Classification and Functional Analysis of Major Plant Disease using Various Classifiers in Leaf Images
Kapilya Gangadharan1, G. Rosline Nesa Kumari2, D. Dhanasekaran3
1Kapilya Gangadharan*, Research Scholar in Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai. She is Currently Utility Analyst at Fidelity National Information Services, Durham, NC, USA
2G. Rosilne Nesa Kumari, Professor in the Department of Computer Science and Engineering, in Shadan women’s college of Engineering and Technology, Chennai, India
3D. Dhanasekaran, Professor in the Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 4240-4248 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6332129219/2019©BEIESP | DOI: 10.35940/ijitee.B6332.129219
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Abstract: 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.
Keywords Classification, Neural Network, Optimization, Accuracy, Efficient
Scope of the Article: Classification