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  • 1. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 36 IMAGE PROCESSING METHODOLOGIES FOR DISEASE DETECTION AND RECOGNITION OF MANGO CROP: A SURVEY Shivayogi B. Ullagaddi Dr. Vishwanadha Raju Department of CSE Department of CSE VTU Belgaum, Karnataka JNTUHCEJ, JNT University, Hyderabad ABSTRACT The advanced technology has created the new challenges in image processing to handle complex images of agriculture or horticulture and its production. The disease detection and diagnose from such images has become important research activity and is very much useful in the development of several new systems. One such a application is ability to identify disease affected area in captured images and diagnose it .such a system require an automated method to detect and extract feature of lesion area prior to further image analysis. In this paper, several challenges related to disease detection and recognition from complex background images are discussed and comprehensive survey on various approaches for disease identification from image is presented, and concludes with Future directions. 1. INTRODUCTION The growth in the technologies has lead to the emergence of intelligent and automated systems such as grading, and classification for agriculture products. These systems advantages, flexibility and convenience of information technology within grasp of individual and impact the quality of all aspects of life. The availability of such a systems and advancement in the technologies has made hitherto unthinkable applications a reality. One such an application is ability to detect and recognize the diseases on mango crop. As There is no doubt that agriculture has been a key driving force of the economy, at all times. As the development of agricultural technology advanced, the proportion of those depending on farming declined the focus of agricultural research and development was mainly on maximizing yields. When pests and diseases affect the crops, there will be a tremendous decrease in production. When the production is good farmers are suffer while selling their INTERNATIONAL JOURNAL OF GRAPHICS AND MULTIMEDIA (IJGM) ISSN 0976 - 6448 (Print) ISSN 0976 -6456 (Online) Volume 5, Issue 1, January - April 2014, pp. 36-45 © IAEME: www.iaeme.com/ijgm.asp Journal Impact Factor (2014): 4.4531 (Calculated by GISI) www.jifactor.com IJGM © I A E M E
  • 2. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 37 production in market or to export due to quality and grade so there is a need to develop a advanced techniques for diagnosis of diseases, classification and grading of mango crop. Therefore identification or classification of mango plants, leaves, fruits and finding out the pest or diseases, percentage of the pest or disease incidence , symptoms of the pest or disease attack is an important and challenging task, machine based automatic detection of disease its classification and diagnose plays a key role in successful cultivation, grading of and marketing for crops. 2. CHALLENGES AND MOTIVATION Mango is the popular delicious fruit and cash crop. When diseases affect the crop there is significant decrease in the yield due to which Farmers suffer in selling their yield, this problem motivated to develop the new techniques to detect and diagnose the diseases affecting the mango crop and devise the expert system to prevent those. The main diseases of mango crop and their symptoms are as below. Powdery Mildew (Oidium mangiferae): Powdery mildew is one of the most serious diseases of mango affecting almost all the varieties. The characteristic symptom of the disease is the white superficial powdery fungal growth on leaves, stalk of panicles, flowers and young fruits. The affected flowers and fruits drop pre-maturely reducing the crop load considerably or might even prevent the fruit set. Anthracnose (Colletotrichum gloeosporioides): It is of widespread occurrence in the field and in storage. The disease causes serious losses to young shoots, flowers and fruits under climatic conditions like high humidity, frequent rains and the temperature. The disease produces leaf spot; blossom blight, withered tip, twig blight and fruit rot symptoms. Tender shoots and foliage are easily affected which ultimately cause die back of young branches. Older twigs may also be infected through wounds, which in severe cases may be fatal. Black spots develop on panicles. Severe infection destroys the entire inflorescence resulting in failure of fruit setting. Young infected fruits develop black spots, shrivel and drop off. Fruits infected at mature stage carry the fungus into storage and cause considerable loss during storage, transit and marketing. Die Back (Botryodiplodia (Lasiodiplodia) theobromae): Die back is one of the serious diseases of mango. The disease on the tree may be noticed at any time of the year but it is most conspicuous during October-November. The disease is characterized by drying of twigs and branches followed by complete defoliation, which gives the tree an appearance of scorching by fire. Initially it is evident by discoloration and darkening of the bark. The dark area advances and extends outward along the veins of leaves. The affected leaf turns brown and its margins roll upwards. At this stage, the twig or branch dies, shrivels and leaf falls. This may be accompanied by exudation of yellowish brown gum. Phoma Blight (Phoma glomerata) : The symptoms of the disease are observed only on old leaves. Initially, the lesions are angular, minute, irregular, yellow to light brown, scattered over leaf lamina. As the lesions enlarge their color changes from brown to cinnamon and they become almost irregular. In case of severe infection such spots coalesce forming patches resulting in complete withering and defoliation of infected leaves.
  • 3. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 38 Bacterial Canker (Xanthomonas campestris pv. mangiferaeindicae) : Canker is a serious disease in India. The disease causes fruit drop (10-70%), yield loss (10-85%) and storage rot (5-100%). Many commercial cultivars of mango including Langra, Dashehari, Arnrapali, Mallika and Totapuri are susceptible to this disease. The disease is found on leaves, petioles, twigs, branches and fruits. The disease first appears as minute water soaked irregular lesions on any part of leaf or leaf lamina. Several lesions coalesce to form irregular necrotic cankerous patches. In severe infections the leaves turn yellow and drop off. Cankerous lesions also appear on petioles, twigs and young fruits. The water soaked lesions also develop on fruits which later turn dark brown to black. They often burst open, releasing highly contagious gummy ooze containing bacterial cells. Red Rust (Cepbaleuros viruses) : The disease attack causes reduction in photosynthetic activity and defoliation of leaves thereby reducing the vitality of the host plant. The disease is evident by the rusty red spots mainly on leaves and sometimes on petioles and bark of young twigs. . The spots are greenish grey in colour and velvety in texture. Later, they turn reddish brown. The circular and slightly elevated spots sometimes coalesce to form larger and irregular spots. The affected portion of stem cracks. In case of severe infection, the bark becomes thick, twigs get enlarged but remain stunted and the foliage finally dries up. Sooty Mould (Meliola mangiferae) : The disease is common in the orchards where mealy bug, scale insects and hoppers are not controlled efficiently. The disease in the field is recognized by the presence of a black sooty mould on the leaf surface. In severe cases, the trees turn completely black due to the presence of mould over the entire surface of twigs and leaves. The severity of infection depends on the honey dew secretion of the above insects. Honey dews secretions from insects stick to the leaf surface and provide necessary medium for fungal growth. Although the fungus causes no direct damage, the photosynthetic activity of the leaf is adversely affected. Diplodia Stem-end Rot (Lasiodiplodia theobromae) : The fungus enters through mechanically injured areas on the stem or skin. The fungus grows from the pedicel into a circular black lesionaround the pedicel. The digital images captured by cameras may contain complex background and significant degradations; hence disease detection activity from such a images is difficult task and poses several challenges as discussed below Identification or recognition of mango trees or plants fruits and leaf. Shape Recognition Color classification Texture classification Mango fruit grading in market for export or to maintain quality Size and color classification Evaluation of aging of fruit Considering temperature and humidity Detection and diagnosis of diseases like powdering mildew, anthracnose and spongy tissue Early Disease detection in leaf, flower, fruit and stem Quantifying affected area of disease
  • 4. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 39 Finding the shape of affected area Determining the feature of affected area (color change) Rate of growth of disease Growth of crop after chemical spray etc. 3. METHODS FOR DISEASE RECOGNITION A number of methods for disease identification and diagnoses have been published in recent years and are categorized into various methods like, texture, neural network, fuzzy and web based methods, the performance of methods found to be inefficient and expensive due to several challenges. Hence techniques based on texture analysis have become good choice for analyzing such images. The techniques based on Gabor filter, Wavelet, spatial variance, etc can be used to detect properties in diseased area of image. Automatic recognition of quarantine citrus diseases has been discussed in [1]. This work, presents a model capable of automatic recognize the quarantine diseases. It is based on the combination of a feature selection method and a classifier that has been trained on quarantine illness symptoms. Citrus samples with citrus canker, black spot, scab and other diseases were evaluated. Experimental work was performed on 212 samples of mandarins from a Nova cultivar. The proposed approach achieved a classification rate of quarantine/not-quarantine samples of over 83% for all classes, even when using a small subset (14) of all the available features (90). The results obtained show that the proposed method can be suitable for helping the task of citrus visual diagnosis, in particular, quarantine diseases recognition in fruits. Methods for disease detection and recognition Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features is proposed in [2]. The proposed system is a software solution for automatic detection and classification of plant leaf diseases. The developed processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, and then the green pixels are masked and removed using specific threshold value followed by segmentation process, the texture statistics are computed for the useful segments, finally the extracted features are passed through the classifier. The proposed algorithm’s efficiency can successfully detect and classify the examined diseases with an accuracy of 94%. Experimental results on a database of about 500 plant leaves confirm the robustness of the proposed approach. Grading and Classification of Anthracnose Fungal Disease of Fruits based on Statistical Texture Features is described in [3]. They have considered three types of fruit namely mango, grape and pomegranate for their work. The developed processing scheme consists of two phases. In the first phase, segmentation techniques namely thresholding, region growing, K-means clustering and watershed are employed for separating anthracnose affected lesion areas from normal area. Then these affected areas are graded by calculating the percentage of affected area. In the second phase texture features are extracted using Runlength Matrix. These features are then used for classification purpose using ANN classifier. We have conducted experimentation on a dataset of 600 fruits’ image samples. The classification accuracies for normal and affected anthracnose fruit types are 84.65% and 76.6% respectively. A web-based tool for visual plant disease identification, a proof of concept with a case study on strawberry is given in [4]. The system is based on a multi-access key of identification and specifically on the selection of pictures by the user and can be used remotely from a desktop as well as from a smart phone or personal digital assistant. The
  • 5. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 40 system was developed following a simple approach: visual identification where images and/or short descriptions are used to uniquely identify diseases when possible and suggest refining the visual identification process in cases of ambiguous identification. It has been designed in a way that allows easy definition of additional diseases by uploading the correct images and defining the identification rules and diseases. The system may aid growers in identifying various diseases when using the system remotely while the system is developed and maintained centrally. The system is tested for visual identification of strawberry diseases using a computer and samples of infected plants. The evaluation showed that it is effective and accurate in enabling its users to identify strawberry diseases. An intelligent system for the assessment of crop disorders has been discussed in [5]. The paper reported Isacrodi, a web based system designed to assist farmers in assessing disorders in their crops, and in protecting their crops. it uses a controlled vocabulary to describe key components such as crops and crop disorders. Crop disorders are described in the form of CDRs which can be labelled if the disorder has been determined by an expert, or unlabelled if the disorder is unidentified. Isacrodi uses a diagnosis provider which based on a multi-class support vector machine. They evaluated the performance of the SVM diagnosis provider with CDR data generated by a random process that that captures key features of the envisaged usage of the Isacrodi system. The results of the test showed to that even in favourable conditions, diagnoses are not perfectly accurate. However, even where the diagnosed disorder, i.e. the disorder withthe highest score, was not correct, the second or third highest scoring disorder could be the correct diagnosis. Plant species identification using digital marphometric is described in [6]. They reviewed the main computational, morphometric and image processing methods that have been used in recent years to analyze images of plants, introducing readers to relevant botanical concepts along the way. it discuss the measurement of leaf outlines, flower shape, vein structures and leaf textures, and describe a wide range of analytical methods in use. also discussed a number of systems that apply this research, including prototypes of hand-held digital field guides and various robotic systems used in agriculture. Rice diseases classification using feature selection and rule generation techniques is given in [7]. This paper aims at classifying different types of rice diseases by extracting features from the infected regions of the rice plant images. Fermi energy based segmentation method has been proposed in the paper to isolate the infected region of the image from its background, symptoms of the diseases are characterized using features like color, shape and position of the infected portion and extracted by developing novel algorithms. To reduce complexity of the classifier, important features are selected using rough set theory (RST) to minimize the loss of information. Finally using selected features, a rule base classifier has been built that cover all the diseased rice plant images and provides superior result compare to traditional classifiers. Applying image processing technique to detect plant diseases is described in [8]. The work proposes a methodology for detecting plant diseases early and accurately, using image processing techniques and artificial neural network (ANN). The presented work is aimed to develop a simple disease detection system for plant diseases. The work begins with capturing the images. Filtered and segmented using Gabor filter. Then, texture and color features are extracted from the result of segmentation and Artificial neural network (ANN ) is then trained by choosing the feature values that could distinguish the healthy and diseased samples appropriately. Experimental results showed that classification performance by ANN taking feature set is better with an accuracy of 91%.
  • 6. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 41 Infection Analysis Using Color Feature Texture Using Image processing is described in [9]. In this paper, a new approach is used to automatically detect the infected pomegranates. Color texture feature analysis is used for detection of surface defects on pomegranates. Acquired image is initially cropped and then transformed into HSI color space, which is further used for generating SGDM matrix. Total 18 texture features were computed for hue (H), saturation (S) and intensity (I) images from each cropped samples. Best features were used as an input to Support Vector Machine (SVM) classifier and tests were performed to identify best classification model. Out of selected texture features, features showing optimal results were cluster shade (99.8835%), product moment (99.8835%) and mean intensity (99.8059%). Investigation and monitoring for leaves disease detection and evaluation using image processing is proposed in [10]. In this, system identifies leaves disease of plants and also determines the stage in which the disease is. The system has various image processing techniques. At first, the images are captured and processed for enhancement. Then image segmentation is carried out to get disease regions. Later, image features such as shape, color and texture are extracted for the disease regions. These resultant features are given as input to disease classifier to appropriately identify and grade the diseases. Classification of Rice Leaf Diseases Based on Morphological Changes is reported in [11]. In this work, an automated system has been developed to classify the leaf brown spot and the leaf blast diseases of rice plant based on the morphological changes of the plants caused by the diseases. Radial distribution of the hue from the center to the boundary of the spot images has been used as features to classify the diseases by Bayes’ and SVM Classifier. The system has been validated using 1000 test spot images of infected rice leaves collected from the field, gives 79.5% and 68.1% accuracies for Bayes’ and SVM Classifier based system respectively. A hybrid intelligent system for automated pomegranate disease detection and grading is described in [12]. This paper proposes the systemthat encompasses various image processing and soft computing techniques. The methodology begins with image acquisition. Captured images are enhanced and segmented with appropriate algorithms. Further, feature extraction is carried out and selected features are used as input to the disease classifier which appropriately identifies and grades the disease. Once the disease and its stage are identified accurately, a proper disease treatment advisory can be provided. Remote Area Plant Disease Detection Using Image Processing is reported in [13]. They propose color and texture features are used to recognize and classify different agriculture/horticulture produce into normal and affected regions. The combinations of features prove to be very effective in disease detection. The experimental results indicate that proposed approach significantly enhances accuracy in automatic detection of normal and affected produce. This paper presents an effective method for detection of diseases in Malus Domestica using methods like K-means clustering, color and texture analysis. Image Processing Techniques for Diagnosing Paddy Disease is described in [14]. This paper concentrates on extracting paddy features through off-line image. The methodology involves image acquisition, converting the RGB images into a binary image using thresholding based on local entropy threshold and Otsu method. A morphological algorithm is used to remove noises by using region filling technique. Then, the image characteristics consisting of lesion type, boundary color, spot color and broken paddy leaf color are extracted from paddy leaf images. Consequently, by employing production rule technique, the paddy diseases are recognized about 94.7 percent of accuracy rates.
  • 7. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 42 Early Pest Identification in Greenhouse Crops using Image Processing Techniques is given in [15]. The paper describes a software prototype system for pest detection on the infected images of different leaves. Images of the infected leaf are captured by digital camera and processed using image growing, image segmentation techniques to detect infected parts of the particular plants. Then the detected part is been processed for further feature extraction which gives general idea about pests. This proposes automatic detection and calculating area of infection on leaves of a whitefly (Trialeurodes vaporariorum Westwood) at a mature stage. Scab Diseases Detection of Potato using Image Processing is described in [16]. This paper proposes image processing methodology to detect scab disease of potato. In this paper first, the captured images are collected from different potato field and are processed for enhancement. Then image segmentation is carried out to get target regions (disease spots). Finally, analysis of the target regions (disease spots) based on histogram approach to finding the phase of the disease and then the treatment consultative module can be prepared by on the lookout for agricultural experts. Leaf Disease Severity Measurement Using Image Processing is given in [17]. This describes Disease symptoms of the plant vary significantly under the different stages of the disease so to the accuracy with which the severity of the disease measured is depends upon segmentation of the image. Simple threshold segmentation is used to calculate the leaf area but this method is not suitable to calculate the area of the lesion region because of varying characteristics of the lesion region. Triangle method of the thresholding used here to segment the lesion region. The average accuracy of the experiment is 98.60 %. So the image processing technology to measure plant disease severity is convenient and accurate. Classification of Herbs Plant Diseases via Hierarchical Dynamic Artificial Neural Network after Image Removal Using Kernel Regression Framework is described in [18]. In this work, image processing and pattern classification are going to be used to implement a machine vision system that could identify and classify the visual symptoms of herb plants diseases. The image processing is divided into four stages: Image Pre-Processing to remove image noises (Fixed-Valued Impulse Noise, Random-Valued Impulse Noise and Gaussian Noise), Image segmentation to identify regions in the image that were likely to qualify as diseased region, Image Feature Extraction and Selection to extract and select important image features and Image Classification to classify the image into different herbs diseases classes. This paper is to propose an unsupervised diseases pattern recognition and classification algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which provides an adjustable sensitivity-specificity herbs diseases detection and classification from the analysis of noise-free colored herbs images. It is also to proposed diseases treatment algorithm that is capable to provide a suitable treatment and control for each identified herbs diseases. Fast and Accurate Detection and Classification of Plant Diseases is given in [19]. In this paper, the applications of K-means clustering and Neural Networks (NNs) have been formulated for clustering and classification of diseases that affect on plant leaves. Recognizing the disease is mainly the purpose of the proposed approach. The proposed Algorithm was tested on five diseases which influence on the plants; they are: Early scorch, Cottony mold, ashen mold, late scorch, tiny whiteness. The experimental results indicate that the proposed approach is a valuable approach, which can significantly support an accurate detection of leaf diseases in a little computational effort.
  • 8. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 43 A Step towards Precision Farming of Rice Crop by Estimating Loss Caused by Leaf Blast Disease Using Digital Image Processing and Fuzzy Clustering is proposed in [20]. This paper describes the application of Fuzzy C-Mean Clustering algorithm to estimate the loss caused by blast disease in rice crop. A digital image has been taken by digital camera of rice crop, which is further analyzed by taking RGB feature of that image and then classified using Fuzzy C Mean Clustering algorithm. That clustered information can beused for precision farming by farmer for decision support system. Image pattern classification for the identification of disease causing agents in plants is described in [21]. This paper reports a machine vision system for the identification of the visual symptoms of plant diseases, from colored images. Diseased regions shown in digital pictures of cotton crops were enhanced, segmented, and a set of features were extracted from each of them. Features were then used as inputs to a Support Vector Machine (SVM) classifier and tests were performed to identify the best classification model. They hypothesized that given the characteristics of the images; there should be a subset of features more informative of the image domain. To test this hypothesis, several classification models were assessed via cross-validation. The results of this study suggested that: texture-related features might be used as discriminators when the target images do not follow a well defined color or shape domain pattern; and that machine vision systems might lead to the successful discrimination of targets when fed with appropriate information. Multiple Classifier Combination for Recognition of Wheat Leaf Diseases is given in [22]. This paper proposes a new strategy of Multi-Classifier System based on SVM for pattern recognition of wheat leaf diseases for higher recognition accuracy. Diseased leaf samples with Powdery Mildew, Rust Puccinia Triticina, Leaf Blight, Puccinia Striiformis were collected in the field and images were captured before a uniform black background. Three feature sets including color feature set, shape feature set and texture feature set were created for classification analysis. The proposed combination strategy was based on stacked generalization and included two-level structure: base-level was a module of three kinds of SVM-based classifiers trained by three feature sets and meta-level was one module of SVM- based decision classifier trained by meta-feature set which are generated through a new data fusion mechanism. Compared with other single classifiers and other strategy of classifier ensembles for wheat leaf diseases, this approach is more flexible and has higher success rate of recognition. 4. CONCLUSION The widespread availability of camera and camera embedded devices has created the new challenges in image processing to handle images captured through such devices. And process of detection and diagnose of disease from image has been an ongoing research area useful in development of several applications such as automated grading system, classification of product and diagnose and expert system of disease etc. one such a application which can help formers to know about diseases affecting their yield and prevent with expert suggestions. Such a systems require an automated method to extract diseased portion of plant, leaf, flower or fruit prior to further image analysis. Hence in this paper, a comprehensive survey of several image processing methods is presented. Despite of several techniques available for disease diagnose of plant/crop, more scope exists to develop computationally inexpensive, robust and high detection and recognition rate techniques. And
  • 9. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 44 also scope exists to further investigate use of wavelets, DCT and other transformation techniques for developing new methods. REFERENCES 1. Georgina Stegmayer , Diego H. Milone et al ,Automatic recognition of quarantine citrus diseases,Expert Systems with Applications 3512–3517 Elsevier B.V. 2013. 2. S. Arivazhagan, R. Newlin Shebiah , Methods for disease detection and recognition Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features, Agric Eng Int: CIGR Journal Vol. 15, No.211-217, 2013. 3. Jagadeesh Devdas Pujari1, Rajesh et al, Grading and Classification of Anthracnose Fungal Disease of Fruits based on Statistical Texture Features, International Journal of Advanced Science and Technology Vol. 52, March, 2013. 4. Pertot et al, Identificator:A web-based tool for visual plant disease identification, a proof of concept with a case study on strawberry, Computers and Electronics in Agriculture 84 (2012) 144–154 Elsevier B.V, 2012 5. Anyela Camargo , Juan P. Molina et al, Intelligent systems for the assessment of crop disorders, Computers and Electronics in Agriculture 85 , 1–7 Elsevier B.V, 2012 6. James S. Cope, David Corney et al, Plant species identification using digital marphometric, Computers and Electronics in Agriculture 85, 1–7 Elsevier B.V, 2012. 7. Santanu Phadikar , Jaya Sil , Asit Kumar Das, Rice diseases classification using feature selection and rule generation techniques Computers and Electronics in agriculture,90 ,76–85 Elsevier B.V,2012. 8. Anand.H.Kulkarni, Ashwin Patil R. K., Applying image processing technique to detect plant diseases, International Journal of Modern Engineering Research (IJMER) Vol.2, Issue.5, Sep-Oct. pp-3661-3664 ISSN: 2249-6645,2012. 9. Ravikant sinha, Pragya Pandey, Infection Analysis Using Colour Feature Texture Using Image processing, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August, pp.1861-1866,2012. 10. K. Padmavathi, Investigation and monitoring for leaves disease detection and evaluation using image processing, International Research Journal of Engineering Science, Technology and Innovation (IRJESTI) Vol. 1(3) pp. 66-70, June 2012 11. S. Phadikar, J. Sil, and A. K. Das, Classification of Rice Leaf Diseases Based on Morphological Changes, International Journal of Information and Electronics Engineering, Vol. 2, No. 3, May 2012 12. Sannakki S.S, Rajpurohit V.S., Nargund v.b, A hybrid intelligent system for automated pomegranate disease detection and grading, International Journal of Machine Intelligence ISSN: 0975–2927 & E-ISSN: 0975–9166, Volume 3, Issue 2, pp-36-44,2011. 13. Sabah Bashir, Navdeep Sharma, Remote Area Plant Disease Detection Using Image Processing, IOSR Journal of Electronics and Communication Engineering ISSN : 2278-2834 Volume 2, Issue 6 PP 31-34, Sep-Oct 2012. 14. A.Senthil Rajan “Image Processing Techniques for Diagnosing Paddy Disease” Proceedings of the World Congress on Engineering ,London, U.K. Vol –II,WCE 4 - 6, july,2012.
  • 10. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print), ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME 45 15. Mr. S. R. Pokharkar, Dr. Mrs. V. R. Thool, Early Pest Identification in Greenhouse Crops using Image Processing Techniques, International Journal of Computer Science and Network (IJCSN) Volume 1, Issue 3, June 2012 16. Debabrata Samanta, Prajna Paramita Chaudhury, Scab Diseases Detection of Potato using Image Processing, International Journal of Computer Trends and Technology- volume3,Issue1, 2012. 17. Sanjay B. Patil, Dr. Shrikant K. Bodhe, Leaf Disease Severity Measurement Using Image Processing, International Journal of Engineering and Technology Vol.3 (5), 297-301,2011. 18. Lili N.A, F. Khalid, N.M. Borhan, Classification Of Herbs Plant Diseases Via Hierarchical Dynamic Artificial Neural Network After Image Removal Using Kernel Regression Framework, International Journal on Computer Science and Engineering (IJCSE) ISSN: 0975-3397 Vol. 3 No. 1 Jan 2011. 19. H. Al-Hiary, S. Bani-Ahmad et al, Fast and Accurate Detection and Classification of Plant Diseases, International Journal of Computer Applications (0975 – 8887) Volume 17– No.1, March 2011. 20. Toran Verma, Susanta Kumar Satpathy, A Step towards Precision Farming of Rice Crop by Estimating Loss Caused by Leaf Blast Disease Using Digital Image Processing and Fuzzy Clustering, International Journal of Computer Trends and Technology- May to June Issue 2011. 21. A. Camargoa, J.S. Smithb, Image pattern classification for the identification of disease causing agents in plants, Computers and Electronics in Agriculture, 66 , 121– 125 Elsevier B.V,2009. 22. Yuan Tian1, Chunjiang Zhao, Multiple Classifier Combination for Recognition of Wheat Leaf Diseases, Intelligent Automation and Soft Computing, Vol. 15, No. X, pp. 1-10, 2009. 23. J.Rajarajan and Dr.G.Kalivarathan, “Influence of Local Segmentation in the Context of Digital Image Processing – A Feasibility Study”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 340 - 347, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 24. Garima Agarwal, Rekha Nair and Pravin Shrinath, “A Review of Plant Leaf Classification Features and Techniques”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 5, 2013, pp. 204 - 216, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.