Fault Area Detection in Leaf Diseases using k-means Clustering
This addresses food security issues by helping the agricultural industry detect crop diseases, but it is incremental as it applies existing image processing techniques to this domain.
The paper tackles the problem of leaf disease detection in crops by proposing a method using k-means clustering and Otsu's method to identify faulty regions in leaf images, with the potential to calculate the ratio of normal to faulty areas to predict curability.
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.In this paper, we have addressed that problem and proposed an efficient method to detect leaf disease.Leaf diseases can be detected from sample images of the leaf 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.