Open AccessDesign and Implementation of an Efficient Rose Leaf Disease Detection using K Nearest Neighbours
Author(s) -
K. Swetharani,
AUTHOR_ID,
Vara Prasad,
AUTHOR_ID
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4213.099320
Subject(s) - identification (biology) , rose (mathematics) , plant disease , computer science , key (lock) , process (computing) , artificial intelligence , machine learning , feature (linguistics) , supervised learning , pattern recognition (psychology) , biology , microbiology and biotechnology , artificial neural network , botany , horticulture , linguistics , philosophy , computer security , operating system
Plants are prone to different diseases caused by multiple reasons like environmental conditions, light, bacteria, and fungus. These diseases always have some physical characteristics on the leaves, stems, and fruit, such as changes in natural appearance, spot, size, etc. Due to similar patterns, distinguishing and identifying category of plant disease is the most challenging task. Therefore, efficient and flawless mechanisms should be discovered earlier so that accurate identification and prevention can be performed to avoid several losses of the entire plant. Therefore, an automated identification system can be a key factor in preventing loss in the cultivation and maintaining high quality of agriculture products. This paper introduces modeling of rose plant leaf disease classification technique using feature extraction process and supervised learning mechanism. The outcome of the proposed study justifies the scope of the proposed system in terms of accuracy towards the classification of different kind of rose plant disease.