International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 04 | Apr-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 2330 Grape Leaf Disease Detection Using K-means Clustering Algorithm Rupali Patil 1 , Sayali Udgave 2 ,Supriya More 3 , Dhanashri Nemishte 4 Monika Kasture 5 123 BE, Department of Computer Science And Engineering, Sau,Sushila Danchand Ghodawat Charitable Trust’s Sanjay Ghodawat Group of Institutions , Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract This paper present survey on different classification techniques that can be used for plant leaf disease classification. A classification technique deals with classifying each pattern in one of the distinct classes. A classification is a technique where leaf is classified based on its different morphological features. There are so many classification techniques such as k-mean clustering, Support Vector Machine. Selecting a classification method is always a difficult task because the quality of result can vary for different input data. Plant leaf disease classifications have wide applications in various fields such as in biological research, in Agriculture etc. Plant disease detection is emerging field in India as agriculture is important sector in Economy and Social life. Earlier unscientific methods were in existence. Gradually with technical and scientific advancement, more reliable methods through lowest turnaround time are developed and proposed for early detection of plant disease. Such techniques are widely used and proved beneficial to farmers as detection of plant disease is possible with minimal time span and corrective actions are carried out at appropriate time. The detection of plant disease is significantly based on type of family plants and same is carried out in two phases as segmentation and classification. Key Words: k-mean clustering, Segmentation, preprocessing, feature extraction. 1.INTRODUCTION Agriculture is not only to feed ever growing population but it’s also important source of energy . Plant diseases affect both quality and quantity of crops in agriculture production. Plant disease diagnosis is very essential in earlier stage in order to prevent and control them. The naked eye observation of experts is the main approach adopted in for detection and identification of plant diseases. But the naked eye observation is time consuming, expensive and take lots of efforts. To remove drawbacks in existing system many system have been proposed to overcome those drawbacks by using different techniques. In the next section this paper tries to present those proposed systems in meaningful way. The management of crops required close inspection especially for management of disease infected crop that can affect the quality and quantity of crop. Image processing is an best technique for agricultural application. Image processing can detect an pest’s attack from the image of plant. The detection and classification of plant diseases are important task to increase plant productivity. There are various techniques emerged to detect the plant disease such as thresholding, region growing, clustering, Edge based detection etc. To detect plant disease the image should go through some process like pre-processing, segmentation, feature extraction and classification processes. The pre-processing is an improvement process of image data to suppresses unwanted distortion or enhances some image features important for further processing [1]. The segmentation process is to partition an image into meaningful regions and it is vital process through which image features are extracted. There are various features of an image such as grey level, color, texture, shape, depth, motion, etc. Classification process is used to classify the given input data into number of classes and groups. It classifies the data based upon selected features [2]. Ajay A.Gurjar,Viraj A.Gulhane describes Eigen feature regularization and extraction technique by this detection of three diseases can be done. This system is having more accuracy, than that of the other feature detection techniques. With this method about 90% of detection of Red spot i.e. fungal disease is detected [2]. Dheeb Al Bashish& et al.proposed image processing based work is consists of the following main steps : In the first step the acquired images are segmented using the K-means techniques. In [4], diagnosis system for grape leaf diseases is proposed. The proposed system is composed of three main parts: Firstly Segmentation, secondly grape leaf disease feature extraction and finally grape leaf disease classification In [6], Tushar H Jaware & et al. developed a Fast and accurate method for detection and classification of plant diseases. The proposed algorithm is tested on main five diseases on the plants; they are: Early Scorch, Cottony mold, Ashen Mold, Late scorch, tiny whiteness. Initially the RGB image is acquired then a color transformation structure for the acquired RGB leaf image is created. After that color values in RGB converted to the space specified in the color transformation structure.