Design and Implementation of an Efficient Rose Leaf Disease Detection using K-Nearest Neighbours
Swetharani K1, Vara Prasad2

1Swetharani K, Department of Computer Science & Engineering, B.M.S College of Engineering, V.T.U Belgaum, Bengaluru, Karnataka, India.
2Vara Prasad*, Department of Computer Science & Engineering,, B.M.S College of Engineering, V.T.U Belgaum, Bengaluru, Karnataka, India.

Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 21-27 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4213099320 | DOI: 10.35940/ijrte.C4213.099320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: 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.
Keywords: Plant Disease, Rose, Machine Learning, KNN, Classification.