期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2016
卷号：14
期号：1
页码：254-261
DOI：10.12928/telkomnika.v14i1.2486
语种：English
出版社：Universitas Ahmad Dahlan
摘要：This research aims to analyze morphological techniques for feature extraction of Jabon’s leaf seedling pathogen using digital microscopic image. The kinds of the pathogen were Curvularia sp., Colletotrichum sp., and Fusarium sp.. Pathogens or causes of disease were identified manually based on macroscopic and microscopic observation of morphological characters. Morphological characters describe the characteristics of shape, color and size of a pathogen structure. We focused on shape feature by using the morphological techniques to feature extraction. The morphology features extraction used were area, perimeter, convex area, convex perimeter, compactness, solidity, convexity, and roundness. The methodologies were acquisition, preprocessing, features extraction and data analysis for derivative features. With features extraction, we got the pattern that described each pathogen for pathogen identification. From the experimental result showed that compactness and roundness feature were able to differentiate each pathogen due to that the characteristics of each pathogen class were separated.
其他摘要：This research aims to analyze morphological techniques for feature extraction of Jabon’s leaf seedling pathogen using digital microscopic image. The kinds of the pathogen were Curvularia sp., Colletotrichum sp., and Fusarium sp.. Pathogens or causes of disease were identified manually based on macroscopic and microscopic observation of morphological characters. Morphological characters describe the characteristics of shape, color and size of a pathogen structure. We focused on shape feature by using the morphological techniques to feature extraction. The morphology features extraction used were area, perimeter, convex area, convex perimeter, compactness, solidity, convexity, and roundness. The methodologies were acquisition, preprocessing, features extraction and data analysis for derivative features. With features extraction, we got the pattern that described each pathogen for pathogen identification. From the experimental result showed that compactness and roundness feature were able to differentiate each pathogen due to that the characteristics of each pathogen class were separated.
