Title :
Study on Vegetable Seed Electrophoresis Image Classification Method
Author :
Jing, Liu ; Renshi, Yu ; Kai, Xiong ; Zhongzhi, Han
Author_Institution :
Coll. of Inf. Sci., Qingdao Agric. Univ., Qingdao, China
Abstract :
To verify the performance of crop classification and variety clustering based on electrophoretogram and investigate vegetable seed genetic relationships, three kinds of breeder´s seeds samples are collected. They are bell pepper, Chinese cabbage and cucumber. 30 varieties of each kind of crop have been collected. And standard electrophoretograms of them are prepared by method of protein ultra thin isoelectric focusing electrophoresis. Then, the digital images of them are obtained by scanners. Using these images, a pattern recognition model of crop recognition based on PCA and SVM are constructed. Test results by leaving-one method show that, more than 97% crops can be recognized correctly. In addition, through k-means clustering analysis, clustering trees of these 3 kinds of crops with different varieties are established and the paternities of partial varieties based on clustering tree are discussed. This study has positive significance to automatic seed inspection by computer based on electrophoresis and selection of breeding direction.
Keywords :
agriculture; crops; electrophoresis; image classification; inspection; pattern clustering; principal component analysis; support vector machines; trees (mathematics); Chinese cabbage; PCA; SVM; automatic seed inspection; bell pepper; breeder seed; breeding direction selection; clustering tree; crop classification; crop recognition; cucumber; electrophoresis image classification method; electrophoretogram; k-means clustering analysis; leaving-one method; partial variety; pattern recognition model; principal component analysis; protein ultra thin isoelectric focusing electrophoresis; support vector machines; variety clustering; vegetable seed electrophoresis; vegetable seed genetic relationship; Agriculture; Focusing; Genetics; Principal component analysis; Proteins; Support vector machines; Vegetation; Images classification; K-means clustering; Principal component analysis (PCA); Protein ultrathin-layer isolectric focusing; Support vector maching (SVM);
Conference_Titel :
Information and Computing Science (ICIC), 2012 Fifth International Conference on
Conference_Location :
Liverpool
Print_ISBN :
978-1-4673-1985-0
DOI :
10.1109/ICIC.2012.55