The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Disease Identification and Detection in Apple Treeijtsrd
Apple trees are widely used in the landscaping of vast farms and private gardens. Also, the kings eye finds it difficult to detect disease early and prevent it from spreading to other parts of the plant. Distinguishing and obtaining accuracy, deep models relating to the convolutional neural network were developed. This text compares and compares various current models. It includes research that can be applied to differentiate and differentiate plant leaf infections. R Tanseer Ahmed | Dr. S.K Manju Bargavi "Disease Identification and Detection in Apple Tree" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42405.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42405/disease-identification-and-detection-in-apple-tree/r-tanseer-ahmed
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
LEAF DISEASE DETECTION USING IMAGE PROCESSING AND SUPPORT VECTOR MACHINE (SVM)Journal For Research
in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it’s important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
Diseases in plants cause major production and economic losses as well as a reduction in both the quality and quantity of agricultural products. In India, 70% of the population depends on agriculture and contributes 17% towards the GDP of the country. Now a day’s plant disease detection has received increasing attention in monitoring large fields of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for the detection and identification of plant diseases.
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to a lack of the necessary infrastructure. The combination of increasing global smartphone penetration and the recent advancement, in computer vision made possible by deep learning, and transfer learning has paved the way for smart systems to diagnose diseases at initial stages, as soon as they appear in plant leaves.
Therefore, a convolutional neural network is created and developed to perform plant disease detection and classification using leaf images of healthy and diseased of 18 crops. Recent developments in deep neural networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. Deep Learning (DL) is the fastest growing and a broader part of the machine learning family. Deep learning uses convolutional neural networks for image classification as it gives the most accurate results in solving real-world problems.
Creating and training a CNN model from scratch is a tedious process when compared to the usage of existing deep learning models for various applications to achieve maximum accuracy. So depending on the application various models can be used or retrained. In this project, we have implemented VGG16 and VGG19 architecture for the leaf diseases of 18 crops and compare their accuracy, VGG16 have shown slightly good accuracy as compared to that of VGG19, using “New Plant Disease Dataset” to train and validate both the models, which contains 87k images of 38 different plant leaf diseases.
Disease Identification and Detection in Apple Treeijtsrd
Apple trees are widely used in the landscaping of vast farms and private gardens. Also, the kings eye finds it difficult to detect disease early and prevent it from spreading to other parts of the plant. Distinguishing and obtaining accuracy, deep models relating to the convolutional neural network were developed. This text compares and compares various current models. It includes research that can be applied to differentiate and differentiate plant leaf infections. R Tanseer Ahmed | Dr. S.K Manju Bargavi "Disease Identification and Detection in Apple Tree" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42405.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42405/disease-identification-and-detection-in-apple-tree/r-tanseer-ahmed
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
LEAF DISEASE DETECTION USING IMAGE PROCESSING AND SUPPORT VECTOR MACHINE (SVM)Journal For Research
in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it’s important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
Diseases in plants cause major production and economic losses as well as a reduction in both the quality and quantity of agricultural products. In India, 70% of the population depends on agriculture and contributes 17% towards the GDP of the country. Now a day’s plant disease detection has received increasing attention in monitoring large fields of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for the detection and identification of plant diseases.
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to a lack of the necessary infrastructure. The combination of increasing global smartphone penetration and the recent advancement, in computer vision made possible by deep learning, and transfer learning has paved the way for smart systems to diagnose diseases at initial stages, as soon as they appear in plant leaves.
Therefore, a convolutional neural network is created and developed to perform plant disease detection and classification using leaf images of healthy and diseased of 18 crops. Recent developments in deep neural networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. Deep Learning (DL) is the fastest growing and a broader part of the machine learning family. Deep learning uses convolutional neural networks for image classification as it gives the most accurate results in solving real-world problems.
Creating and training a CNN model from scratch is a tedious process when compared to the usage of existing deep learning models for various applications to achieve maximum accuracy. So depending on the application various models can be used or retrained. In this project, we have implemented VGG16 and VGG19 architecture for the leaf diseases of 18 crops and compare their accuracy, VGG16 have shown slightly good accuracy as compared to that of VGG19, using “New Plant Disease Dataset” to train and validate both the models, which contains 87k images of 38 different plant leaf diseases.
Plant Leaf Disease Analysis using Image Processing Technique with Modified SV...Tarun Kumar
In this computing era, image processing has
spread its wings in human life upto the extent that image
has become an integral part of their life. There are various
applications of image processing in the field of commerce,
engineering, graphic design, journalism, architecture and
historical research. In this research work, Image
processing is considered for the analysis of plant leaf
diseases. Plant leaf diseases can be detected based on the
disease symptoms. Here, dataset of disease affected leaves
is considered for experimentation. This dataset contains
the plant leaves suffered from the
AlternariaAlternata,Cercospora Leaf Spot, Anthracnose
andBacterial Blight along with some healthy leaf images.
For this analysis, an autonomous approach of modified
SVM-CS is introduces. Here, concept of cuckoo search is
considered to optimize the classification parameters. These
parameters further help to find more accurate solutions.
This autonomous approach also extracts the healthy
portion and disease affected leaf portion along with the
accuracy of results.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
Segmentation of unhealthy region of plant leaf using image processing techniqueseSAT Journals
Abstract A segmentation technique is used to segment the diseased portion of a leaf. Based on the segmented area texture and color feature, disease can be identified by classification technique. There are many segmentation techniques such as Edge detection, Thresholding, K-Means clustering, Fuzzy C-Means clustering, Penalized Fuzzy C-Means, Unsupervised segmentation. Segmentation of diseased area of a plant leaf is the first step in disease detection and identification which plays crucial role in agriculture research. This paper provides different segmentation techniques that are used to segment diseased leaf of a plant. Keywords: Fuzzy C-Means, K-Means, Penalized FCM, Unsupervised Fuzzy Clustering
Wheat leaf disease detection using image processingIJLT EMAS
India is a agricultural based county where approx 70%
of population depend on agriculture. Now a days the plant
disease detection is very important because agriculture is the
backbone of the county like india. Farmer is not aware what type
of disease plant having and how to prevent them from these
diseases. To overcome from these we are going to develop a
technique in which we can able to detect plant disease using
image processing technique. This includes following steps: image
acquisition image pre-processing, feature extraction and at last
we apply a classifier know as neural network.
EDGE DETECTION IN DIGITAL IMAGE USING MORPHOLOGY OPERATIONIJEEE
This paper shows a method in digital image processing technique to find the defects in tablets. In this paper we use mathematical manipulation, to detect the defected tablet packet.
Disease Detection in Plant Leaves using K-Means Clustering and Neural Networkijtsrd
The most contributing variable for the Indian Economy is Agriculture yet at the same time there is absence of mechanical improvement in many parts of it. The harm caused by rising, re developing and endemic pathogens, is vital in plant frameworks and prompts potential misfortune. The harvest generation misfortunes its quality because of much infections and some of the time they happen however are indeed, even not obvious with stripped eyes. Plant malady recognition is one such dull process that is hard to be inspected by exposed eye. This paper shows an answer utilizing image processing calculations by loading the image, preprocessing and feature extraction using K means clustering and segmentation method to identify the disease with which the plant leaf been affected. P. Harini | V. Chandran "Disease Detection in Plant Leaves using K-Means Clustering and Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29562.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/29562/disease-detection-in-plant-leaves-using-k-means-clustering-and-neural-network/p-harini
Comparative Study on Machine Learning Algorithms for Network Intrusion Detect...ijtsrd
Network has brought convenience to the earth by permitting versatile transformation of information, however it conjointly exposes a high range of vulnerabilities. A Network Intrusion Detection System helps network directors and system to view network security violation in their organizations. Characteristic unknown and new attacks are one of the leading challenges in Intrusion Detection System researches. Deep learning that a subfield of machine learning cares with algorithms that are supported the structure and performance of brain known as artificial neural networks. The improvement in such learning algorithms would increase the probability of IDS and the detection rate of unknown attacks. Throughout, we have a tendency to suggest a deep learning approach to implement increased IDS and associate degree economical. Priya N | Ishita Popli "Comparative Study on Machine Learning Algorithms for Network Intrusion Detection System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38175.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-network/38175/comparative-study-on-machine-learning-algorithms-for-network-intrusion-detection-system/priya-n
Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
The major problem that the farmers around the world face is losses, because of pests, disease or a nutrient deficiency. They depend upon the information that they get from the agricultural departments for the diagnosis of plant leaf disease. This process is lengthy and complicated. Here comes a system to help farmers everywhere in the world by automatically detecting plant leaf diseases accurately and within no time. The proposed system is capable of identifying the disease of majorly 5 crops which are corn, sugarcane, wheat, and grape. In this paper, the proposed system uses the Mobile Net model, a type of CNN for classification of leaf disease. Several experiments are performed on the dataset to get the accurate output. This system ensures to give more accurate results than the previous systems. Shivani Machha | Nikita Jadhav | Himali Kasar | Prof. Sumita Chandak ""Crop Leaf Disease Diagnosis using Convolutional Neural Network"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd29952.pdf
Paper Url : https://www.ijtsrd.com/engineering/information-technology/29952/crop-leaf-disease-diagnosis-using-convolutional-neural-network/shivani-machha
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Plant Leaf Disease Analysis using Image Processing Technique with Modified SV...Tarun Kumar
In this computing era, image processing has
spread its wings in human life upto the extent that image
has become an integral part of their life. There are various
applications of image processing in the field of commerce,
engineering, graphic design, journalism, architecture and
historical research. In this research work, Image
processing is considered for the analysis of plant leaf
diseases. Plant leaf diseases can be detected based on the
disease symptoms. Here, dataset of disease affected leaves
is considered for experimentation. This dataset contains
the plant leaves suffered from the
AlternariaAlternata,Cercospora Leaf Spot, Anthracnose
andBacterial Blight along with some healthy leaf images.
For this analysis, an autonomous approach of modified
SVM-CS is introduces. Here, concept of cuckoo search is
considered to optimize the classification parameters. These
parameters further help to find more accurate solutions.
This autonomous approach also extracts the healthy
portion and disease affected leaf portion along with the
accuracy of results.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
Segmentation of unhealthy region of plant leaf using image processing techniqueseSAT Journals
Abstract A segmentation technique is used to segment the diseased portion of a leaf. Based on the segmented area texture and color feature, disease can be identified by classification technique. There are many segmentation techniques such as Edge detection, Thresholding, K-Means clustering, Fuzzy C-Means clustering, Penalized Fuzzy C-Means, Unsupervised segmentation. Segmentation of diseased area of a plant leaf is the first step in disease detection and identification which plays crucial role in agriculture research. This paper provides different segmentation techniques that are used to segment diseased leaf of a plant. Keywords: Fuzzy C-Means, K-Means, Penalized FCM, Unsupervised Fuzzy Clustering
Wheat leaf disease detection using image processingIJLT EMAS
India is a agricultural based county where approx 70%
of population depend on agriculture. Now a days the plant
disease detection is very important because agriculture is the
backbone of the county like india. Farmer is not aware what type
of disease plant having and how to prevent them from these
diseases. To overcome from these we are going to develop a
technique in which we can able to detect plant disease using
image processing technique. This includes following steps: image
acquisition image pre-processing, feature extraction and at last
we apply a classifier know as neural network.
EDGE DETECTION IN DIGITAL IMAGE USING MORPHOLOGY OPERATIONIJEEE
This paper shows a method in digital image processing technique to find the defects in tablets. In this paper we use mathematical manipulation, to detect the defected tablet packet.
Disease Detection in Plant Leaves using K-Means Clustering and Neural Networkijtsrd
The most contributing variable for the Indian Economy is Agriculture yet at the same time there is absence of mechanical improvement in many parts of it. The harm caused by rising, re developing and endemic pathogens, is vital in plant frameworks and prompts potential misfortune. The harvest generation misfortunes its quality because of much infections and some of the time they happen however are indeed, even not obvious with stripped eyes. Plant malady recognition is one such dull process that is hard to be inspected by exposed eye. This paper shows an answer utilizing image processing calculations by loading the image, preprocessing and feature extraction using K means clustering and segmentation method to identify the disease with which the plant leaf been affected. P. Harini | V. Chandran "Disease Detection in Plant Leaves using K-Means Clustering and Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29562.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/29562/disease-detection-in-plant-leaves-using-k-means-clustering-and-neural-network/p-harini
Comparative Study on Machine Learning Algorithms for Network Intrusion Detect...ijtsrd
Network has brought convenience to the earth by permitting versatile transformation of information, however it conjointly exposes a high range of vulnerabilities. A Network Intrusion Detection System helps network directors and system to view network security violation in their organizations. Characteristic unknown and new attacks are one of the leading challenges in Intrusion Detection System researches. Deep learning that a subfield of machine learning cares with algorithms that are supported the structure and performance of brain known as artificial neural networks. The improvement in such learning algorithms would increase the probability of IDS and the detection rate of unknown attacks. Throughout, we have a tendency to suggest a deep learning approach to implement increased IDS and associate degree economical. Priya N | Ishita Popli "Comparative Study on Machine Learning Algorithms for Network Intrusion Detection System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38175.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-network/38175/comparative-study-on-machine-learning-algorithms-for-network-intrusion-detection-system/priya-n
Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
The major problem that the farmers around the world face is losses, because of pests, disease or a nutrient deficiency. They depend upon the information that they get from the agricultural departments for the diagnosis of plant leaf disease. This process is lengthy and complicated. Here comes a system to help farmers everywhere in the world by automatically detecting plant leaf diseases accurately and within no time. The proposed system is capable of identifying the disease of majorly 5 crops which are corn, sugarcane, wheat, and grape. In this paper, the proposed system uses the Mobile Net model, a type of CNN for classification of leaf disease. Several experiments are performed on the dataset to get the accurate output. This system ensures to give more accurate results than the previous systems. Shivani Machha | Nikita Jadhav | Himali Kasar | Prof. Sumita Chandak ""Crop Leaf Disease Diagnosis using Convolutional Neural Network"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd29952.pdf
Paper Url : https://www.ijtsrd.com/engineering/information-technology/29952/crop-leaf-disease-diagnosis-using-convolutional-neural-network/shivani-machha
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Foliage Measurement Using Image Processing TechniquesIJTET Journal
Automatic detection of fruit and leaf diseases is essential to automatically detect the symptoms of diseases as early as they appear on the growing stage. This system helps to detect the diseases on fruit during farming , right from plan and easily monitoring the diseases of grapes leaf and apple fruit. By using this system we can avoid the economical loss due to various diseases in agriculture production. K-means clustering technique is used for segmentation. The features are extracted from the segmented image and artificial neural network is used for training the image database and classified their performance to the respective disease categories. The experimental results express that what type of disease can be affected in the fruit and leaf .
Pine wilt disease spreading prevention system using semantic segmentation IJECEIAES
Pine wilt disease is a disease that affects ecosystems by rapidly killing trees in a short period of time due to the close interaction between three factors such as trees, mediates, and pathogens. There are no 100% mortality infectious forest pests. According to the Korea Forest Service survey, as of April 2019, the damage of pine re-nematode disease was about 490,000 dead trees in 117 cities, counties and wards across the country. It's a fatal condition. In order to prevent this problem, this paper proposes a system that detects dead trees, early infection trees, and the like, using deep learningbased semantic segmentation. In addition, drones were used to photograph the area of the forest, and a separate pixel segmentation label could be used to identify three levels of transmission information: Suspicion, attention, and confirmation. This allows the user to grasp information such as area, location, and alarm to prevent the spread of re-nematode disease.
Pest Control in Agricultural Plantations Using Image ProcessingIOSR Journals
Abstract: Monocropped plantations are unique to India and a handful of countries throughout the globe. Essentially, the FOREST approach of growing coffee along with in India has enabled the plantation to fight many outbreaks of pests and diseases. Mono cropped Plantations are under constant threat of pest and disease incidence because it favours the build up of pest population. To cope with these problems, an automatic pest detection algorithm using image processing techniques in MATLAB has been proposed in this paper. Image acquisition devices are used to acquire images of plantations at regular intervals. These images are then subjected to pre-processing, transformation and clustering.
Using deep learning algorithms to classify crop diseasesIJECEIAES
The use of deep learning algorithms for the classification of crop diseases is one of the promising areas in agricultural technology. This is due to the need for rapid and accurate detection of plant diseases, which allows timely measures to be taken to treat them and prevent their spread. One of them is to increase productivity and maintain land quality through the timely detection of diseases and pests in agriculture and their elimination. Traditional classification methods in machine learning and algorithms in deep learning were compared to note the high accuracy in detecting pests and crop diseases. The advantages and disadvantages of each model considered during training were taken into account, and the Inception V3 algorithm was incorporated into the application. They can monitor the condition of crops on a daily basis with the help of new technology-applications on gadgets. Aerial photographs used by research institutes and agricultural grain centers do not show the changes that occur in agricultural grains, that is, diseases and pests. Therefore, the method proposed in this paper determines the types of diseases and pests of cereals through a mobile application and suggests ways to deal with them.
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In an agricultural field plant diseases are very important aspect as it directly affect on the production of plant and economical value of market. In this research generally we uses image processing technique that is automatically detect symptoms of the disease as early as possible. This is the first and important phase for automatic detection and classification of plant diseases. There are some stages to find the disease like image acquisition, preprocessing on image, color transform usingYCbCr, segmentation using Otsu method, feature extraction using Gabor filter method and classification using SVM, using those steps we can surely detect the disease and classified it and also can take preventive measures.
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The International Journal of Engineering and Science (The IJES)
1. The International Journal Of Engineering And Science (IJES)
||Volume|| 2 ||Issue|| 10 ||Pages|| 39-42 ||2013||
ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805
Pest Detection on Leaves Using Poission’s Thresholding
Techniques
Sitaram Longani1, Prof.V.V. Dixit2
1
M.E. (Communication Network) Student, Sinhgad College of engineering, Pune- 41.
Department of Electronics and Telecommunication, Sinhgad College of engineering, Pune- 41.
2
--------------------------------------------------------ABSTRACT-------------------------------------------------Different diseases and bioagressor are affecting the crops, due to which the plants cannot survive for a
long duration. Mainly the bioagressor which is been frequently seen on the leaves of the plant is the whitefly.
Our main aim is to count the number of whitefly on the leaves. Different techniques are used such as poission’s
minimum error thresholding, learning, layer labelling, to count the number of pest on the leaves. The automatic
counting of pests on the leaf helps in deciding the amount of pesticides to be sprayed on the leaf. This system is
useful not only to count the number of pest on its mature stage but also it can count the number of stages at its
middle stage and starting stage. We have compared our result with the manual method and its has been seen
that automatic counting gives the greater accuracy.
Keywords - whitefly, Digital image, Image segmentation, leaf, Thresholding technique
----------------------------------------------------------------------------------------------------------------------------- ---------Date of Submission: 11th, October, 2013
Date of Acceptance: 30th, October, 2013
----------------------------------------------------------------------------------------------------------------------------- ----------
I.
INTRODUCTION
Our India is an agriculture country, most of the people in india are farmer and the other depends on the agriculture.
Most of the scientists are doing research to increase the cultivity of crops. But one problem still exist which is a major
concern of the cultivation of crop and that is crop diseases. Due to these problems, the cultivation decreases and hence all the
farmers and in turn the country suffers the lack of cultivation of crop. Different types of pesticides are there in market which
are used to avoid the damage to fruit and vegetable, but the amount of pesticides to be used is not known due to which the
cost as well as the environmental pollution gets affected. Different methods are used to find the disease on the leaves; Cerco
Spora-leaf Spot is again one type of disease in which it affects older leaves of mature plants. The spots are round or irregular
in shape yellowish brown, with purple, dark brown or blackish borders and white centers affected leaves become pale in
colour and finally fall off [1]. Uptill now the naked eye observation was used to calculate the number of pest on the leaves
but the results were subjective and it was not possible to get the accurate results. Grid counting method was used to calculate
the severity of the disease leaf, which improved the accuracy but this method has cumbersome operation process and time
consuming [2]. To measure severity of Rust disease on Soybean, disease spot have segmented by Sobel operator to find out
spot edge and plant disease severity has measured by calculating the quotient of disease spot area and leaf area [3]. To
identify on line of pest damage in pip fruit in orchards they used a wavelet based image processing technique and neural
network [4]. Different number of disease are affecting the cotton leaves and the color of the leaves are also changed, also
there are different shapes of the holes on the leaves. So calculating the major and minor axis of the holes is the major task.
They extracted the feature using the self organizing feature map together with a back-propagation neural network to
recognize the color of image and by using these techniques they used to analyzed the image [1]. To detect and calculate
automatically the number of pest they propose cognitive vision system that combines image processing, learning and
knowledge based techniques. For automatic detection they used the digital remote sensor, then the object was identified. The
main challenge was to identify the object for which the different techniques were used [5]. To calculate the severity of attack
of herbivorous on the leaves the video digitizer is used. A Tekmatic system Video Van Gogh digitizer card was equipped
with an IBM PC or compatible microcomputer and the card can the card can be interfaced with standard video camera for
rapidly measuring the percentage of leaf area lost (LAL) to herbivorous insects [10]. Disease infection on fruits is also
important to maintain the quality of the fruit, to increase the final yield. Many researches have been conducted for the same
as - classification of grape fruit peel disease have been done using color texture feature sets through a discriminate function
with 2.3 % standard deviation [7]. A strong demand now exists in many countries for non-chemical control methods for
pests or diseases, and this issue have not been studied enough [9].
www.theijes.com
The IJES
Page 39
2. Pest Detection on Leaves Using Poission’s Thresholding Techniques
II.
Methodology
Fig. 1: block diagram
Image Acquisition:A leaf containing the bioagressor i.e the pests is taken from the environment and its image is stored in
computer in JPEG format. The pest leaf is kept on the white background and is zoomed so that the picture taken
contains the leaf part and the white background.
Fig. 2: Image captured
Fig. 3: Gray image
Poisson’s Minimum Error Thresholding:For the initial binarization, we first compute the image into gray and then normalized image histogram,
denoted h(i), where i denotes the intensity of a pixel in the range {0, Imax}[8]. Here we use Poisson-distribution
based on minimum error thresholding algorithm. The normalized image histogram for the mixture of Poisson
distributions is written as[8]
h(i ) = P0 × p(i│0) + P1 × p(i│1) …(1)
where P0 and P1 are the a priori probabilities of the background and foreground regions[8].
p(i|j), j = 0, 1 are Poisson distributions with means μj [8].
www.theijes.com
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Page 40
3. Pest Detection on Leaves Using Poission’s Thresholding Techniques
For a threshold t, the Poisson mixture parameters are given by[8]
t
t
P0(t) = ∑ h(i)
µ0(t) = 1/P0(t) ∑ i × h(i)
IMAX
P1(t) = ∑ h(i)
IMAX
µ1(t) = 1/P1(t) ∑ i × h(i) …(2)
t=0
i=t+1
t=0
i=t+1
The optimal threshold t* is chosen to minimize an error criterion, as follows:[8]
t* = arg min {µ - p0(t) (ln p0(t) + µ0(t) ln µ0(t)) – p1(t) (ln
p1(t) + µ1(t) ln µ1(t))}
where μ is the mean intensity of the complete image. The result of thresholding IN (x, y) using t* is refined by
incorporating spatial continuity constraints.[8]
Fig. 4: poission’s threshold image
Layer Labelling:-
Layer labelling is used to label the connected components, the MATLAB has the direct instruction for labelling the
object. L = bwlabeln(BW) is the instruction which returns a label matrix, L, containing labels for the connected components
in BW. The input image BW can have any dimension; L is the same size as BW. The elements of L are integer values greater
than or equal to 0. The pixels labeled 0 are the background. The pixels labeled 1 make up one object; the pixels labeled 2
make up a second object; and so on. Here the layer labelling is used to first label the background and the leaf part then
labeled the pest on the leaves.
Training:Training sets are created and they can be evaluated, deleted, renamed and merged with signature from
other files training sets evaluation and editing permit complex classification with training that are derived from
more than one training method (supervised and/or unsupervised ). The training set parameters includes
following attributes in additional to standard attribute training sets.
The number of bands in input image (as processed in program).
The minimum and maximum data file value in each band for each sample or cluster.
The mean data file value for each band for each sample or cluster.
The number of pixel in the sample or cluster [11].
The training is done to get the RGB component of the input image. The number of select point is set to
acquire the data set so that it can be used to match the data set acquired from processing the image.
Pixel intensity matching:-
The pixel intensity used at the training data set is matched with the
data set of the image which is processed. The Euclidean distance is used to match the image with the query image. The
Euclidean is the ordinary distance between two points that one would measure with a ruler and is given by Pythagorean
formula. Suppose there are two points p and q of the line segment then the Euclidean distance between two point is (
) in
Cartesian coordinates if p = (p1, p2,….,pn) and q = (q1, q2,…, qn) are two points in Euclidean n-space, then the distance from p
to q, or from q to p is given by:
d(p,q) = d(q,p) =
hence the equation used to calculate the Euclidean distance between the training image and the image used for processing,
the minimum distance achieved between the set of training image and processed image gives the disease part on the leaf.
www.theijes.com
The IJES
Page 41
4. Pest Detection on Leaves Using Poission’s Thresholding Techniques
III.
RESULTS
We have tested our system over the database which consist of 148 samples of images in which some of
the images were infected with the disease and other images were having the pest on it. The images were
captured when the leaves were infested with the whiteflies, which was found manily on the bottom of leaves. the
main aim is to count the number of pest on the leaves. On training the numerical values of the pest were learned
on the subset of 30 images for pests and 79 images for disease leaves. The image contain the pest on the leaf in
which atleast one pest is been detected on the leaf.
Table I: Result table for detecting atleast one pest.
Types of leaves
1(79)
2(67)
Whole set(148)
FNR%
0.0
7.46
3.42
FPR%
2.53
1.49
2.09
Table II: Result table for detecting number of correct pest.
Types of leaves
1(79)
2(67)
Whole set(148)
FNR%
0.0
25.3
11.8
FPR%
2.53
1.49
2.09
To make use of our result table, we have separated it into two classes c1 and c2 , where the first class c1 has the 79
images which does not has a whitefly on the leaves but there are some disease on the leaves and the second class c2 consist
of whitefly on its leaves. The FPR i.e., false positive rate means the images for which the number of detected whiteflies is
greater than the actual whiteflies on the leaf (rate of over detection) and the FNR i.e., false negative rate means the images
for which the number of detected whiteflies is less than the actual whiteflies on the leaf (rate of under detection) [5] Table 1
and Table 2 summarizes the detection results. The figures represent the mean values of FNR and FPR for class C1, C2, and
for the whole image test set. Table 1 shows the result of detecting atleast one pest on the leaves i.e. the FNR and FPR
calculated was to detect atleast one pest from the number of pest on the leaves. Table 2 shows the result of detecting the
number of correct pest i.e. to detect the actual number of pest available on the leaves. The FNR are roughly similar for the
two configurations. The two overlapping whitefly have been segmented into one region though there were two whitefly on
the leaf, hence the system count one whitefly instead of two whitefly.
IV.
CONCLUSION
Hence using different techniques such as poission’s minimum error thresholding which was used for segmentation,
pixel labelling, the third mode rejection and extraction of ROI and so on. Certain training for the color model is performed
which is used in the final part in classification, which uses linear Euclidean classifier for the purpose. There are some
disadvantage i.e., different other bioaggressor are not extracted from the leaves and secondly if the pests are overlapped it is
difficult to count automatically. This system will be helpful to get the accurate result and the time requirement for counting
the pest will also get reduced.
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[1]
[2]
[3]
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[5]
[6]
[7]
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Mr. V. A. Gulhane & Dr. A. A. Gurjar.” Detection of Diseases on Cotton Leaves and Its Possible Diagnosis, “International Journal
of Image Processing (IJIP), Volume (5) : Issue (5) : 2011
Sanjay B. Patil, Dr. Shrikant K. Bodhe,” LEAF DISEASE SEVERITY MEASUREMENT USING IMAGE
PROCESSING,”International Journal of Engineering and Technology Vol.3 (5), 2011, 297-301
Shen Weizhong, Wu Yachun [at.el], “Grading method of leaf spot disease based on image processing,’’ IEEE, 2008, pp.491494.
Brendon J. Woodford, Nikola K. Kasabov and C. Howard Wearing,” Fruit Image Analysis using Wavelets.
Paul Boissard, Vincent Martin, Sabine Moisan,” A cognitive vision approach to early pest detection in greenhouse crops”, UR880
Unit´e de recherches int´egr´ees, F-06903 Sophia Antipolis, France b INRIA, ORION Team, BP93, F-06902 Sophia Antipolis,
France Accepted 20 November 2007
Kridsakron Auynirndronkool, Varinthon Jarnkoon,”Analysis of economic crop reflectance by field spectral signature, case study
sugarcane”, Journal of plant physiol, 2008, pp.1-9.
Dae Gwan Kim, Thomas F. Burks,”Classification of grape fruit peel diseases using color texture feature analysis”, IJABE, 2009,
pp.41-50.
Yousef Al-Kofahi, Wiem Lassoued, William Lee, and Badrinath Roysam, Senior Member, “Improved Automatic Detection and
Segmentation of Cell Nuclei in Histopathology Images”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL.
57, NO. 4, APRIL 2010.
Hanafi, A., 2003. Integrated production and protection today and in the future in greenhouse crops in the Mediterranean region.
Acta Horticulturae (ISHS) 614, 755–765.
William W. Hargrove, D. A. Crossley, “Video Digitiser for the rapid measurement of leaf area lost due to Herbivorous insecct”
Journal of Entomological Society of America, 1998,pp.591-598.
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