Disease Detection of Cotton Leaves Using Advanced Image Processing

• Published in: International journal of advanced computer research 2014-06, Vol.4 (2), p.653-653
• Main Authors: Chaudhari, Vivek, Patil, C Y
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Color; Cotton; Image processing; Image segmentation; Segmentation
• ISSN: 2249-7277; 2277-7970

In this research, identification and classification of cotton diseases is done. The pattern of disease is important part, where some features, like the colour of actual infected image are extracted from image. There are so many diseases occurred on cotton leaf, so the leaf color is different for different diseases. This paper uses k-mean clustering with Discrete Wavelet Transform for efficient plant leaf image segmentation and classification between normal & diseased images using neural network technique. Segmentation is basic pre-processing task in image processing applications and it is required to extract diseased plant leaf from normal plant leaf image and image background. Image segmentation is necessary to detect objects and borderlines in images. Even though different methods are already proposed, it is still hard to accurately segment a random image by one specific method. In last years, additional attention has been given to merge segmentation algorithm and feature extraction algorithm, to enhance segmentation results.