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In the field of agriculture, there is need of recognizing as well as classifying diseases from leaf images that are taken from plant. Finding the diseases of paddy leaf by making use of image processing mechanism would reduce the reliance on farmers in order to save the product related to agricultural activity. This research paper is finding and categorizing the disease in paddy leaf with the help of CNN with integration of edge detection mechanism. The proposed work is focusing on the classification of paddy leaf on the basis of the brown spot, bacterial blight, blast disease and sheath rot after spot detection. However, there have been some previous researches to fulfill such objectives but the proposed work used edge detection mechanism to reduce the time consumption as well as space consumption.Leaves of the rice plant have been captured from the field for the normal, brown spot, bacterial blight, blast diseases and sheath rot. During the pre-processing operation RGB images have been converted in HSV images. Image processing is made on the basis of hue and saturation. The portions of binary graphical contents have been captured to split the infected and non-infected portion. A clustering mechanism has been used for segmentation of the infected portion. However, there are several existing researches that have classified diseases with the support of optimized DNN_JOA (Deep Neural Network with Jaya Optimization Algorithm). Time and space consumption are the major issue in those researches. There is need to provide the solution to improve the performance and space consumption thus proposed work is making use of deep neural network with integration of canny edge detection. In this paper, pattern detection using edge based CNN (convolution neural network) algorithm is proposed