Noise Deduction in Novel Paddy Data Repository using Filtering Techniques

Abstract

Classification of paddy crop diseases in prior knowledge is the current challenging task to evolve the economic
growth of the country. In image processing techniques, the initial process is to eliminate the noise present in the dataset. Removing the noise leads to improvements in the quality of the image. Noise can be removed by applying filtering techniques. In this paper, a novel data repository created from different paddy areas in Vellore, which includes the following diseases, namely Bacteria Leaf Blight, Blast, Leaf Spot, Leaf Holder, Hispa and Healthy leaves. In the initial process, three kinds of noises, namely Salt and Pepper noise, Speckle noise, and Poisson noises, were removed using noise filtering techniques, namely Median and Wiener filter. The interpretation made over the median and Wiener filtering techniques concerning noises, the performance of the methods measured using metrics namely PSNR (peak to signal to noise ration), MSE (mean square error), Maxerr (Maximum squared error), L2rat (ratio of squared error). It is observed that the PSNR value of the hybrid approach is 18.42dB, which produces less error rate as compared with the traditional approach. Results suggest that the methods used in this paper are suitable for processing noise.