Computer Aided Corn Leaf Disease Identification System

Automated corn leaf classification is important for the precise identification of corn leaf diseases and their type based on machine learning algorithms. Although, as mentioned above the manual detection of corn diseases is possible under a skilled botany exert. However, manual detection is extremely challenging and time-consuming process and always require a skilled pathologist to analyze plant behavior. As computer aided technologies and machine learning algorithms have widely evolved in recent time, especially, in the field of medical, defense, engineering, multimedia and agriculture. These techniques have the characteristics to process images and extract essential multimedia information from those images. Thus, segmentation and classification process are utilized to extract necessary information from the leaf images and to identify leaf disease and their type using that information. Further, utilization of machine learning techniques supports in detection of patterns for leaf images in computational way. Several researchers have shown immense interest in identification of varied diseases in leaf images through machine learning and computer aided techniques. In this paper, a computer aided corn leaf disease identification system is proposed for accurate disease identification system. The proposed model is compared with the traditional methods and the results show that the proposed model accuracy is better than the traditional models.