Disease Detection System for Grape leaf

Computer Engineering

It is challenging for human eye to detect 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 pre-processing 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 sound using anisotropic diffusion. Then grape leaf sickness segmentation is done. The diseased serving from segmented images is recognized.

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