International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Detection of Diseases on Cotton Leaves and its Possible DiagnosisCSCJournals
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In a research of identifying and diagnosing cotton disease, the pattern of disease is important part in that, various features of the images are extracted viz. the color of actual infected image, there are so many diseases occurred on the cotton leaf so the leaf color for different diseases is also different, also there are various other features related to shape of image, also there are different shape of holes are present on the leaf of the image, generally the leaf of infected image have elliptical shape of holes, so calculating the major and minor axis is the major task . The features could be extracted using self organizing feature map together with a back-propagation neural network is used to recognize color of image. This information is used to segment cotton leaf pixels within the image, now image which is under consideration is well analyzed and depending upon this software perform further analysis based on the nature of this image.
528Seed Technological Development – A Surveyidescitation
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This paper provides a review of automating or semi-automating the seed quality
purity test. Computer vision (CV) technology used in variety of industries is a sophisticated
type of inspection technology; however, it is not widely used in agriculture.The application
of CV technologies is very challenging in agriculture. As CV plays an important role in this
domain, research in this area has been motivated. Several theories of automating seed
quality purity test are briefly mentioned. The reviewed approaches are classified according
to features and classifiers. The methods for extracting features of a particular seed, and the
classifiers used for classifying the seeds, are mentioned in the paper. An overview of the
most representative methods for feature extraction and classification of seeds is presented.
The major goal of the paper is to provide a comprehensive reference source for the
researchers involved in automation of seed classification, regardless of particular feature or
classifier.
Detection of Diseases on Cotton Leaves and its Possible DiagnosisCSCJournals
Â
In a research of identifying and diagnosing cotton disease, the pattern of disease is important part in that, various features of the images are extracted viz. the color of actual infected image, there are so many diseases occurred on the cotton leaf so the leaf color for different diseases is also different, also there are various other features related to shape of image, also there are different shape of holes are present on the leaf of the image, generally the leaf of infected image have elliptical shape of holes, so calculating the major and minor axis is the major task . The features could be extracted using self organizing feature map together with a back-propagation neural network is used to recognize color of image. This information is used to segment cotton leaf pixels within the image, now image which is under consideration is well analyzed and depending upon this software perform further analysis based on the nature of this image.
528Seed Technological Development – A Surveyidescitation
Â
This paper provides a review of automating or semi-automating the seed quality
purity test. Computer vision (CV) technology used in variety of industries is a sophisticated
type of inspection technology; however, it is not widely used in agriculture.The application
of CV technologies is very challenging in agriculture. As CV plays an important role in this
domain, research in this area has been motivated. Several theories of automating seed
quality purity test are briefly mentioned. The reviewed approaches are classified according
to features and classifiers. The methods for extracting features of a particular seed, and the
classifiers used for classifying the seeds, are mentioned in the paper. An overview of the
most representative methods for feature extraction and classification of seeds is presented.
The major goal of the paper is to provide a comprehensive reference source for the
researchers involved in automation of seed classification, regardless of particular feature or
classifier.
Feature extraction of Jabon (Anthocephalus sp) leaf disease using discrete wa...TELKOMNIKA JOURNAL
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Jabon (Anthocephalus cadamba (Roxb.) Miq) is one type of forest plants that have very rapid growth until the process of the harvest. One inhibitor is a disease that attacks the leaves in the form of spots and blight that can cause death during the growth process of this tree. The purpose of this process is to detect the object of diseases that attack the leaves of jabon at the time in the nursery. Images of affected jabon leaf disease segmented by reducing the RGB color cylinders to separate the disease object from the background. Reduced channel G-R provides information in the form of disease areas contained in the image of Jabon leaf. Furthermore, the characteristics of leaf disease can be detected well using DWT in the 3-level decomposition process with SVM classification results that can separate both classes of spots and blight by 84.672%.
Root cutting retards scion sprouting and subsequent growth of rubber buddingsAI Publications
Â
Rubber ground nursery is one of rubber seedling propagation. However, there is still a lack of systematic studies investigating root cutting effect on rubber budding nursery. In this study, sprouting rate, scion stem diameter and plant height, and root stock diameter were observed between non-in situ and in situ buddings over five periods. The results showed that root cutting destroyed rootstock, retarded rubber scion sprouting rate and influenced subsequent growth such as plant height, scion diameter, leaf width and leaf length-width ratio.
Identification of paddy leaf diseases based on texture analysis of Blobs and ...TELKOMNIKA JOURNAL
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There are three types of paddy leaf diseases that have similar symptoms, making it difficult for farmers to identify them, namely blast, brown-spot, and narrow brown-spot. This study aims to identification paddy plant diseases based on texture analysis of Blobs and color segmentation. Blobs analysis is used to get the number of objects, area and perimeter. Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
Â
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.
Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
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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
A study on real time plant disease diagonsis systemIJARIIT
Â
We aim to develop a real time application to the farmers for managing crop diseases. However, disease detection requires
continuous monitoring of experts which might be prohibitively expensive in large farms area. Automatic detection of plant diseases
is an essential research topic as it may prove benefits in monitoring large fields of crops and thus automatically detect the symptoms
of diseases as soon as they appear on plant leaves. Regarding plant disease diagnosis methodologies to detect diseases on crops,
image processing in disease diagnosis and eAGROBOT was studied. This paper is aiming to all are collectively used and formed
semi real time system for a disease diagnosis which uses image processing and data mining concepts to give pesticide
recommendation and pesticide cost estimation system. Thus the android application makes a good foundation for following effective
characteristic parameters for the disease diagnoses and setting up recommender system. The system is to be designed and developed
using Android studio as front-end software and SQLite as back-end software. The pictures and remedial measures of the diseases
were stored in the database and can be retrieved whenever necessary. The challenge is to make the farmers listen to the crop disease
diagnosis system and to get the advice related to the crop diseases. The constraint here is to develop the expert in local languages so
that farmers can operate the ES by themselves and get expert advice from the system.
Image based anthracnose and red-rust leaf disease detection using deep learningTELKOMNIKA JOURNAL
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Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease’s detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from “ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101” show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease’s detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
Bacterial foraging optimization based adaptive neuro fuzzy inference system IJECEIAES
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Life of human being and animals depend on the environment which is surrounded by plants. Like human beings, plants also suffer from lot of diseases. Plant gets affected by completely including leaf, stem, root, fruit and flower; this affects the normal growth of the plant. Manual identification and diagnosis of plant diseases is very difficult. This method is costly as well as time-consuming so it is inefficient to be highly specific. Plant pathology deals with the progress in developing classification of plant diseases and their identification. This work clarifies the identification of plant diseases using leaf images caused by bacteria, viruses and fungus. By this method it can be identified and control the diseases. To identify the plant leaf disease Adaptive Neuro Fuzzy Inference System (ANFIS) was proposed. The proposed method shows more refined results than the existing works.
Feature extraction of Jabon (Anthocephalus sp) leaf disease using discrete wa...TELKOMNIKA JOURNAL
Â
Jabon (Anthocephalus cadamba (Roxb.) Miq) is one type of forest plants that have very rapid growth until the process of the harvest. One inhibitor is a disease that attacks the leaves in the form of spots and blight that can cause death during the growth process of this tree. The purpose of this process is to detect the object of diseases that attack the leaves of jabon at the time in the nursery. Images of affected jabon leaf disease segmented by reducing the RGB color cylinders to separate the disease object from the background. Reduced channel G-R provides information in the form of disease areas contained in the image of Jabon leaf. Furthermore, the characteristics of leaf disease can be detected well using DWT in the 3-level decomposition process with SVM classification results that can separate both classes of spots and blight by 84.672%.
Root cutting retards scion sprouting and subsequent growth of rubber buddingsAI Publications
Â
Rubber ground nursery is one of rubber seedling propagation. However, there is still a lack of systematic studies investigating root cutting effect on rubber budding nursery. In this study, sprouting rate, scion stem diameter and plant height, and root stock diameter were observed between non-in situ and in situ buddings over five periods. The results showed that root cutting destroyed rootstock, retarded rubber scion sprouting rate and influenced subsequent growth such as plant height, scion diameter, leaf width and leaf length-width ratio.
Identification of paddy leaf diseases based on texture analysis of Blobs and ...TELKOMNIKA JOURNAL
Â
There are three types of paddy leaf diseases that have similar symptoms, making it difficult for farmers to identify them, namely blast, brown-spot, and narrow brown-spot. This study aims to identification paddy plant diseases based on texture analysis of Blobs and color segmentation. Blobs analysis is used to get the number of objects, area and perimeter. Color segmentation is used to find out some color parameters of paddy leaf disease such as the color of the lesion boundary, the color of the spot of the lesion, and the color of the paddy leaf lesion. To get the best results, four methods have been chosen to obtained the threshold value, Otsu threshold value, variable threshold value, local threshold value and global threshold value. The best accuracy of the four methods using threshold variables is 90.7%. The results of this study indicate that the method used has been very satisfactory in identifying paddy plant disease.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
Â
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.
Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
Â
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
A study on real time plant disease diagonsis systemIJARIIT
Â
We aim to develop a real time application to the farmers for managing crop diseases. However, disease detection requires
continuous monitoring of experts which might be prohibitively expensive in large farms area. Automatic detection of plant diseases
is an essential research topic as it may prove benefits in monitoring large fields of crops and thus automatically detect the symptoms
of diseases as soon as they appear on plant leaves. Regarding plant disease diagnosis methodologies to detect diseases on crops,
image processing in disease diagnosis and eAGROBOT was studied. This paper is aiming to all are collectively used and formed
semi real time system for a disease diagnosis which uses image processing and data mining concepts to give pesticide
recommendation and pesticide cost estimation system. Thus the android application makes a good foundation for following effective
characteristic parameters for the disease diagnoses and setting up recommender system. The system is to be designed and developed
using Android studio as front-end software and SQLite as back-end software. The pictures and remedial measures of the diseases
were stored in the database and can be retrieved whenever necessary. The challenge is to make the farmers listen to the crop disease
diagnosis system and to get the advice related to the crop diseases. The constraint here is to develop the expert in local languages so
that farmers can operate the ES by themselves and get expert advice from the system.
Image based anthracnose and red-rust leaf disease detection using deep learningTELKOMNIKA JOURNAL
Â
Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease’s detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from “ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101” show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease’s detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
Bacterial foraging optimization based adaptive neuro fuzzy inference system IJECEIAES
Â
Life of human being and animals depend on the environment which is surrounded by plants. Like human beings, plants also suffer from lot of diseases. Plant gets affected by completely including leaf, stem, root, fruit and flower; this affects the normal growth of the plant. Manual identification and diagnosis of plant diseases is very difficult. This method is costly as well as time-consuming so it is inefficient to be highly specific. Plant pathology deals with the progress in developing classification of plant diseases and their identification. This work clarifies the identification of plant diseases using leaf images caused by bacteria, viruses and fungus. By this method it can be identified and control the diseases. To identify the plant leaf disease Adaptive Neuro Fuzzy Inference System (ANFIS) was proposed. The proposed method shows more refined results than the existing works.
Food is one of the basic needs of human being. Population is increasing day by day. So, it has become important to grow sufficient amount of crops to feed such a huge population. Agricultural intervention in the livelihood of rural India is about 58%. But with the time passing by, plants are being affected with many kinds of diseases, which cause great harm to the agricultural plant productions. It is very difficult to monitor the plant diseases. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing speed and time. Hence, image processing is used for the detection of plant diseases by just capturing the images of the leaves and comparing it with the data sets available. Latest and fostering technologies like Image processing is used to rectify such issues very effectively. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. This paper aims to support and help the farmers in an efficient way.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
Â
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
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A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
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Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
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1. Ms. Rupali S.Zambre et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 1), May 2014, pp.92-97
www.ijera.com 92 | P a g e
Classification of Cotton Leaf Spot Disease Using Support Vector
Machine
Prof. Sonal P. Patil1
, Ms. Rupali S.Zambre2
1
Assistant Professor, Computer Science, GHRIEM, Jalgaon, Maharashtra, India
2
Research Scholar, Computer Science & Engg, GHRIEM, Jalgaon, Maharashtra, India
Abstract
In order to obtain more value added products, a product quality control is essentially required Many studies
show that quality of agriculture products may be reduced from many causes. One of the most important factors
of such quality plant diseases. Consequently, minimizing plant diseases allows substantially improving quality
of the product Suitable diagnosis of crop disease in the field is very critical for the increased production. Foliar
is the major important fungal disease of cotton and occurs in all growing Indian cotton regions. In this paper I
express Technological Strategies uses mobile captured symptoms of Cotton Leaf Spot images and categorize the
diseases using support vector machine. The classifier is being trained to achieve intelligent farming, including
early detection of disease in the groves, selective fungicide application, etc. This proposed work is based on
Segmentation techniques in which, the captured images are processed for enrichment first. Then texture and
color Feature extraction techniques are used to extract features such as boundary, shape, color and texture for
the disease spots to recognize diseases.
Key Words: Feature extraction, segmentation, Support vector Machine (SVM)
I. INTRODUCTION
India is known as agricultural country where
large percentage of the population depends on
agriculture. The farming of different crops for
optimum yield and quality product is highly
important. It can be improved with the help of
technological support. India is the important cotton
growing country. Many states in India grows cotton.
Cotton is also known as" The White Gold" or the
"emperor Fibers" enjoys a most excellent status
among all cash crops in the country. It provides
livelihood to about sixty million people and is an
important agricultural commodity providing
remunerative income to millions of farmers both in
developed and developing countries. The world
textile industries require cotton. India thus enjoys
the distinction of being the earliest country in the
world to domesticate cotton and utilize its fibre to
manufacture fabric. The main cause for the disease is
the leaf of the cotton plant. About 80 % of disease on
the cotton plant is on its leaves. Therefore our study
of interest is the leaf of the cotton tree rather than
whole cotton plant the cotton leaf is mainly suffered
from diseases like fungus, Foliar leaf spot of cotton,.
The machine vision system now a day is normally
consists of computer, digital camera and application
software.
Image recognition has attracted many
researchers in the area of pattern recognition, similar
flow of ideas are applied to the field of pattern
recognition of plant leaves, that is used in diagnosing
the cotton leaf diseases. We know that observation of
the human eye is not so strong that he can the
infected part of image because that minute variation
pattern of color can be a different disease present on
the leaf of cotton. Our software can provide the
exactly differentiate the difference of color present on
these leaves and depending upon that difference the
further compare with database stored image features
related to the color
1.1 The Image analysis in agriculture
The Image analysis techniques are
extensively applied to agricultural science, and it has
great use especially in the plant protection field.
Image analysis can be applied for the following
purposes:
1. To detect diseased leaf, stem, fruit
2. To quantify affected area by disease.
3. To find the boundaries of the affected area.
4. To determine the color of the affected area
5. To determine size & shape of fruits.
6. To identify the Object correctly. [2]
1.2 Types of cotton leaf spot diseases
The various diseases recognize on the cotton
leaf spots are classified as
ď‚· Grey mildew
ď‚· Bacterial blight
ď‚· Leaf curl
ď‚· Fusarium wilt
ď‚· Verticillium wilt
RESEARCH ARTICLE OPEN ACCESS
2. Ms. Rupali S.Zambre et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 1), May 2014, pp.92-97
www.ijera.com 93 | P a g e
ď‚· Alternaria Leaf Spot-alternaria Macro Spora
1.3 Symptoms of Cotton Diseases
1.3.1 Foliar leaf spot on cotton
As shown in above figures the, the disease
is known as foliar disease arises due to potassium
deficiency [2], [3], [4].
Fig -1: Foliar leaf spot on cotton
Fig -2: Foliar leaf spot on cotton
The early stage of this disease is as shown in
fig 1, now if the more spots of this disease results
into the final stage of this plant where the plant leaf is
get fall so it is called as Foliar disease of the cotton
plant as shown in fig 2. The leaf is having multiple
no of spots which clearly denotes more potassium
deficiency in the plant. [3]
1.3.2 Bacterial Blight
Xanthomonas campestis PV. Malvacearum
Bacterial blight starts out as Dark green, water
soaked angular leaf spot of 1 to 5 mm across the
leaves and bracts, especially on the under surface of
leaves with a red to brown border
Fig -3: Bacterial Blight
The angular appearance is due to restriction
of the lesion by fine veins of the cotton leaf. Spots on
infected leaves may spread along the major leaf veins
as disease progresses, leaf petioles as shown in Fig.
The angular leaf spot, results in premature defoliation
and stems may become infected resulting in
premature defoliation. [2][3]
1.3.3 Alternaria Leaf Spot-alternaria Macro
Spora
Fig -4: Alternaria Leaf Spot-alternaria Macro
Spora
This causes small, gray, pale to brown,
round or irregular spots measuring 0.5 - 3 mm in
diameter and cracked centers appears on the affected
leaves of the plant. Affected leaves become dry and
fall off [2][3]
1.3.4 Grey mildew
This disease primarily appears on older
leaves as the plants reach middle age, in the form of
irregularly angular, pale spots, usually 3-4 mm in
diameter and The lesions are light to yellowish green
on the upper surface. As the spots grow older, the
leaf tissues turn yellowish brown while a whitish
frosty growth appears chiefly on the under surface
but occasionally also on the upper surface. [1][2][3]
1.3.5 Cerco Spora-leaf Spot Cerco Spora
Fig -5: Cerco Spora-leaf Spot
The disease 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 color and finally fall off [3]
II. LITERATURE SURVEY
The fuzzy feature selection approach fuzzy
curves (FC) and surfaces (FS) - is proposed to select
features of cotton disease leaves image. In order to
get best information for diagnosing and identifying, a
subset of independent significant features is
identified exploiting the fuzzy feature selection
approach. [6] Detection and classification of rice
diseases, including RBLB, RSB and RB. Rice disease
spots were segmented efficiently according to color
and outline of disease spots. Four shape features
(rectangularity, compactness, elongation and
roundness) and 60 texture features (contrast,
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uniformity, entropy, inverse difference and linearity
correlation) of disease spot were extracted [9]
The objective of this paper is to concentrate
on the plant leaf disease detection based on the
texture of the leaf. Leaf presents several advantages
over flowers and fruits at all seasons worldwide [2]
Homogenize techniques like sobel and canny filter
has been used to identify the edges by P.Revathi et
al. [3]. These extracted edge features have been used
in classification to identify the disease spots. The
proposed homogeneous pixel counting technique for
cotton diseases detection (HPCCDD) algorithm has
been used for categorizing the diseases. They claim
the accuracy of 98.1% over existing algorithm. [2]
Dheeb Al Bashish, et al. [4] developed neural
network classifier based on statistical classification
and could successfully detect and classify the
diseases with a precision of around 93%.
Menukaewjinda et al. [14] tried another ANN, i.e.
back propagation neural network (BPNN) for
efficient grape leaf color extraction with complex
background. They also explore modified self
organizing feature map (MSOFM) and genetic
algorithm (GA) and found that these techniques
provide automatic adjustment in parameters for grape
leaf disease color extraction. Support vector machine
(SVM) has been also found to be very promising to
achieve efficient classification of leaf diseases. The
major techniques for detection of plant diseases are:
BPNN, SVM, K-means clustering, and SGDM.
These techniques are used to analyses the healthy and
diseased plants leaves. Some of the challenges in
these techniques viz. effect of background data in the
resulting image, optimization of the technique for a
specific plant leaf diseases, and automation of the
technique for continuous automated monitoring of
plant leaf diseases under real world field conditions.
The review suggests that this disease detection
technique shows a good potential with an ability to
detect plant leaf diseases and some limitations. [10]
WEB-based Intelligent Diagnosis System for Cotton
Diseases Control has been developed in BP neural
network as a decision-making system to establish an
intelligent diagnosis model[16]. In [12] paper, Zhang
Jian applied support vector machine method to the
identification of cucumber diseases. They carried out
two sets of tests using different kernel functions. The
results showed that, the SVM method based on the
RBF kernel function when they took each spot as a
sample made the best performance for classification
of diseases of cucumber Presented work carried out
CMYK based image cleaning technique to remove
shadows, hands and other impurities from images.
The images are subsequently classified using two
indigenous techniques RPM and Dis Bin and
compared with the classical PCA based technique.
[15]
III. METHODOLOGY
The methodology for diagnosing cotton leaf
spot diseases involves several tasks, such as Image
acquisition, image enhancement, segmentation, shape
feature extraction and color based segmentation, and
cotton leaf diseases classification based on lesion
percentage, lesion type, boundary color, spot color,
and cotton leaf color.The first phase is the image
acquisition phase. In this step, the images of the
various leaves that are to be classifies are taken using
a digital camera. In the second phase image
preprocessing is completed. In the third phase,
segmentation is performed to discover the actual
segments of the leaf in the image. Later on, feature
extraction for the infected part of the leaf is
completed based on specific properties among pixels
in the image or their texture. After this step, certain
statistical analysis tasks are completed to choose the
best features that represent the given image, thus
minimizing feature redundancy. Finally,
classification is completed using support vector
machine.
Fig -6: Stages for classification of cotton leaf spot
diseases
3.1 Image Acquisition
The RGB color images of cotton leaf are
captured using a digital camera. Images are stored in
BMP format.
33.2. Image Pre-Processing and Segmentation
The pre-processing task involves some
procedures to prepare the images enhancement.
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Cotton leaf image is in RGB color format. The RGB
image is converted to a grayscale image, next, the
image segmentation based on gray-level threshold
segmentation is adapted and the binary image is
gained. This research applied global threshold,
variable threshold and Otsu method for an automatic
threshold. Camera flash can act as noise and affects
the image quality. Hence, median filter and
morphological operators are applied to remove
unnecessary spots
3.3 Feature Extraction
When crops suffer from many diseases,
batches (spots) often happen on leaves. Leaf spots are
considered the important units indicating the
existence disease and regarded as indicator of crops
disease. In order to classify disease leaf samples
category, a set of spot features for Classification and
detection of the different disease leaves are
investigated. Spot features are extracted from image
using the appropriate image processing method.
These features are very important for the color and
morphology of the leaf spots and they provide critical
information about its visual representation. The
features correspond to color characteristics are the
mean and variance of the gray level of the red, green
and blue channel of the spots; and other features
correspond to morphological and geometrical
characteristics of the spots. By using segmentation
technique it is easy for us to extract the features of
disease leaf of the image.
The image analysis here focuses on the shape feature
extraction and color based segmentation.
3.3.1 Shape Feature Extraction
General descriptors such as number of the
object, area of the shape object, width and length of
the object, and area of image, are important
characteristics to describe its shape. Those
characteristics are used to extract feature the lesion,
spot and percentage of the lesion. Blob Analysis is
used in this research to calculate statistics for labeled
regions in a noise free binary image, such as the
number of the object, area, and perimeter.
3.3.2 Color Feature Extraction
Color is an important sign in recognizing
different classes. The pixel in a color image is
commonly represented in the RGB space, in which
the color at each pixel is represented as a triplet (R,
G, B), where R, G and B are respectively the red,
green, and blue value from a color image capturing
device. Other color spaces like the HSI and CIE color
model are also used in many other segmentation
methods where their benefits and limitations are
analyzed and reported [13]. Generally, the color
difference is evaluated using the distance between
two color points in a color space. The most common
distance is Euclidean distance. Our planned technique
is based on the CIELab color space, which is a
uniform chromaticity color space to get boundary
color, spot color and broken leaf color. It is known
that Euclidean distance of two colors is proportional
to the difference that human visual system perceived
in the CIELab color space [14]. Generally the image
is composed of RGB color components. Thus, we
must convert RGB color components to CIELab
color component.
3.4 Statistical Analysis
Statistical analysis tasks are completed to
choose the best features that represent the given
image, thus minimizing feature redundancy. [4]
3.5 Classification
The Concept of SVM (Support Vector
Machine) was introduced by Vapnik and co-workers.
It gains popularity because it offers the attractive
features and powerful machinery to tackle the
problem of classification i.e., we need to know which
belongs to which group and promising empirical
performance. The SVM is based on statistical
learning theory. SVM’s better generalization
performance is based on the principle of Structural
Risk Minimization (SRM) .The concept of SRM is to
maximize the margin of class separation. The SVM
was defined for two-class problem and it looked for
optimal hyper-plane, which maximized the distance,
the margin, between the nearest examples of both
classes, named SVM.
At present SVM is popular classification
tool used for pattern recognition and other
classification purposes. Support vector machines
(SVM) are a group of supervised learning methods
that can be applied to classification or regression. The
standard SVM classifier takes the set of input data
and predicts to classify them in one of the only two
distinct classes. SVM classifier is trained by a given
set of training data and a model is prepared to
classify test data based upon this model. For
multiclass classification problem, we decompose
multiclass problem into multiple binary class
problems, and we design suitable combined multiple
binary SVM classifiers [17].
Most traditional classification models are
based on the empirical risk minimization principle.
SVM implements the structural risk minimization
principle which seeks to minimize the training error
and a confidence interval term. A number of
applications showed that SVM hold the better
classification ability in dealing with small sample,
nonlinearity and high dimensionality pattern
recognition. Support Vector Machines are based on
the concept of decision planes that define decision
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www.ijera.com 96 | P a g e
boundaries. A decision plane is one that separates
between a set of objects having different class
memberships. The classifier that separates a set of
objects into their respective classes with a line. Most
classification tasks, however, are not that simple, and
often more complex structures are needed in order to
make an optimal separation, i.e., correctly classify
new objects (test cases) on the basis of the examples
that are available (train cases).
All the information from above processes is
given to multiclass SVM .The Multiclass SVM is
used for cotton leaf spot disease classification. The
cotton leaf color is segmented corresponding to
number of weight vectors. Information from
segmented images both diseased and non-diseased
pixels are used for training in support vector machine
for cotton leaf disease segmentation.
IV. PERFORMANCE ANALYSIS
Researchers have investigated for different
feature extraction technique and different classifier.
Some analyses from different research paper are
listed below.
Table -1: Performance analysis
Ref
no
Purpose/Feature
Extraction
Class
ifier
Accura
cy(%)
1 Canny edge
detection,
color feature
extraction
HPC
CDD
98.1
16 WEB-based
Intelligent Diagnosis
System for Cotton
Diseases Control
BPN
N
90
5 fuzzy feature
selection approach-
for fuzzy curves (Fe)
and surfaces (FS)
BPN
N
90
15 CMYK based image
cleaning technique-
to remove shadows,
impurities
RPM
,Dis
Bin,
PCA
83
17 Wavelet transform SVM 97
18 RGB color feature
extraction model
NN 75.9
3 Image enhancement-
anisotropic- diffusion
technique Color
image segmentation -
unsupervised SOFM
network
ANN 85-91
9 Texture feature
extraction-GLCM
SVM 97.2
V. CONCLUSIONS
This work consists of identifying the
affected part of the disease. Cotton leaf spot disease
spots were segmented efficiently according to color
and outline of disease spots. Initially Image
segmentation is done, and finally image analysis and
important features are extracted and classification of
diseases is performed using SVM classifier.
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