International Journal of Advanced Engineering Research and Science (IJAERS)
Vol-3, Vol Issue-2 , Feb- 2016] ISSN: 2349-6495
A Survey – Cotton Leaf Disease Detection Using Image Processing N. R. Deepa, Dr. S. Gunasekaran, G. Kausalya Department of CSE, Coimbatore Institute of Engineering and Technology, Coimbatore, Coimbatore India
Abstract— The plant disease detection has become significant growth in monitoring large area of crops. The early detection of disease in leaf can control the disease in plants. This paper presents a survey on methods that detect, quantify and classify the disease in cotton leaf. This paper compares benefits and limitations of classification algorithm and need for leaf disease detection in agriculture field. This paper is useful to people those who are working in pattern recognition and image processing. Keywords—Support Vector Machine (SVM), Fuzzy classifier, Genetic Algorithm (GA), k-Means Means Clustering, Otsu’s method. I. INTRODUCTION The Image Processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, such as photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image.[1] 1.1 SEGMENTATION: The process of dividing an image into multiple parts is called segmentation. It is used to identify objects or other relevant information in digital images. The goal of image segmentation is to simplify or change the representa representation of an image into something that is more meaningful and easier to analyze. It will display the result as a set of segments that collectively cover the entire image. Image segmentation can be performed by different ways: Otsu’s method is used for thresholding holding an image, Color based segmentation can be performed by K–Means K clustering, Watershed segmentation can be used to transform methods and Texture Filters can be performed by texture methods. Otsu’s method is used to reduce the gray level image into a binary image. It can be performed automatically in clustering based image thresholding. A binary image contains foreground and background pixels followed by bibi modal histogram.
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k-Means Means clustering is used to divide n observations into k clusters. Each observation vation belongs to the clusters with the nearest mean serves as a prototype of the clusters. k – Means clustering is used to segment the color based images. A texture image is used to artificially create a natural scene in an original image. In computer graphics, gra it has two ways to approach: 1. Structured 2. Statistical. A structured approach works well in artificial texture analyzing. A statistical approach is easier to compute. It has segmented an image by edge detection and co-occurrence co matrices. 1.2 CLASSIFICATION: Classification is the process in which ideas and objects are differentiated and recognized. It is used to easily understand. Two main types of classification are Supervised and Unsupervised classification. Supervised classification is calculated calculat by human – guided. User can select a sample images in a image that represent a specific classes. The software can refer these pixel for classify all other pixels in an image. Unsupervised classification is calculated by software. In an image a pixels aree classified, without the user providing sample classes the outcomes are based on software analysis of an image. Classification can be performed by different ways: Support Vector Machine, Genetic Algorithm, and Fuzzy Algorithm. Support Vector Machine has associated a learning algorithms to analyze the given data and recognize patterns are used for classification. Support Vector Machine is a Supervised learning models.
Fig: 1.2.1 Classification of objects Page | 52
International Journal of Advanced Engineering Research and Science (IJAERS) H3 (green) doesn't separate the 2 classes. H1 (blue) does, with a small margin and H2 (red) with the maximum margin Genetic algorithm is used to find approximations in search problems. To find a solution the ideas are inspired by evolution. Genetic algorithm is a class of evolutionary algorithm. Evolutionary algorithm is used to create a solution to optimization problems using techniques such as inheritance, mutation and selection. Grouping an element into a fuzzy set is called FUZZY classification. A same characteristic of individuals are grouping into a fuzzy set. This process is known as Fuzzy classification. II. LITERATURE SURVEY 2.1 Survey 1 In this paper, visual symptoms of cotton crop diseases can be identified from machine vision system from RGB images. Diseases regions are revealed in digital pictures, and the digital pictures can be amended and segmented. The picture can be extracted using features like Edge, Color, Texture variances using Enhanced PSO feature selection method adopts Skew divergence method. The extracted images are input to the SVM, BPN, and Fuzzy with Edge CYMK color feature and GA feature selection. The three types of classification models are assessed via crossvalidation. The best classification model can be identified by performance of tests. The efficiency can be evaluated in six types of diseases. The diseases can be accurately classified like Bacterial Blight, Fusarium Wilt, Leaf Blight, Root rot, Micro Nutrient, Verticillium Wilt. The disease Bacterial Blight is spread by high temperature, humidity and rainfall. Initial symptoms of this disease include the undersides of leaves having angular water soaked lesions. Leaves are shed when lessions dry and darken with age. Black lessions spread along with stem.
Fig: 2.1.1 Bacterial Blight The disease Fusarium Wilt is a fungal disease. External symptoms of this disease are normally occur at any stage in the crop but, most commonly occur in either the seedling phase or when bolls are filling after flowering. www.ijaers.com
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Fig: 2.1.2 Fusarium Wilt The disease Root Rot is an extremely damaging fungal disease. Symptoms of this disease include leaves wilt and die, yellowing or bronzing of leaves, dead leaves usually remain on plant. At this stage, roots are dead and surface is covered with network of tan fungal strands.
Fig: 2.1.3 Root Rot The disease Micro Nutrient has number of deficiency. The common Micro Nutrient deficiency in cotton is Zn, Mn, and Fe. Symptoms of this deficiency are dark rings on the petiole, Aborted flowers and boll shedding, Distorted, Abnormal uppermost leaves.
Fig: 2.1.4 Micro Nutrient
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International Journal of Advanced Engineering Research and Science (IJAERS) The symptoms of the disease Verticillium Wilt is Leaf Mottle. Leaf Mottle is yellowing between the veins and around the leaf margins, Browning extending throughout the stem and into the petioles. Internal symptoms of this disease can be detected only by cutting the stem.
Fig: 2.1.5 Verticillium Wilt METHODS First, the feature selection is used in feature extraction of cotton leaf diseases like GA with Edge, CMYK color features and Proposed Feature extraction method is EPSO. It has been used to statistical features analyze for the affected part of Poisson distributions like Edge, texture, color variance features. The following method has been used to overcome the accuracy rate from existing features. Second, the classification work has been performed using the existing classifiers like SVM, BPN, Fuzzy. These classifications are used to investigate the results of cotton leaf disease identification. In this system accuracy rate has been evaluated and compared. The existing GA has been used for randomly feature selection subset with color CMYK feature, edge feature techniques. The best fitness of the feature vector can be analyzed to classify the diseases. The proposed work EPSO feature selection is performed using skew divergences of features like edge, color, texture variance. Skew divergences can be used to identify the diseases matching pixels more randomly. The diseases can be classified by the best matching of the obtained features. In EPSO acquisition of segmented leaf images are taken as input image and it gives the output as feature extraction result. It can be calculated as: Cv
=
( − ) ] Where, = ∑
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∑
∑
[
–
− ̅
+
+
∑ ∑
= ∑ Where, = ∑
̅=
∑
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,
=
Proposed calculate divergence oriented variance) ∑ [( = ∑ = ∑
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∑
[(
color_var −
−
CDOV
(Color
) ] …………….. (1)
,
̅=
[(
−
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) ] …………….. (3)
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=
The above calculation, Rxy be the color value of coordinates x and y. where x is the horizontal axis value, y is the vertical axis value, R be the value of color variance. R value can be calculated from original values of the image of red. Blue and Green values can also be calculated using the same method. The color variances of the skew divergence between red and blue images are calculated as: sα ( Cv1 , Cv2 ) = D ( Cv1 || αCv2 + (1 – α ) Cv2 ) The color variances of the skew divergence between green and blue images are calculated as: sα ( Cv2 , Cv3 ) = D ( Cv2 || αCv3 + (1 – α ) Cv3 ) The color variances of the skew divergence between blue and red images are calculated as: sα ( Cv3 , Cv1 ) = D ( Cv3 || αCv1 + (1 – α ) Cv1 ) Then Color variance CV = Sα (Cv1, Cv2) + Sα (Cv2 , Cv3) + Sα (Cv3, Cv1) ……….(4) The variance of the system with color divergence of variance (CDOV) can give the better result compare to the color variance system. Comparing to the existing system the proposed color variance is calculated additionally. It can be calculated as: CV = Sα (Cv1, Cv2) + Sα (Cv2, Cv3) + Sα (Cv3, Cv1) The edge variance edge_var (Ev, El) can be calculated by Canny Sobel method. It can be calculated by the following way: a. Noise of an image can be removed by Gaussian filter b. Choose the image width c. Grad (SI) d. Sobel mask can be used : |G| = √ x2 + Gy2 ~ |Gx| + |Gy|
f. g.
∑
∑
∑
e. ,
Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495
Edge direction θ = tan-1
and resolve the
edge direction. Suppression of non-maxima Edge divergence oriented variance (EDOV) can be calculated as: v^(e^)= !^ 1(u|e^(x,y)<0)) + ^ ^ v 2(u|e (x,y)>0))…………….. (5)
̅ ) ] …………….. (2)
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International Journal of Advanced Engineering Research and Science (IJAERS) The variance values between the different images can be measured by Edge variation. In existing system, edge variance measures the variance of the edges between the images. By calculating the edge variance between the edge points, the skew divergence gives more efficient result compare to the normal variation of edge points. $% (&' u((̂( , ) ), &' u((̂( , ) )) = D(&' u((̂( , ) ) || α&' u((̂( , ) ) + (1-α) &' u((̂( , ) )) Where, α is the skew parameter Where (^( , ) = 0 (detected edge location) & V^1, V^2 (Edge variance) Calculate tex_var (Tv) (TDOV) , =
-./
∑
6
∑5
6
|0(1 + 2, 3 + 4) − 0̅ |
6
∑5
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0 (1 + 2, 3 + 4)
Where, 0̅ =
-./
∑
The skew divergence between the x, y points in total variation Sα (x,y) = D (x || αy + (1 – α) y) ……….. (6) Feature level fusion can be calculated by Joint Feature Vector (JFV). Score level fusion can be calculated by the following two rules: h. Sum rule = Cv + V^(e^) + Tv i. Product rule = Cv . V^(e^) . Tv The images can be classified using EPSO – SVM classification method. j. PSO can be used for feature selection. Edge detected feature vector as an input data point for SVM classification. It gives classification result and prediction to the output. Performance evaluation of edge feature with SVM, BPN, Fuzzy Classifiers in disease vise. Edge feature extraction method can be used with SVM, BPN and Fuzzy classifiers gives the 68%, 70%, 78% of result accuracy. Performance evaluation of edge feature with CYMK Color feature with SVM, BPN, Fuzzy Classifiers in disease vise. Edge with CYMK feature extraction method can be used with SVM, BPN and Fuzzy classifiers gives the 73%, 77%, 78% of result accuracy. Performance evaluation of edge feature with GA with SVM, BPN and Fuzzy classifiers in disease vise. Edge features with GA feature extraction method can be used with SVM, BPN and Fuzzy classifier gives the 81%, 81%, 82% of result accuracy. Performance evaluation of edge feature with CYMK Color feature combined with GA feature extraction method can be used with SVM, BPN and Fuzzy Classifiers in disease vise. Edge feature with CYMK Color feature and GA feature www.ijaers.com
Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495
extraction method can be used with SVM, BPN and Fuzzy Classifier gives the 83%, 85%, 85% of result accuracy. Performance evaluation of edge feature with Color, Texture features combined with GA feature selection with SVM, BPN and Fuzzy classifiers in disease vise. Edge features with Color, Texture features and GA feature extraction method can be used with SVM, BPN and Fuzzy Classifier gives the 86%, 89%, 91% of result accuracy. Performance evaluation of edge feature with Edge feature with Color Texture features combined with a PSO feature selection with SVM, BPN and Fuzzy classifiers in disease vise. Edge with Color, Texture features and PSO feature selection method can be used with SVM, BPN and Fuzzy Classifier gives the 91%, 93%, 94% of result accuracy. 2.2 Survey 2 In this paper, the quality of agriculture products is reduced from many causes. The main cause of quality reduction is plant diseases. In Indian cotton regions, major fungal disease such as Foliar disease is occurring in cotton leaf. The symptoms of cotton leaf spot images are captured by mobile and the diseases are categorized using Support Vector Machine classifier. Achievement of intelligent farming is obtained by providing adequate training given to classifier. It includes selective fungicide application, early detection of disease in the groves, etc. The proposed work is based on segmentation technique, pre-processing the images captured from the mobile. To recognize the diseases, text feature and color feature extraction techniques are used to extract the features such as shape, color, texture and boundary for the disease spots. Plant disease is reduced allows for improving the quality of the product. Various diseases is recognized on the cotton leaf spots like Grey Mildew, Bacterial Blight, Leaf Curl, Fusarium Wilt, Verticillium Wilt, Alternaria Leaf spot. The disease Grey Mildew is infecting on both surfaces of the leaves. White powdery fungus is uniformly spread on the leaves. The diseased leaves are defoliate when the fungus leads to curling and drying of the leaves. The white powdery fungus appears on under surface of the leaves, reversely a yellowish color spots on upper surface of the leaves.
Fig: 2.2.1 Grey Mildew Page | 55
International Journal of Advanced Engineering Research and Science (IJAERS) The symptoms of the disease Bacterial Blight is dark-green water soaked spots with red to brown margin is on the infected leaves. Red to brown margin turn dark- brown or black margin for the death of infected tissues. The leaf petiole and stem are infected in premature defoliation. The infected stem is to die and break when it is gridle with black lesions.
Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495
symptoms of the disease only on the lower or outer parts of the plants.
Yellowing between the veins
Fig: 2.2.2 Bacterial Blight The initial symptoms of Leaf Curl disease is swelling and darkening of leaf veins. Leaf margins are curled and the youngest leaves are deep downward cupping. The cupshaped leaf-like structures can occur in some types of cotton leaves.
Brown streaks Fig: 2.2.5 Verticillium Wilt The disease Alternaria Leaf Spot has the symptoms of infections on cotyledons. The matured leaves spots are dry, grey centers are often cracked and fall out. Irregular dead areas are produced by coalesce. Susceptible varieties affected the disease can defoliate the leaves.
Fig: 2.2.3 Leaf Curl The typical symptoms of the disease Fusarium Wilt is wilting, defoliated plant, yellowing and death of the plant. The entire main stem is exhibits a brown discoloration when the vascular tissue affected plants. Regrowth of plants is also a symptoms of disease affection.
Fig: 2.2.4 Fusarium Wilt The symptoms of the disease Verticillium Wilt and Fusarium Wilt are similar. The stem cuts diagonally, Verticillium disease has dark brown to black color streaks at the centre of the stem. The stem cuts lengthways, brown flecking of the inner tissues in stem compared to continuous browning of the disease Fusarium Wilt infected plants.The www.ijaers.com
Fig: 2.2.6 Alternaria Leaf Spot Methodology Image acquisition, in this phase various leaf images are captured using a digital camera. Pre-processing the captured images. In segmentation actual segments of the images are segmented. The infected part of the leaf is extracted using the pixels in the image or texture extraction. Statistical analysis is used to select the best features for the given image. The diseases are classified using SVM. Image Acquisition
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International Journal of Advanced Engineering Research and Science (IJAERS) The RGB images of cotton leaves are captured by digital camera. The captured image is stored in BMP format. Image pre-processing and segmentation In pre-processing phase, the images are enhanced by some procedures. The captured cotton leaf images are in RGB format. RGB image is converted into grayscale image. The image can be segmented using gray-level threshold, the binary image is gained. It can be segmented using global, variable threshold and automatic threshold can be used by Otsu method. The quality of an image can be affected by camera flash act as noise. The unnecessary spots of an images are removed by median filter and morphological operators. Feature Extraction To indicate the existence disease leaf spots are the important units. To investigate the set of spot features for classification and detection of different diseased leaves. Using image processing method spot features of leaves are extracted from a leaf. The visual representation of color and morphological features provide critical information. Features of diseased leaves can be extracted by segmentation technique. The features are mean and variance of gray level of RGB channel of spots. a) Shape feature extraction The shape of objects is characterized by number of an objects, width and length of an object, area of images. The characteristics of object shape are used to extract the feature of lesion, spot and percentage of lesion. A noise free labeled region of binary image can be calculating statistics using Blob analysis. Noise free binary images are number of object, area and perimeter. b) Color feature extraction Color is used to easily recognize the images in different classes.In a color image pixel is represented in RGB space each pixel is represented by triplet color as R, G, B. In segmentation method other color spaces like HIS and CIE are also used. Distance of two color can be evaluated by two color points in a color space. Commonly, Euclidean distance methods can be used to evaluate the distance of two colors. The planned technique is CIELab color space. CIELab color space is chromaticity color space, it gets broken leaf color, spot color and boundary color. In CIELab color space is perceived by human visual system is difference from proportional of two colors in Euclidean distance. The composed RGB images are converted into CIELab color component. Statistical analysis It can be used for select the best features to represent the image. It reduce the redundancy feature. www.ijaers.com
Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495
Classification Support Vector Machine (SVM) is used for classification. SVM is generally based on machine learning theory. Generalization performance of SVM is based on the Structural Risk Minimization (SRM) Principle. Class separation margin can be maximized by the concept of SRM. Two-Class problem is defined in SVM for maximize the distance, margin, and optimal hyper-plane of two classes. SVM is used for pattern recognition and other classification purpose. A group of supervised learning methods is SVM. SVM is applied in classification and regression. The given data is predicts and classify only two distinct classes. A set of training data is given to the SVM classifier, to classify a set of data based on this model. Multiclass problem of SVM is decompose by multi class problem into multiple binary class problem. Multiple binary class can be used by suitable design for combined multiple binary classifiers in SVM. Training errors are minimized by structural risk minimization implemented by SVM. To classify cotton leaf spot disease using multiclass SVM. Number of weight vectors is used to segment the cotton leaf color. The segmented images give both diseased and nondiseased pixels. The pixels are trained in SVM for segmentation of cotton leaf disease. Performance Analysis The performance of canny edge detection and color feature extraction in feature extraction and HPCCDD algorithm is used in classification gives an accuracy of 98.1%. The performance of WEB-Based intelligent diagnosis system for cotton disease control feature extraction and BPNN algorithm is used in classification gives an accuracy of 90%. The performance of Fuzzy feature selection approach for fuzzy curves (Fe) and surfaces (Fs) feature extraction and BPNN algorithm is used in classification gives an accuracy of 90%. The performance of CYMK based image cleaning technique to remove shadows, impurities feature extraction and RPM, Dis Bin, PCA algorithm is used in classification gives an accuracy of 83%. The performance of Wavelet transform feature extraction and SVM algorithm is used in classification gives an accuracy of 97%. The performance of RGB Color feature extraction model feature extraction and NN algorithm is used in classification gives an accuracy of 75.9%. The performance of Texture feature extraction-GLCM feature extraction and SVM algorithm is used in classification gives an accuracy of 97.2%. Page | 57
International Journal of Advanced Engineering Research and Science (IJAERS) 2.3 Survey 3 In this paper, eigen features are extracted and regularizes from the cotton leaf images. Within class type scatter matrix is developed. Various subspaces is decomposed by scatter matrix related to the diseases like fungal disease and leaf crumple. Number of sample images is provided for considering various variation of pixel value. In subspaces eigen features are regularized, the discriminant evaluation enabled by eigen spectrum is performed in whole space feature extraction. Finally dimensionality reduction occurs; the disease is identified by comparison of feature result. Disease classification Cotton leaves are classified as three types such as bacterial disease, fungal disease and viral disease. Bacterial diseases are like Bacterial Blight, Crown Gall and Lint Degradation. Fungal diseases are like Anthracnose, Red Spots and White spots. Viral diseases are like Leaf Curl, Leaf Crumple and Leaf Roll. The above mentioned diseases are affected dramatically in cotton leaves. The selective types of cotton leaf diseases are explained below: A. Red Spots In early stage of Red Spot disease, red to brown border starts as an angular leaf spot. Red Spots spread along a major leaf veins as a disease progressed in leaves.
Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495
C. White Spots The cotton leaves are suffered from the White Spot disease and diagnosis of disease can be applying method of PCA. PCA method is used to analyze the disease on the cotton plant leaf.
Fig: 2.3.4 White Spots Eigen Spectrum Modeling In eigen spectrum modeling a set of w-by-h images of leaves are given. A training set of column image vectors {Xij}. Where, Xij € IRn=wh , the order of pixel elements of image j of test image i. 8 l = ∑9 79 is the number of total training samples. Each disease leaf image is a class with prior probability ci for image recognition. Within class scatter matrix is defined by, < 8 : Sw = ∑9 ; ∑> ; =9> − =9, (=9> − =9, )? ……………… (1) <;
Between class scatter matrix Sb and the total matrix St is defined by, 8 Sb = ∑9 @9 =9′ − = ′ (=9′ − = ′ )? ……………….. (2) 8
St = ∑ 9 Fig: 2.3.1 Red Spots B. Leaf Crumpel The reduction of yield and stunted season the plants infected early. This disease occurs in growing season of plant. It is very dangerous disease, 100% of yield losses the leaf crumpel disease affected plant.
Fig: 2.3.2 Leaf Crumpel Downward
Fig: 2.3.3 Leaf Crumpel Upward www.ijaers.com
Where,
:; < ∑> ; <; =9′ = 796
=9> − =9, (=9> − =9, )? …………… (3) <
∑> ; =9> and Xi = ∑89 @9 =9′
Let Sg, one of the above scatter matrices is represented by g€ {t,w,b}. The elements of the image vector and class mean vector as decorrelated by solving the eigen value problem. ^A = ФgT Sg Фg ........................... (4) Where, A A ^A is the diagonal matrix of eigen values ʎ , … . , ʎ/ corresponding to eigen vector. The eigen vector is stored in descending vector ALGORITHM The features are extracted that have smallest within class variations and largest between class variations. Number of training samples is used to estimate the variations. Now, equation (4) can be solve, it is related to eigen value problem. In training stage, proposed eigen feature regularization and extraction (ERP) approach is given below: Page | 58
International Journal of Advanced Engineering Research and Science (IJAERS) a. b. c. d.
{Xij} is a given set of cotton leaf images, compute Sw and the eigen value problem is solved Leaf images are decomposing the eigen space Training set are transform Xij into Yij and in equation (4) solve the eigen value problem To solve the eigen value problem by computing
$5 e. Finally feature regularization and extraction matrix is obtained At recognition stage a. Each n-D sample images vector X is transform into d-D feature vector F by feature regularization and extraction matrix in the training stage. b. Set of images are recognized by classifier method. Analysis and comparisons a. Sample train matrix Based on the sample train matrix the analyzed images are compare for its disease. A train matrix contains training values of disease detection. Created database is called as N sample train matrix. The train matrix obtained is 100 sample images i.e. N=50, it contains various feature values of disease. b. Disease detection In analysis point of view, 50 images from the database the red spot disease is affected mostly in the cotton leaves. In cotton leaves the disease affected period is August to December. Out of 50 samples 45 leaf is highly affected by disease Red Spot i.e. Fungal disease. A same leaf is affected by both Red Spot and Leaf Crumple. Another disease also detected called Whit spots. A disease part is detected and ROI of the image is taken to detect the disease on a particular image. Performance Analysis The disease is detected in the early stage before the diseases affect the whole plant. Three diseases can be detecting the cotton leaves by the method of Eigen feature regularization and extraction technique. Compare to other feature extraction methods the proposed method gives more success rate. In this method 90% of disease detection is Red Spot disease. It is fungal disease and it is dangerous, highly affected the cotton leaves. ′
III. SURVEY 1 2 3
COMPARISION TABLE METHODS
Fuzzy classification with PSO feature selection SVM classification and GLCM Texture feature extraction Eigen feature
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RESULT ACCURACY 94
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IV. CONCLUSION This paper presents a survey of cotton leaf disease detection using several ways of classification methods. First survey has Fuzzy classification with PSO feature selection method gives 94 percent accuracy. Second survey has SVM classification and GLCM texture feature extraction method gives 97.2 percent result accuracy. Third survey has eigen feature method gives 90 percent accurate result. Finally, SVM classification method gives more accurate result for leaf disease detection. REFERENCES [1] Mrunalini R. Badnakhe and Prashant R. Deshmukh .2012. "Infected Leaf Analysis and Comparison by Otsu Threshold and k-Means Clustering”, IJARCSSE, 2 (3): 449-452 [2] Arivazhagan. S, Newlin Shebiah.R, Ananthi.S, and Vishnu Varthini. S. 2013. Detection of unhealthy regions of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int: CIGR Journal, 15(1):211-217 [3] Hui Li, Ronghua Ji, Jianhua Zhang, Xue Yuan, Kaiqun Hu and Lijun Qi, 2011.WEB-Based Intelligent Diagnosis System for Cotton Diseases Control” IFIP Advances in Information and Communication Technology, 346ss: 483-490. [4] P.Revathi M.Hemalatha, Classification of Cotton Leaf Spot Diseases Using Image Processing Edge Detection Techniques, 2012 - International Conference on Emerging Trends in Science, Engineering and Technology [5] P.Revathi, M.Hemalatha, ”Advance Computing Enrichment Evaluation of Cotton Leaf Spot Disease Detection Using Image Edge detection “, ICCCNT'1226t_28t July 2012, Coimbatore, India, IEEE- 20180 [6] Yan Cheng Zhang, Han Ping Mao, Bo Hu, Ming Xili “features selection of Cotton disease leaves image based on fuzzy feature selection techniques” IEEE Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2-4 Nov. 2007. [7] http://www.cicr.org.in/pdf/resistance_greymildew.pdf [8] http://www.oisat.org/pests/diseases/fungal/fusarium_w ilt.html [9] http://agridr.in/tnauEAgri/eagri50/PATH272/lecture13 /001.html
97.2 90
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International Journal of Advanced Engineering Research and Science (IJAERS)
Vol-3, Issue-2 , Feb- 2016] ISSN: 2349-6495
[10] https://www.daf.qld.gov.au/plants/field-crops-and pastures/broadacre-field-crops/cotton/diseasemanagement/disease-photos [11] https://en.wikipedia.org/wiki/Verticillium_wilt [12] https://www.planthealthaustralia.com.au/wpcontent/up loads/2013/03/Verticillium-wilt-FS.pdf [13] http://www.oisat.org/pests/diseases/bacterial/bacterial_ leaf_blight.html [14] https://www.daf.qld.gov.au/plants/field-crops-andpastures/broadacre-fieldcrops/cotton/diseasemanagement/alternaria-leaf-spot [15] http://www.ces.ncsu.edu/depts/pp/notes/Cotton/cdin1/ cdin1.htm
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