizer for facial recognition.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128899525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587763
Bhagyesh Desai, Er. Nitika Kapoor
Software product refers to the software which is developed for a specific requirement. Simultaneously, engineering deals with the development of product using explicit technical fundamentals and methods. The software defect can be predicted in diverse stages in which data is utilized as input and pre-processed, attributes are extracted, and classification is performed. This research work makes the implementation of several classifiers in order to predict the software defect. These classifiers are GNB (gaussian naive bayes), Bernoulli NB, RF (random forest) and MLP (multilayer perceptron) which are employed with the objective of forecasting the software defect. The performance of the software defect is enhanced by developing an ensemble classifier. In the introduced ensemble classifier, the PCA (Principal Component Analysis) algorithm is integrated with class balancing. Python is executed to implement the introduced model. Diverse metrics are considered to analyze the results concerning accuracy, precision and recall.
{"title":"Hybrid Classification Approach for Software Defect Prediction with Feature Reduction and Clustering","authors":"Bhagyesh Desai, Er. Nitika Kapoor","doi":"10.1109/GCAT52182.2021.9587763","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587763","url":null,"abstract":"Software product refers to the software which is developed for a specific requirement. Simultaneously, engineering deals with the development of product using explicit technical fundamentals and methods. The software defect can be predicted in diverse stages in which data is utilized as input and pre-processed, attributes are extracted, and classification is performed. This research work makes the implementation of several classifiers in order to predict the software defect. These classifiers are GNB (gaussian naive bayes), Bernoulli NB, RF (random forest) and MLP (multilayer perceptron) which are employed with the objective of forecasting the software defect. The performance of the software defect is enhanced by developing an ensemble classifier. In the introduced ensemble classifier, the PCA (Principal Component Analysis) algorithm is integrated with class balancing. Python is executed to implement the introduced model. Diverse metrics are considered to analyze the results concerning accuracy, precision and recall.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"53 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127988694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587743
A. Chaitanya Kumar, Arjun Sharma, Velmathi Guruviah
Vehicular operation especially through four-wheeled vehicles is one of the most common ways of traveling across the world. A possibility for an unfortunate accident is always a possibility. Unfortunately, about 1.35 million people globally lose their lives on an average every year according to the World Health Organization (WHO) [1], many such incidents are preventable. To help address these avoidable incidents, there is a need to implement a means to keep a check on the driver at all times of vehicle operation. The authors propose a real-time solution that detects any and all instances of a driver experiencing drowsiness/fatigue or any form of distraction while driving. The implementation also undertakes appropriate measures to alert the driver and other passengers apart from any designated contacts about each such incidence of interest wherein the driver showcases said behaviors. Finally, the authors develop the above functionalities as an application compatible in devices running Windows or Linux Operating Systems.
{"title":"Driver Activity Oversight System","authors":"A. Chaitanya Kumar, Arjun Sharma, Velmathi Guruviah","doi":"10.1109/GCAT52182.2021.9587743","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587743","url":null,"abstract":"Vehicular operation especially through four-wheeled vehicles is one of the most common ways of traveling across the world. A possibility for an unfortunate accident is always a possibility. Unfortunately, about 1.35 million people globally lose their lives on an average every year according to the World Health Organization (WHO) [1], many such incidents are preventable. To help address these avoidable incidents, there is a need to implement a means to keep a check on the driver at all times of vehicle operation. The authors propose a real-time solution that detects any and all instances of a driver experiencing drowsiness/fatigue or any form of distraction while driving. The implementation also undertakes appropriate measures to alert the driver and other passengers apart from any designated contacts about each such incidence of interest wherein the driver showcases said behaviors. Finally, the authors develop the above functionalities as an application compatible in devices running Windows or Linux Operating Systems.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128488820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587764
Eeda Srinavya, Maddula Bhaswitha, S. Vineeth, B. K. Priya
Every year lot of children are passing away due to hyperthermia and coronary heart strokes. This is happening because the children are left inside the car unknowingly. Many incidents of such cases are increasing rapidly in the past few decades. These incidents are recognized as the automobile injuries and for this a research has been done to know more about the fat situations of the surroundings of such instances. By the research it is known that there are two elements which made the kids more liable to hyperthermia when compared to adults. A systematic rationalization about how this can be appeared that the children are left unknowingly by their parents in the vehicle can be identified with working memory, it builds up the pressure obstruction and impends to a particular interest. In past two years, 16 children of these cases in Italy and 53 children of these cases in US of infant hyperthermia because of abandonment in vehicles were perceived. These discoveries propose that instructive bundles and writing for guardians concerning auto insurance should incorporate such data about these threats of the heart stress, in fact such actions are unknowingly happened and not intentionally done. In triumph over these issues a prototype has been proposed by means of the child safety alert system.
{"title":"Implementation of Child Safety Alert System in Automobiles","authors":"Eeda Srinavya, Maddula Bhaswitha, S. Vineeth, B. K. Priya","doi":"10.1109/GCAT52182.2021.9587764","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587764","url":null,"abstract":"Every year lot of children are passing away due to hyperthermia and coronary heart strokes. This is happening because the children are left inside the car unknowingly. Many incidents of such cases are increasing rapidly in the past few decades. These incidents are recognized as the automobile injuries and for this a research has been done to know more about the fat situations of the surroundings of such instances. By the research it is known that there are two elements which made the kids more liable to hyperthermia when compared to adults. A systematic rationalization about how this can be appeared that the children are left unknowingly by their parents in the vehicle can be identified with working memory, it builds up the pressure obstruction and impends to a particular interest. In past two years, 16 children of these cases in Italy and 53 children of these cases in US of infant hyperthermia because of abandonment in vehicles were perceived. These discoveries propose that instructive bundles and writing for guardians concerning auto insurance should incorporate such data about these threats of the heart stress, in fact such actions are unknowingly happened and not intentionally done. In triumph over these issues a prototype has been proposed by means of the child safety alert system.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131000283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587545
N. S, H. S. Devi
Leaves are the primary sources for identifying a healthy plant and identifying many plant diseases. When leaf disease has not been correctly analyzed and early detection is not taken may produce a severe effect on the plants, which results in the loss of yield and quality of the production. Identifying or monitoring the diseases manually requires a tremendous amount of work and a lot of processing time. To overcome this, today, image processing has been widely used to identify conditions in plants to increase production. This paper has proposed a methodology to segment the leaf region using different color space models and flood filling algorithms. This system can be future used to classify the type of leaf disease.
{"title":"Leaf Region Segmentation for Plant Leaf Disease Detection using Color Conversion and Flood Filling","authors":"N. S, H. S. Devi","doi":"10.1109/GCAT52182.2021.9587545","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587545","url":null,"abstract":"Leaves are the primary sources for identifying a healthy plant and identifying many plant diseases. When leaf disease has not been correctly analyzed and early detection is not taken may produce a severe effect on the plants, which results in the loss of yield and quality of the production. Identifying or monitoring the diseases manually requires a tremendous amount of work and a lot of processing time. To overcome this, today, image processing has been widely used to identify conditions in plants to increase production. This paper has proposed a methodology to segment the leaf region using different color space models and flood filling algorithms. This system can be future used to classify the type of leaf disease.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129869934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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