3958 KB24 KB200020242015Latha, PitchaiMathukannan, Kanthanštevilka:3letnik:34str. 209-216ISSN:1580-3139COBISSID:32520409URN:URN:NBN:SI:doc-USAY6BP8enDruštvo za stereologijo in kvantitativno analizo slike, Medicinska fakultetaImage analysis and stereologydisease patternlistimodeliranje z rojem delcevparticle swarm optimizationperformance analysispridobivanje hibridnih značilkrastlinske boleznisegmentacija sliksegmentationstereologijatexture featureA PSO model for disease pattern detection on leaf surfaces|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 effectivenessTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaDruštvo za stereologijo in kvantitativno analizo slike