reasons that disease detection in plants plays an important role in the agricultural field

reasons that disease detection in plants plays an important role in the agricultural field.If there is decrease in agro product, total economy will affected . Diseases in crops mostly on the leaves affects on reduction of both quality and quantity of agricultural products. Perception of human eye is not so much stronger so as to observe minute variation in the infected of leaf. In recent decades, digital image processing, image analysis and machine vision have been sharply developed, and they have become a very important part of artificial intelligence and the interface between human and machine grounded theory and applied technology. These technologies have been applied widely in industries, medicine and agriculture. As part of this project, the elaboration of such an application has been attempted. In this paper, we are and 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.
INTRODUCTION:
In India, Farmers have a great diversity of crops. The old and classical approach for detection and recognition of plant diseases is based on naked eye observation, which is very slow method also gives less accuracy. In India , especially in rural areas consulting experts to find out plant disease is expensive and time consuming due to availability of expert. A large team of experts as well as continuous monitoring of experts is required, which costs very high when farms are large.. Also unnecessary use of pesticides might be dangerous for natural resources such as water, soil, air, food chain etc. as well as it is expected that there need to be less contamination of food products with pesticides. Automatic detection of plant diseases is essential to detect the symptoms of diseases in early stages. The main identification of the affected plant or crop are its leaves. ome general diseases are brown and yellow spots, or early and late scorch, and other are fungal, viral and bacterial diseases. Image processing is the technique which is used for measuring affected area of disease, and to determine the difference in the color of the affected area.
The process of separating or grouping an image into different parts is called Image Segmentation. There are currently many different ways of performing image segmentation, ranging from the simple thresholding method to advanced color image segmentation methods. These parts normally correspond to something that humans can easily separate and view as individual objects. Computers have no means of intelligently recognizing objects, and so many different methods have been developed in order to segment images. The segmentation process in based on various features found in the image. This might be color information, boundaries or segment of an image
The MATLAB image processing starts with capturing of digital high resolution images. Healthy and unhealthy images are captured and stored for experiment. Then images are applied for pre-processing for image enhancement. Captured leaf & fruit images are segmented using k-means clustering method to form clusters. Features are extracted before applying K-means and SVM algorithm for training and classification. Finally diseases are recognised by this system.
LITERATURE REVIEW:
PROPOSED METHODOLOGY:
In image processing, it is defined as the action of retrieving an image from some source, usually a hardware-based source for processing. The images of the plant leaf can be acquired using two ways. Image acquisition is the first method of digital image processing and it is described as capturing the image through digital camera and stores it in digital media for further MATLAB operations. It is also an action of retrieving an image from hardware, so it can be passed through further process.
Next step is image preprocessing technique. Pre-processing is a common name for operations with images at the lowest level of abstraction — both input and output are intensity images. These iconic images are of the same kind as the original data captured by the sensor, with an intensity image usually represented by a matrix of image function values (brightnesses). The aim of pre-processing is an improvement of the image data that suppresses unwilling distortions or enhances some image features important for further processing. Image preprocessing techniques such as contrast enhancement, image smoothening, image slicing are applied to reduce the noise present in the image.
Third step is image segmentation. Segmentation means partitioning of images into various part of the same feature or having some similarity. K-means clustering is a partitioning method. The function ‘kmeans’ partitions data into k mutually exclusive clusters, and returns the index of the cluster to which it has assigned each observation. Unlike hierarchical clustering, k-means clustering operates on actual observations (rather than the larger set of dissimilarity measures), and creates a single level of clusters. The distinctions mean that k-means clustering is often more suitable than hierarchical clustering for large amounts of data. K-means treats each observation in your data as an object having a location in space. It finds a partition in which objects within each cluster are as close to each other as possible, and as far from objects in other clusters as possible.
The segmented image is then feature extracted. Feature extraction is used for identification of an object. The features such as color, texture and Morphology can be used for plant disease detection. Morphological results give better results than other features. Morphological operation mean that shape of the image can be modified. Commonly used morphological operations are erosion and dilation. After applying morphological operation edge detection technique is used for obtaining edges or border of the image. We used sobel operator for edge detection of the image. Feature extraction of image is helpful for easy detection of disease in the leaf.
Finally for classification and detection of disease in the leave we used SVM algorithm. “Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiate the two classes very well. Maximizing the distances between nearest data point (either class) and hyper-plane will help us to decide the right hyper-plane. This distance is called as Margin. It works really well with clear margin of separation.It is effective in high dimensional spaces and effective in cases where number of dimensions is greater than the number of samples.It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. Intuitively, a good separation is achieved by the hyper plane that has the largest distance to the nearest training-data point of any class
Block diagram of proposed methodology of automatic plant disease detection.

CONCLUSION:-
The accurate detection and classification of disease affected leaf using digital image processing techniques and MATLAB software by implementing k means clustering and svm algorithm made it possible to automatically detect the plant disease.

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