Automated segmentation of powdery mildew disease from cherry leaves using image processing

An automated detection of plant disease is an important task to find features or abnormalities in plant and its effect on the fruits. In this paper an algorithm is proposed for detection of powdery mildew disease from a cherry leaf images. The proposed method uses an automated strategic removal of background from the image and then extracting the desired diseased portion. A combination of morphological operators and intensity based thresholding are used which creates a method computationally efficient and less complex. A set of public arXiv e-prints data are used to test the proposed algorithm. The tested algorithm achieves accuracy of 99%.