Paddy Disease Detection Using Image Processing

Abstract

Being an agricultural country, most of the people of Bangladesh are dependent on agriculture directly or indirectly. It is the fourth largest rice producing country in the world. Main hindrance in rice production is paddy diseases. So in this research the main objective is to develop a prototype system for detecting the paddy diseases, which are Paddy Blast, Brown Spot and Narrow Brown Spot diseases. This concentrate on the image processing techniques used to find pattern in the image and artificial neural network technique to classify the diseases. The methodology involves image collection, image processing, feature extraction and classification. Features are extracted from the images using Haralick’s texture feature from color co-occurrence matrix. Then an artificial neural network is trained by these features and a trained model is found. In testing phase, all paddy samples are passed through the leaf color analysis to detect the normal paddy leaf image. If the sample passes leaf color analysis, then it is automatically classified as Normal Paddy leaf image. Otherwise, all the segmented paddy disease samples are converted into the features data and is passed through the artificial neural network. Consequently, by employing the artificial neural network technique, the paddy diseases are recognized. The accuracy to detect diseases of this model is good enough to use in practical life.