Wheat leaves need to be scouted routinely for early detection and recognition of rust diseases. This facilitates timely management decisions. In this paper, an integrated image processing and analysis system has been developed to automate the inspection of these leaves and detection of any disease present in them. Disease features of wheat leaves have been extracted using Fuzzy c-means Clustering algorithm and disease detection, recognition of its type and identification algorithm has been developed based on artificial neural network (ANN). Through the use of ANN and more specifically multilayer perceptrons, detection of the presence of disease in wheat leaves have been successful in 97% of the cases, after analysis of about 300 test images of wheat leaves. Also, identification of type of disease, if present, in wheat leaf has been successful in 85% of the cases.
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