Advance computing enrichment evaluation of cotton leaf spot disease detection using Image Edge detection

Proposed Research work exposes, a advance computing technology has been developed to help the farmer to take superior decision about many aspects of crop developed process. Suitable evaluation and diagnosis of crop disease in the field is very critical for the increased production. Foliar is the major important fungal disease of cotton and occurs in all growing Indian cotton regions. In this work we express Technological Strategies using mobile captured symptoms of Cotton Leaf Spot images and categorize the diseases using neural network. The classifier is being trained to achieve intelligent farming, including early detection of disease in the groves, selective fungicide application, etc. This proposed work is based on Image Edge detection Segmentation techniques in which, the captured images are processed for enrichment first. Then R, G, B color Feature image segmentation is carried out to get target regions (disease spots). Later, image features such as boundary, shape, color and texture are extracted for the disease spots to recognize diseases and control the pest recommendation.