• Kanthan Muthukannan Assistant Professor/ECE Einstein College of Engineering Tirunelveli-627012 Tamilnadu http://orcid.org/0000-0003-0857-030X
  • Pitchai Latha Associate Professor/CSE Government College of Engineering Tirunelveli-627007 Tamilnadu




disease pattern, particle swarm optimization, performance analysis, segmentation, texture feature


The main objective of this paper is to segment the disease affected portion of a plant leaf and extract the hybrid features for better classification of different disease patterns. A new approach named as Particle Swarm Optimization (PSO) is proposed for image segmentation. PSO is an automatic unsupervised efficient algorithm which is used for better segmentation and better feature extraction. Features extracted after segmentation are important for disease classification so that the hybrid feature extraction components controls the accuracy of classification for different diseases. The approach named as Hybrid Feature Extraction (HFE), which has three components namely color, texture and shape based features. The performance of the preprocessing result was compared and the best result was taken for image segmentation using PSO. Then the hybrid feature parameters were extracted from the gray level co-occurrence matrices of different leaves. The proposed method was tested on different images of disease affected leaves, and the experimental results exhibit its effectiveness.


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How to Cite

Muthukannan, K., & Latha, P. (2015). A PSO MODEL FOR DISEASE PATTERN DETECTION ON LEAF SURFACES. Image Analysis and Stereology, 34(3), 209–216. https://doi.org/10.5566/ias.1227



Original Research Paper