Plant Disease Classification And Identification Applying CNN
The Proposed work using Image Processing to predict the use of the given wheat crop for finding disease and suggest pesticides. The algorithm is designed to make a thorough analysis of the wheat crop’s leaf in the given input photographed image to find the areas that have been affected by the disease and then doe’s further analysis to find the disease and pesticide. The proposed decision making system utilizes image content characterization and supervised classifier type of neural network Image processing techniques for this form of decision analysis include Gaussian Blurring, Bilateral Filtering, K-means clustering, GLCM, NN classification using MLP, COM and Luminance classification.The system will be used to classify the test images automatically to decide if the crop is affected by disease or not and if it is, then by which disease and what pesticide suggestion is to be given. The accuracy of our project lies at 82 %, an elevated percentage due to the use of MLP NN classifier.