PRECISION AGRICLUTURE-CLASSIFICATION WEED FROM PADDY USING IMPROVED CNN

Abstract

Rice is a primary food and Encounter an Essential role in providing food security worldwide. However the existing Disease diagnosis method for rice are neither accurate nor efficient and special equipment is often required .In this study, The disease classification is done by SVM classifier and therefore the detection accuracy is improved by optimizing the info exploitation .In this proposed system we are using image processing techniques to classify disease. This Approach will enhance productivity crops. Furthermore in precision agriculture, the accurate segmentation crops and weeds has been always been the center of attention. This work proposes a segmentation method based on combination of semantic segmentation and K means algorithm for segmentation crop and weeds in color image. The proposed algorithm provided more accurate segmentation in comparison of other method with the maximum accuracy of equivalent to 99.19% the result indicate that the proposed method successfully provided accurate result for the segmentation of crop and weed in the image with a complex presence of weed.