International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 8  (August 2017), Pages:  79-83

Title: Detection and classification of leaf diseases using integrated approach of support vector machine and particle swarm optimization

Author(s):  Prabhjeet Kaur *, Sanjay Singla, Sukhdeep Singh


Computer Science Engineering, IET Bhaddal Technical Campus, Rupnagar, India

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Plant diseases are one of the common factors responsible for the decrease in plant growth. Plant diseases are analyzed with their leaves. Many researchers have analyzed the different methods to detect the leaf diseases but the evaluated results are not appropriate enough. So, in this paper we have presented an integrated approach of particle swarm optimization (PSO) and support vector machine (SVM) for plant leaf disease detection and classification. Here, the disease affected dataset of plant leaves is considered that is suffered with four diseases Cercospora leaf spot, bacterial blight, anthracnose, and Alternaria alternata. The main objective of this paper is to detect the disease affected portion of leaf and healthy portion of leaf. We have calculated the percentage of leaf affected portion with their classification. Overall results are evaluated in the form of accuracy of proposed integrated approach. 

© 2017 The Authors. Published by IASE.

This is an open access article under the CC BY-NC-ND license (

Keywords: Leaf disease detection, Particle swarm optimization, K-means clustering, Support vector machine

Article History: Received 19 March 2017, Received in revised form 2 July 2017, Accepted 7 July 2017

Digital Object Identifier:


Kaur P, Singla S, and Singh S (2017). Detection and classification of leaf diseases using integrated approach of support vector machine and particle swarm optimization. International Journal of Advanced and Applied Sciences, 4(8): 79-83


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