Vegetable disease detection using k-means clustering and svm

Abstract :  India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. Image processing technique is helpful in examination and evaluating the products. In this paper we proposed a vegetable disease detection system for recognizing diseased vegetables. Here we utilize the Image processing system for reviewing the vegetables. Vegetables are recognized dependent on their features. The features are color, shape, size, texture. We extract these features utilizing algorithms to distinguish the vegetables. We develop a recognition system for 2D input images. The main aim of this work is detecting infected vegetable based on features with K-means clustering algorithm. Algorithm includes three main steps namely enhancement, segmentation and classification. Vegetable samples are collected as images from high resolution camera and data acquisition is carried out for database preparation. The image segmentation process is based on pixel of the image and is applied to get the segmented and infected vegetables using K-Means Clustering algorithm. The image classification is based on Support Vector Machine (SVM) which perform supervised leaning for classification.
 EXISTING SYSTEM :
 ? With increasing population the crisis of food is getting bigger day by day. ? In this time of crisis, the leaf disease of crops is the biggest problem in the food industry. ? We used the unsupervised learning method by implementing the k-means clustering algorithm for segmentation of the faulty region in leaves which is way more accurate than the existing methods.
 DISADVANTAGE :
 ? Crop diseases have turned into dilemma because it may cause reduction in productivity. ? Generally through the naked eyes the observations taken by the Experts ancient time for the detection and identification of crop diseases. ? But for this the continuous monitoring is required by the Experts and It is too expensive in large fields.
 PROPOSED SYSTEM :
 • In this paper, we have addressed that problem and proposed an efficient method to detect leaf disease. • Leaf diseases can be detected from simple images of the leaves with the help of image processing and segmentation. • Using k-means clustering and Otsu’s method the faulty region in a leaf is detected which helps to determine proper course of action to be taken. • Further the ratio of normal and faulty region if calculated would be able to predict if the leaf can be cured at all.
 ADVANTAGE :
 • Using k-means++ algorithm at first random two centers or pixels are chosen from the infected leaf. • The centers represent the faulty and faultless regions of the leaf. It is done based on similar kind of featured weights. • It is done to identify the infected cluster by a specific type of disease, of the sample leaf. • Now the for all the pixels the nearest center is calculated and assigned to the corresponding centers • At this stage the new two centers are calculated using the assigned pixels and the algorithm goes back to the previous step. This iterative process is followed till the centers stabilize.

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