dc.description.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. |
en_US |