This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
Agriculture is the backbone of human sustenance on this world. Now a days with growing population we need the productivity of the agriculture to be increased a lot to meet the demands. In olden days they used natural methods to increase the productivity, such as using the cow dung as a fertilizer in the fields. That resulted increase in the productivity enough to meet the requirements of the population. But later people started thinking of earning more profits by getting more outcome. So, there came a revolution called “Green Revolution”. In this paper we implemented image processing using MATLAB to detect the weed areas in an image we took from the fields.
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.
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.
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
Agriculture is the backbone of human sustenance on this world. Now a days with growing population we need the productivity of the agriculture to be increased a lot to meet the demands. In olden days they used natural methods to increase the productivity, such as using the cow dung as a fertilizer in the fields. That resulted increase in the productivity enough to meet the requirements of the population. But later people started thinking of earning more profits by getting more outcome. So, there came a revolution called “Green Revolution”. In this paper we implemented image processing using MATLAB to detect the weed areas in an image we took from the fields.
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.
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.
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
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.
Analysis And Detection of Infected Fruit Part Using Improved k-means Clusteri...IJSRD
Drastic increase in the overseas commerce has increased nowadays .Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually become time consuming process. The combined study of image processing and clustering technique gave a turning point to the defect defection in fruits. This paper gives a solution for defect detection and classification of fruits using improved K-means clustering algorithm. Based on their color pixels are clustered. Then the merging takes place to a specific no of regions. Although defect segmentation is not depend on the color, it causes to produce different power to different regions of image. We have taken some of the fruits for the experimental results to clarify the proposed approach to improve the analysis and detection of fruit quality to minimize the precious and computational time. The proposed system is effective due to result obtained.
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 .
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
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
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.
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
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
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.
Automated Crop Inspection and Pest Control Using Image ProcessingIJERDJOURNAL
ABSTRACT: Agriculture is the backbone of our country. India is an agricultural country where the most of the population depends on agriculture. Research in agriculture is aimed towards increasing productivity and profit. There are several automated systems available in literature, which are developed for irrigation control and environmental monitoring in the field. However, it is essential to monitor the plant growth stage by stage and take decisions accordingly. In addition to monitoring the environmental parameters such as pH, moisture content and temperature, it is inevitable to identify the onset of plant diseases too. It is the key to prevent the losses in yield and quantity of agricultural product. Plant disease identification by continuous visual monitoring is very difficult task to farmers and at the same time it is less accurate and can be done in limited areas. Hence this projects aims at developing an image processing algorithm to identify the diseases in rice plant. Rice blast disease occurring in rice plant is due to magnaporthe grisea and this disease also occurs in wheat, rye, barley, pearl and millet. Due to rice blast disease, 60 million people are affected in 85 countries worldwide. Image processing technique is adopted as it is more accurate. Early disease detection can increase the crop production by inducing proper pesticide usage.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
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.
Analysis And Detection of Infected Fruit Part Using Improved k-means Clusteri...IJSRD
Drastic increase in the overseas commerce has increased nowadays .Modern food industries work on the quality and safety of the products. Fruits such as oranges and apple are imported and exported on large scale. Identifying the defect manually become time consuming process. The combined study of image processing and clustering technique gave a turning point to the defect defection in fruits. This paper gives a solution for defect detection and classification of fruits using improved K-means clustering algorithm. Based on their color pixels are clustered. Then the merging takes place to a specific no of regions. Although defect segmentation is not depend on the color, it causes to produce different power to different regions of image. We have taken some of the fruits for the experimental results to clarify the proposed approach to improve the analysis and detection of fruit quality to minimize the precious and computational time. The proposed system is effective due to result obtained.
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 .
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
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
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.
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
A STUDY ON WEED DISCRIMINATION THROUGH WAVELET TRANSFORM, TEXTURE FEATURE EXT...ijcsit
Texture based weed classification has played an important role in agricultural applications. In the recent years weed classification based on wavelet transform is an effective method. But the feature extraction is main issue for proper classification of weed species. In this paper, the issue of statistical and texture
classification based on wavelet transform has been analysed. The efficient texture feature extraction
methods are developed for weed discrimination. Three group feature vector can be constructed by the mean
and standard deviation of the wavelet statistical features (WSF), Texture feature as Contrast, Cluster
Shade, Cluster Prominence and Local Homogeneity (WCSPH) and Energy, Correlation, Cluster Shade,
Cluster Prominence and Entropy features (WECSPE) which are derived from the sub-bands of the wavelet
decomposition and are used for classification. Experimental results show that Rbio33 Wavelet with
WECSPE texture feature obtaining high degree of success rate in classification.
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.
Automated Crop Inspection and Pest Control Using Image ProcessingIJERDJOURNAL
ABSTRACT: Agriculture is the backbone of our country. India is an agricultural country where the most of the population depends on agriculture. Research in agriculture is aimed towards increasing productivity and profit. There are several automated systems available in literature, which are developed for irrigation control and environmental monitoring in the field. However, it is essential to monitor the plant growth stage by stage and take decisions accordingly. In addition to monitoring the environmental parameters such as pH, moisture content and temperature, it is inevitable to identify the onset of plant diseases too. It is the key to prevent the losses in yield and quantity of agricultural product. Plant disease identification by continuous visual monitoring is very difficult task to farmers and at the same time it is less accurate and can be done in limited areas. Hence this projects aims at developing an image processing algorithm to identify the diseases in rice plant. Rice blast disease occurring in rice plant is due to magnaporthe grisea and this disease also occurs in wheat, rye, barley, pearl and millet. Due to rice blast disease, 60 million people are affected in 85 countries worldwide. Image processing technique is adopted as it is more accurate. Early disease detection can increase the crop production by inducing proper pesticide usage.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES ...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem growth from birth to maturity level of selected plant using image processing technique. Few reasons have been observed commonly by the researchers that some plants could not grow sufficiently as to the other plants of similar breed and age. Images were taken of different stage of bean plant and images of some other plant samples were also included for better assessment. Researchers are trying to understand through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable comparison technique is applied to detect their period of growth. Image processing methods like Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects that are not supporting to grow the plants of their similar age group are matter to calculate scientifically later in the future. The observation would help for further support in medicinal science or agricultural science of plant and Bio-chemical.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
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Rice seed image classification based on HOG descriptor with missing values im...TELKOMNIKA JOURNAL
Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach.
Textural Feature Extraction of Natural Objects for Image ClassificationCSCJournals
The field of digital image processing has been growing in scope in the recent years. A digital image is represented as a two-dimensional array of pixels, where each pixel has the intensity and location information. Analysis of digital images involves extraction of meaningful information from them, based on certain requirements. Digital Image Analysis requires the extraction of features, transforms the data in the high-dimensional space to a space of fewer dimensions. Feature vectors are n-dimensional vectors of numerical features used to represent an object. We have used Haralick features to classify various images using different classification algorithms like Support Vector Machines (SVM), Logistic Classifier, Random Forests Multi Layer Perception and Naïve Bayes Classifier. Then we used cross validation to assess how well a classifier works for a generalized data set, as compared to the classifications obtained during training.
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
Application of informative textural Law’s masks methods for processing space...IJECEIAES
Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law’s textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law’s textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated.
Extraction of spots in dna microarrays using genetic algorithmsipij
DNA microarray technology is an eminent tool for genomic studies. Accurate extraction of spots is a
crucial issue since biological interpretations depend on it. The image analysis starts with the formation of
grid, which is a laborious process requiring human intervention. This paper presents a method for optimal
search of the spots using genetic algorithm without formation of grid. The information of every spot is
extracted by obtaining a pixel belonging to that spot. The method developed selects pixels of high intensity
in the image, thereby spot is recognized. The objective function, which is implemented, helps in identifying
the exact pixel. The algorithm is applied to different sizes of sub images and features of the spots are
obtained. It is found that there is a tradeoff between accuracy in the number of spots identified and time
required for processing the image. Segmentation process is independent of shape, size and location of the
spots. Background estimation is one step process as both foreground and complete spot are realized.
Coding of the proposed algorithm is developed in MATLAB-7 and applied to cDNA microarray images.
This approach provides reliable results for identification of even low intensity spots and elimination of
spurious spots.
Extraction of spots in dna microarrays using genetic algorithm
La2418611866
1. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-August 2012, pp.1861-1866
Infection Analysis Using Colour Feature Texture Using Image
Processing
Ravikant Sinha1
Department of E&TC Pune
University, SCOE sudumbare
Maharashtra, India
Pragya Pandey2
Department of E&TC Pune
University, SCOE sudumbare
Maharashtra, India
ABSTRACT
In this study, a new approach is used to Which India produces approximately 50% in the
automatically detect the infected pomegranates. In states of Maharashtra and Andhra Pradesh. Iran is
the development of automatic grading and sorting the second largest, producing around 35% of global
system for pomegranate, critical part is detection production. Spain produces around 2.5% and the
of infection. Color texture feature analysis is used USA has around 10,000ha under production.
for detection of surface defects on pomegranates. This study centres at developing a method to detect
Acquired image is initially cropped and then infected pomegranate using color texture features.
transformed into HSI color space, which is further The various steps involved for development of
used for generating SGDM matrix. Total 18 database of features are summarized in figure 1
texture features were computed for hue (H), below:
saturation (S) and intensity (I) images from each
cropped samples. Best features were used as an
input to Support Vector Machine (SVM) classifier INPUT IMAGE
and tests were performed to identify best
classification model. Out of selected texture
CROPPING WITH SIZE OF 16X16
features, features showing optimal results were
cluster shade (99.8835%), product moment
(99.8835%) and mean intensity (99.8059%).
CONVERTING CROPPED IMAGES
Keywords:-Pomegranate, disease detection, INTO HIS IN=MAGE SPACE
machine vision, color co-occurrence method, SGDM,
texture features.
GENERATION OF SGDM MATRIX
1. INTRODUCTION
The pomegranate (Punica granatum) is a COMPUTATION OF TEXTURE
fruit-bearing deciduous shrub or small tree that grows
to between five and eight metres tall and is best
suited to climates where winters are cool and Fig 1: Process of developing the database of
summers are hot. The pomegranate is thought to features.
have been first cultivated 5 to 6,000 years ago and is
native to the regions from Iran through to north 2. LITERATURE REVIEW
India. It is now widely cultivated throughout Eastern Thomas J. Burks et al (2009) [1]
Europe, Asia and the USA, the main areas of world demonstrated that color imaging and texture feature
production being in India, Iran, Spain and California. analysis could be used for classifying citrus peel
Pomegranates can be consumed as fresh fruit or used diseases under the controlled laboratory lighting
in fruit juices, teas, pharmaceutical and medicinal conditions. The present work is an extension of that
products and in dyes or as decoration. There are research, providing a feasibility analysis of the
several hundred different varieties of pomegranate technology in classification of infected
recognised in Iran alone and even more globally, pomegranates. Edwards and Sweet (1986) [2] used
some of the cultivars that have the greatest impact are reflectance spectra of the entire citrus plant for
‘Moller', ‘Ahmar', ‘Bhagawa', ‘Hicaznar' and ‘Dente estimating the damage caused due to citrus blight.
di cavallo'. Globally, it is estimated that total Hetal Patel et al (2011) [3] designed the algorithm
production amounts to around 2,000,000 tonnes, of aiming at calculation of different weights for features
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2. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-August 2012, pp.1861-1866
like intensity, color, orientation and edge of the test and distance‘d’ is small compared to the size of the
image. S.Arivazhagan et al (2010) [4] used computer texture elements, the pairs of points at distance d
vision strategy to recognize a fruit rely on four basic should have similar gray levels. In turn, if the texture
features which characterize the object on the basis of is fine and distance d is comparable to the texture
intensity, color, shape and texture. R. Pydipati et al size, then the gray levels of points separated by
(2006) [5] used the color co-occurrence method to distance d should often be quite distinct, so that the
determine whether texture based hue, saturation, and values in the SGDM matrix should be disperse
intensity color features in conjunction with statistical uniformly. Thus, texture directionality can be
classification algorithms could be used to identify analyzed by examining spread measures of SGDM
diseased and normal citrus leaves under laboratory matrices created at various distances‘d’. Extraction of
conditions. Shearer Scott (1990) [6] proposed a a numerous features is possible using the SGDM
dissertation for the development of new color-texture matrices generated in the above manner.
analysis method to characterize and identify canopy
sections of nursery plant cultivators. Yousef Al Ohali The test image is then cropped such that
(2010) [7] studied the performance of a back around 2000 cropped images of size 16 x 16 each are
propagation neural network classifier and tested the formed. These RGB images are converted into HSI
accuracy of the system on preselected date samples. color space representation. Then each pixel map is
Czeslaw Puchalski et al (2008) [8] developed a used to generate a color co-occurrence matrix,
system for identifying surface defects on apple and resulting in three CCM matrices, one for each of the
successfully obtained 96% classification accuracy. H. H, S and I pixel maps. These matrices measure the
Al-Hiary et al (2011) [9] experimentally evaluated a probability that a pixel at one particular gray level
software solution for automatic detection and will occur at a distinct distance and orientation from
classification of plant leaf diseases. Lanlan Wu et al any pixel, given that pixel has a second particular
(2010) [10] investigated the support vector machine, gray level. For a position operator p, we can define a
as a classifier tool to identify the weeds in corn fields matrix ‘Pij’ that counts the number of times a pixel
at early growth stage. Li Daoliang et al (2012) [11] with grey-level i occurs at position p from a pixel
shown that texture-related features such as co- with grey-level j. For example, if we have four
occurrence matrices might be used as effective distinct grey-levels 0, 1, 2 and 3, then one possible
discriminators for high resolution remote sensing SGDM matrix P (i, j, 1, 0) is given below as shown:
images. Xing-yuan Wang et al (2012) [12] presented
an effective color image retrieval method based on 1 0 3 0 0 1 3 3
texture, which used the color co-occurrence matrix to 2 2 0 3 1 0 0 1
extract the texture feature and measure the similarity I ( x, y ) P
of two color images. Ryusuke Nosakaet al (2012) 3 2 0 2 3 0 2 3
[13] suggested a new image feature based on spatial 1 3 2 3 3 1 3 0
co-occurrence within micro patterns, where each
micro pattern is presented by a Local Binary Pattern Statistical methods use order statistics to
(LBP). model the relationships between pixels within the
region by constructing Spatial Gray-level
3. COLOR CO-OCCURRENCE MATRIX Dependency Matrices (SGDM’s). A SGDM matrix is
Thomas J. Burks et al (2009) [1] used the the joint probability occurrence of gray levels ‘i’ and
color co-occurrence method for citrus peel fruit ‘j’ for two pixels with a defined spatial relationship in
classification. Pydipati et al. (2006) [5] utilized the an image. The SGDMs are represented by the
color co-occurrence method to extract various function P (i, j, d, Ө) where ‘i’ represents the gray
textural features from the color RGB images of citrus level of the location (x, y) in the image I(x, y), and j
leaves. There are two main analysis methods for represents the gray level of the pixel at a distance d
calculation of texture viz. from location (x, y) at an orientation angle of Ө. The
1) Structural Approach nearest neighbour mask is exemplified in figure 2,
2) Statistical Approach where the reference pixel is shown as an asterisk.
Statistical approach, which is used here, is a
quantitative measure of arrangement of intensities in
a region. Statistical methods use second order
statistics to describe the relationships between pixels
within the region by constructing Spatial Gray-level
Dependency Matrices (SGDM). A SGDM matrix is
the joint probability occurrence of gray levels ‘i’ and
‘j’ for two pixels with a defined spatial relationship in
an image. Distance‘d’ and angle ‘θ’ are used to Fig 2: Nearest neighbour mask for calculating
define the spatial relationship. If the texture is coarse spatial Gray-level dependence matrix (SGDM)
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3. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-August 2012, pp.1861-1866
4. TEXTURE FEATURE CALCULATION Where, P (i, j) is the SGDM matrix and Ng is total
Texture is one of the characteristics that number of gray levels considered for generation of
segments images into regions of interest (ROI) and SGDM matrix.
classifies those regions. It gives information about
the spatial organization of the intensities in an image. 5. SUPPORT VECTOR MACHINE
In general, texture can be defined as a characteristic A support vector machine (SVM) is based
of image that can provide a higher-order description on a statistical learning theory showing various
of the image and includes information about the advantages over existing soft computing methods. As
spatial distribution of tonal variations or gray tones. compared with the soft computing methods generally
Texture involves the spatial distribution of gray used for various applications, SVM gives better
levels. Thus, two-dimensional histograms or co- performance. Lanlan Wu et al (2010) [10] achieved
occurrence matrices are reasonable texture analysis classification accuracy ranging 92.31 to 100% for
tools. An image can have one or more textures. These different feature selections using SVM. It is used in
features are useful in carrying out differentiation case of finite sample data. It aims at acquiring the
algorithms for detection purpose ahead. worthy solutions on the ground of present data rather
than the optimal value for infinite samples.
For differentiating infected pomegranate If the original problem is stated in a finite
from the uninfected ones the following features are dimensional space and the sets required to separate
reckoned for the components H, S and I: out are not linearly separable in that space then it can
1) Cluster Shade: It is measures of the lack of be mapped into a higher dimensional space, so as to
symmetry which shows skewness of the matrix. make separation between the sets more
Ng 1 distinguishable. This peculiar quality of SVM shows
F1 = (i P j P ) P(i, j )
i , j 1
x y
3
its good potentiality of abstraction. As its
ramification is independent of sample dimension, it
(1)
solves the problem of dimension disaster. This
method is successfully implemented in face
2) Sum Entropy: It measures the randomness of the recognition, machine fault detection and in
of the matrix, when all elements of the handwritten digit recognition for tablets, PDA’s and
elements other electronic devices.
matrix are maximally random entropy has its highest
value. As shown in figure 3 the two sets of sample
Ng 1 Ng 1
P(i, j ) ln P(i, j )
points are represented by green and red color, H is
F2 = the hyper plane or separator, H1 and H2 are planes
i 0 j 0
(2) running parallel to the hyper plane and pass through
3) Uniformity: It is the measure of uniform pattern in the sample points closest to the hyper plane. As there
the image. are many such hyper planes which can show
Ng 1 Ng 1 divergence between the two sample sets, a hyper
F3 = [ P(i, j )]
i 1 j 1
2
plane which has maximum distance from both H1
(3) and H2 is chosen. The distance between H1 and H2 is
called as margin. The hyper plane with largest margin
4) Mean Intensity: It is the measure of image is called as maximum-margin hyper plane and the
brightness derived from the co-occurrence matrix. linear classifier it defines is addressed as maximum-
Ng 1 margin classifier. The more margin a hyper plane
F4 = iP
i 0
x (i ) has, less is the error in optimizing the separation
(4) between sample points.
5) Product Moment: It is analogous to the co-
variance of the intensity of co-occurrence matrix.
Ng 1 Ng 1
F5 = (i F )( j F ) P(i, j )
i 0 j 0
4 4
(5)
6) Inverse Difference Moment: It returns the
measures of closeness of the distribution of SGDM
elements to the SGDM diagonal.
Ng 1 Ng 1 Fig 3: Distinguishing sample point using SVM
P (i, j )
F6 =
i 0 j 0 1 (i j ) 2 This case study uses the Gaussian Radial
(6)
Basis Function Kernel for the classification. For the
nonlinear case, we project the original space into a
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4. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-August 2012, pp.1861-1866
higher dimension space in which the SVM can Various features are computed from the test
construct an optimal separating hyper-plane as images for components H, S and I which are further
explained above. Let us consider a function (
x), used for generating the 3D plot of each feature
such that F {( x is the feature point
x): X} independently for distinguishing the infected and
corresponding to the data item x. For SVM, the uninfected pomegranate over the plot. In figure 3, red
kernel function is represented as color signifies the feature points for the infected
K(x, z) (( ( )), z
x), z x, X pomegranate whereas green color signifies the same
replacing the inner product (x, z). for uninfected ones. Here X, Y and Z axes represent
feature of image components Hue, Saturation and
6. RESULTS AND DISCUSSION Intensity respectively. It can be easily observed
For the purpose classification, it is often through figure 6 that Cluster Shade (Plot 1), Mean
anticipated that the linear separabilty of the mapped Intensity (Plot 4) and Product Moment (Plot 5)
samples is enhanced in the kernel feature space so features are best for differentiating the diseased
that applying traditional linear algorithms in this pomegranate as the data points are clearly separable
space could result in better performance compared to whereas Sum Entropy (Plot 2), Uniformity (Plot 3)
those obtained in the original input space. If an and Inverse difference Moment (Plot 6) features do
inappropriate kernel is chosen, the classification not possess separate explicit data points.
performance of kernel-based methods can be even
worse than that of their linear counterparts. Li
Daoliang et al (2012) [11] verified that SVM
classification systems leads to the successful
discrimination of targets when fed with appropriate
information. Therefore, selecting a proper kernel with
good class separability plays a vital role in kernel-
based classification algorithms. Here the Gaussian
basis function performs well to do the task. The
contour plots for features cluster shade and mean
intensity are shown in figure 4 and figure 5 below.
Fig 6: 3D Plots for features Plot 1-Cluster Shade,
Plot 2-Sum Entropy, Plot 3-Uniformity, Plot 4-
Mean Intensity, Plot 5-Product Moment & Plot 6-
Inverse Difference Moment
Figure 7 shows bar diagram of success rates
obtained after passing the test images through SVM
training function. As it is seen that cluster shade,
mean intensity and product moment show a eminent
success rate whereas sum entropy, uniformity and
Fig 4: Contour Plot of Cluster shade inverse difference moment have modest success rate
and hence we egest these features for final
classification of images under test.
Fig 7: Success Rate percentage of Features after
Fig 5: Contour Plot of Mean Intensity undergoing SVM training
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5. Ravikant Sinha, Pragya Pandey / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-August 2012, pp.1861-1866
Based on obtained results, features with 7. CONCLUSION
percentage above 90% are selected to acquire higher A study for early sensing of the surface
accuracy for classifying the images. The selected infections on pomegranates using image processing
features for classification show less error or was accomplished. The test image was cropped to
misclassification. Cluster Shade and Cluster form around 2000 samples of size 16x16. The RGB
Prominence have 6, variance has 10 such pomegranate image was then transformed into HSI
misclassifications. image space for nullifying effects of light reflected
The final classification is accomplished by by image. Then each pixel map was used to generate
comparing the features calculated for the test images a color co-occurrence matrix, resulting in three CCM
with the reference training database generated using matrices, one for each of the H, S and I pixel maps.
support vector machine. The images are classified
The generated SGDM matrix was further used for
into two categories namely infected and uninfected. calculation of the discussed texture features. The 3D
plot for each feature were plotted and examined. The
The figure 8 shows the GUI running feature features Cluster shade, Mean intensity and Product
calculation process for the test image. Moment show the towering separation of the data
points whereas the Sum Entropy, Uniformity and
Inverse Difference Moment were found to give lower
separation hence omitted in final process. This
analysis was verified by the result of the SVM
training which showed the success rate of 99% for
the selected ones. The testing process involved the
feature calculation for the test image succeeded by
the SVM classification.
This algorithm can be implemented for
automatic grading and sorting system for quality
control. This can be further extended for detecting
multi diseases of pomegranate using multi-class
classifier.
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