Grape leaf disease detection System

Abstract

It is difficult for human eye to identify the exact form of leaf disease which occurs on the leaf of plant.
Thus, in order to identify the leaf diseases accurately, the use of image processing and machine learning techniques
can be helpful. The images used for this work were acquired from the cotton field using digital camera. In preprocessing step, background removal technique is applied on the image in order to remove background from the
image. Then, the background removed images are further processed for image segmentation using Otsu thresholding
technique. Different segmented images will be used for extracting the features such as color, shape and texture from
the images. At last, these extracted features will be used as inputs of classifier. Plant diseases cause significant
damage and economic losses in crops. Subsequently, reduction in plant diseases by early diagnosis results in
substantial improvement in quality of the product. Erroneous diagnosis of disease and its severity leads to
inappropriate use of pesticides. The goal of proposed work is to identify the disease with image processing of grape
plant leaf. In the proposed system, grape leaf image with complex background is taken as input. Thresholding is
deployed to mask green pixels and image is processed to remove noise using anisotropic diffusion. Then grape leaf
disease segmentation is done. The diseased portion from segmented images is identified.