Computing Performance evaluation of Cotton leaves Spot diseases recognition using Image Segmentation

In this model of work, a new computing technology has been proposed to help the farmer to take superior decision about many aspects of crop manufacturing process. Suitable evaluation and diagnosis of crop diseases in field is very critical for the increased production. Foliar is the most 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 leaves spot images and classify the diseases using neural network. The classifier is being trained to achieve intelligent farming, including early detection of diseases in groves, selective fungicide application, etc. This proposed work is based on image pre-processing techniques in which, the captured images are processed for enhancement first. Then color image segmentation is carried out to get target regions (disease spots). Later, image features such as shape, color and texture are extracted for the disease spots to identify diseases and control the pest recommendation.