Limiao Deng1, Zhongzhi Han1. 1. School of Science and Information Science, Qingdao Agriculture University, Qingdao, China.
Abstract
BACKGROUND: DUS (Distinctness, Uniformity and Stability) testing of new varieties is an important method for peanut germplasm evaluation and identification of varieties. In order to verify the feasibility of variety identification for peanut DUS testing based on image processing, 2000 peanut pod images from 20 varieties were obtained by a scanner. Initially, six DUS testing traits were quantified using a mathematical method based on image processing technology, and then, size, shape, color and texture features (total 31) were also extracted. Next, the Fisher algorithm was used as a feature selection method to select 'good' features from the extracted features to expand the DUS testing traits set. Finally, support vector machine (SVM) and K-means algorithm were respectively used as recognition model and clustering method for variety identification and pedigree clustering. RESULTS: By the Fisher selection method, a number of significant candidate features for DUS testing were selected which can be used in the DUS testing further; using the top half of these features (about 18) ordered by Fisher discrimination ability, the recognition rate of SVM model was found to be more than 90%, which was better than unordered features. In addition, a pedigree clustering tree of 20 peanut varieties was built based on the K-means clustering method, which can be used in deeper studies of the genetic relationship of different varieties. CONCLUSION: This article can provide a novel reference method for future DUS testing, peanut varieties identification and study of peanut pedigree.
BACKGROUND: DUS (Distinctness, Uniformity and Stability) testing of new varieties is an important method for peanut germplasm evaluation and identification of varieties. In order to verify the feasibility of variety identification for peanut DUS testing based on image processing, 2000 peanut pod images from 20 varieties were obtained by a scanner. Initially, six DUS testing traits were quantified using a mathematical method based on image processing technology, and then, size, shape, color and texture features (total 31) were also extracted. Next, the Fisher algorithm was used as a feature selection method to select 'good' features from the extracted features to expand the DUS testing traits set. Finally, support vector machine (SVM) and K-means algorithm were respectively used as recognition model and clustering method for variety identification and pedigree clustering. RESULTS: By the Fisher selection method, a number of significant candidate features for DUS testing were selected which can be used in the DUS testing further; using the top half of these features (about 18) ordered by Fisher discrimination ability, the recognition rate of SVM model was found to be more than 90%, which was better than unordered features. In addition, a pedigree clustering tree of 20 peanut varieties was built based on the K-means clustering method, which can be used in deeper studies of the genetic relationship of different varieties. CONCLUSION: This article can provide a novel reference method for future DUS testing, peanut varieties identification and study of peanut pedigree.