Productivity of the crops is affected due to diseases. Traditional disease diagnosis system is very time consuming in which pathologist carried out experimentation in the laboratory. Hence it is needed to produce the system which diagnosis the disease accurately and fast with the help of the technology. Identifying disease of crops in its early stage is a major challenge in front of researchers. Many machine learning algorithms and image processing techniques are applied to efficiently identify the disease based on the symptoms that appeared on the leaves. In this paper, the infected leaf is segmented using the Kmeans clustering algorithm and further the 12 texture features are extracted from the segmented image. The backpropagation (BP) algorithm is used for identifying the disease. Here two versions of the backpropagation i.e. online BP and batch BP are used. The Pomegranate infected leaf image database is used for the experimentation purpose. It is observed that online BP performance is better as compared to the batch BP.

Leaf disease, Agriculture,Backpropagation neural network

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