Fault Area Detection in Leaf Diseases using k-means Clustering

TL;DR

This paper presents a method combining k-means clustering and Otsu's thresholding to detect leaf diseases from images, aiding in early diagnosis and treatment decisions for crop health management.

Contribution

It introduces an efficient image processing approach for leaf disease detection using clustering and segmentation techniques, improving accuracy over traditional methods.

Findings

Effective detection of diseased leaf regions

Accurate ratio calculation for disease severity assessment

Potential for early disease prediction and treatment planning

Abstract

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.