Deep Learning Regarding Virus Diagnosis Through Modified Segmentation Technique

Abstract

A deep learning technique is used for plant virus diagnosis and it is a process of artificial neural network that consists of overall 3 layer. Plant disease detection is discussed using models from AI and ML. These traditional ML techniques have been used in the field of detection of plant virus widely. The proposed system is efficient computationally due to the use of mostly statically image processing through modified segmentation with modified watershed algorithm and its deep learning model. Many parts of the world are still facing problems in agriculture. One of the major problem arise in agriculture is disease detection. To overcome this problem, many researches were done on this concept using the advantages of various learning Techniques. Though these researches were resulted with limitations in early identification of plant diseases. So, this proposed CNN model via WUDHOA and modified segmentation approach with severity estimation procedure was efficiently worked on tomato plant disease classification. The proposed CNN model via WUDHOA and modified segmentation approach with severity estimation procedure was not only identify the diseases but also focused on the estimation of tomato plant disease severity.