SEMI-AUTOMATIC LEAF DISEASE

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This is from 1CRORE PROJECT DEVELOPMENT AND TRAINING CENTER IN CHENNAI Here the clear explanation of SEMI-AUTOMATIC LEAF DISEASE DETECTION AND CLASSIFICATION SYSTEM FOR SOYBEAN CULTURE for more about this kindly check our ppt.

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Hello! Nice to meet you:

Hello! Nice to meet you 1 This is from 1crore project Development and Training Center in Chennai . It is one of the leading student academic activity institute. Contact us at: www.1croreprojects.com 1croreprojects@gmail.com @ 1croreprojects

SEMI-AUTOMATIC LEAF DISEASE DETECTION AND CLASSIFICATION SYSTEM FOR SOYBEAN CULTURE:

SEMI-AUTOMATIC LEAF DISEASE DETECTION AND CLASSIFICATION SYSTEM FOR SOYBEAN CULTURE

ABSTRACT:

ABSTRACT In Our project, a rule based semi-automatic system using concepts of k-means is designed and implemented to distinguish healthy leaves from diseased leaves. In addition, a diseased leaf is classified into one of the three categories (downy mildew, frog eye, and Sartorial leaf blight). Experiments are performed by separately utilising colour features, texture features, and their combinations to train three models based on support vector machine classifier. Results are generated using thousands of images collected from Plant Village dataset. Acceptable average accuracy values are reported for all the considered combinations which are also found to be better than existing ones. 3

EXITING METHOD:

EXITING METHOD 4 In Existing, a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each ieee 2018 android projects species consists of 48 images, so the total images used in this research are 2,448 images. This research investigates effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color . The fusion of FLBP and FCH is done by using Product Decision Rules (PDR) method. This research used Probabilistic Neural Network (PNN) classifier for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identifying and finding information about Indonesian medicinal plant . DISADVANTAGES: The most of existing methods has ignored the poor quality images like images with noise or poor brightness. Less accuracy.  

PROPOSED METHOD:

PROPOSED METHOD In Our proposed method, after preprocessing , Image is segmented using K-means clustering. Then GLCM(Gray Level Co- occurance Matrix) , Haralick and Gabor features are extracted and classified using SVM (Support Vector Machine) classifier . High accuracy is obtained and time consumption for detecting the shape. More datasets are included. 5 ADVANTAGES

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6 BLOCK DIAGRAM:

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MODULE DESCRIPTION Pre-processing Segmentation Feature extraction Classification Pre-processing Pre-processing is a common name for operations with images at the lowest level of abstraction both input and output are intensity images The aim of Pre-processing is an improvement of the image data that suppresses unwanted distortions or enhances some image  7

Segmentation:

Segmentation K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. The k-means clustering algorithm attempts to split a given anonymous data set (a set containing no information as to class identity) into a fixed number (k) of clusters. Initially k number of so called centroids are chosen. Each centroid is thereafter set to the arithmetic mean of the cluster it defines. K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed apriori . The main idea is to define k  centers , one for each cluster. 8 K-means clustering

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Feature extraction / selection Extracting and selecting some essential features is very needful for classification. Here we are using a GLCM (Gray level Occurrence matrix).   Classification Support vector machine In  machine learning , support vector machines (SVMs, also support vector networks) are  supervised learning  models with associated learning  algorithms  that analyze data used for  classification  and  regression analysis . Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non- probabilistic   binary   linear classifier  (although methods such as  Platt scaling  exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.   9

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10 SYSTEM REQUIREMENTS:   GENERAL: The system requirement of the project is described and the specification of the software and hardware requirements of the project is described. HARDWARE REQUIREMENTS: Processor Type : Pentium -IV Speed : 2.4 GHZ Ram : 128 MB RAM Hard disk : 20 GB HD SOFTWARE REQUIREMENTS Operating System : Windows 7 Software Programming Package : Matlab R2014b

REFERENCES::

REFERENCES: 11 [1] ‘Nitrogen fixation’, available at https://en.wikipedia.org/wiki/ Nitrogen_fixation , accessed February 2017 [2] Savary , S., Ficke , A., Aubertot , J.-N., et al.: ‘Crop losses due to diseases and their implications for global food production losses and food security’, Food Secur ., 2012, 4, (4), pp. 519–537 [3] ‘Diagnosing plant problems: plant diseases and disorders’, available at https://firstdetector.org/static/pdf/NPDNDiagnosingPlantProblemsPlantDiseaseforreview2.pdf, accessed February 2017 [4] ‘Signs and symptoms of plant disease: Is it fungal, viral or bacterial?’, available at http://msue.anr.msu.edu/news/ signs_and_symptoms_of_plant_disease_is_it_fungal_viral_or_bacterial, accessed January 2017 [5] Li, X., Yang, X.B.: ‘Similarity, pattern, and grouping of soybean fungal diseases in the United States: implications for the risk of soybean rust’, Plant Dis., 2009, 93, (2), pp. 162–169 [6] ‘Soybean growth and development’, available at http:// corn.agronomy.wisc.edu/Crops/Soybean/ pdfs /L004.pdf, accessed February 2017 [7] Mian , M.A., Missaoui , A.M., Walker, D.R., et al.: ‘Frogeye leaf spot of soybean: a review and proposed race designations for isolates of Cercospora sojina Hara’, Crop Sci., 2008, 48, (1), pp. 14–24

Thank you very much for your time:

Thank you very much for your time 12 If you have any questions about this document please don’t hesitate to contact us at : Contact us at: www.1croreprojects.com 1croreprojects@gmail.com @1croreprojects

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