The accurate identification of disease symptoms in plants using image processing techniques is a major scientific priority. The creation of a reasonable system for diagnosing plant diseases that can aid producers in their agriculture & growing is urgently required. With the help of cotton plant leaves, this initiative aims to create a framework for the diagnosis of plant diseases. This research presents a method for removing noise from images of cotton plant leaves using hybridized filtering. The proposed architecture combines morphological procedures, including Wiener & Median filters in a modified form. Using morphological operation, the image's perimeter & structure are recovered. Modified median filtering & wiener filters were employed to reduce & improve the noise. The proposed approach is used to calculate the variable's Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Signal Noise Ratio (SNR). The final findings show that the proposed strategy is doing effectively with the upgraded grade.

Image Processing; Noise Removal; Hybrid Filter; Cotton Plant