Open AccessClassification and Functional Analysis of Major Plant Disease using Various Classifiers in Leaf Images
Author(s) -
Kapilya Gangadharan,
Gitanjali Kumari,
D. Dhanasekaran
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b6332.129219
Subject(s) - plant disease , identification (biology) , categorization , crop productivity , artificial intelligence , computer science , plant identification , crop , image processing , pattern recognition (psychology) , machine learning , microbiology and biotechnology , biology , agronomy , image (mathematics) , botany
Plant disease interrupts the normal or Ordinary condition of a plant and it alters the essential functionality of a plant. Which intern impacts the productivity of the crop. Speedy observation, recognition and categorization of the plant pathogens will increase the crop yield more than 60% of the total productivity. Disease analysis is more evident on the leaves when compare to the other parts of the plants. Automated methods are most commonly available in different image processing techniques to detect the pathogen attack which can be made more efficient by combining multiple domain, that utilizes computer vision technologies. Most modern techniques or technologies are analyzed to identify the various disease on several crops or crop types. The paper summarizes about types of plants, types of plant diseases and the standard methodologies or technique that would help gaining knowledge about Computer Vision and its applications on plant disease identification and classification. Performance of the Classifiers are analyzed to recognize and classify the better method that typically works among different plant groups and different types of pathogen attack.