Severity and Disease Affected Area Measurement in Paddy and Maize Crop Leaves using Image Processing Techniques

Large population of India is dependent on agriculture directly or indirectly for their livelihood. To achieve better yield and quality in agricultural products, it is good to diagnose crop diseases at very initial stages. Pathogens like fungi, bacteria, viruses and adverse environment are the main reasons for agricultural crop diseases. For sustainable agriculture, continuous observation of crops from early stage may play prominent role. Early stage crop disease detection and severity measurement is very significant as far as profit in good forming is concerned. Paddy and Maize are two major crops grown in the country which not only provide feeding but also give major contribution to GDP. In the proposed work, K-means clustering with fuzzy logic is adopted to measure the disease affected area of the leaf and in turn to determine the severity of the diseases. For this experiment numbers of Paddy and Maize diseased leaves are taken. Later the results are validated with ImageJ tool to check accuracy from the proposed method.