A HYBRID INTELLIGENT SYSTEM FOR AUTOMATED POMEGRANATE DISEASE DETECTION AND GRADING

Abstract

This paper proposes an image processing methodology to address one of the core issues of plant pathology i.e. disease identification and its grading. The proposed system is an efficient module that identifies various diseases of pomegranate plant and also determines the stage in which the disease is. The system employs various image processing and machine learning techniques. At first, the captured images are processed for enhancement. Then 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. These resultant features are then given as input to disease classifier to appropriately identify and grade the diseases. Finally, based on the stage of the disease, the treatment advisory module can be prepared by seeking agricultural experts, there by helping the farmers.