利用花生荚果图像特征识别品种与检验种子

doi:10.3724/SP.J.1006.2012.00535

Abstract

Abstract: To verify the feasibility of peanut variety recognition and seed testing by pod image characteristics, we screened 20 peanut varieties mainly released in North peanut regions and collected 50 traits based on pod morphology, colour and texture. We used PCA data optimization, neural networks, support vector machine, and clustering analysis to discuss the vvarieties iidentification, origin recognition, DUS characters selecting method and vvarietiesclustering process. It has been discovered that the PCA optimization SVM model is better and its identification effect is stable. By this model, the variety recognition rate was above 90% for 20 vvarieties, and the correct origin recognition rate of three origins reached 100%. Additionally, we sorted out some useful traits for seeds DUS test from the 50 features and established the dendrogram of 20 peanut varieties. The results of this study provided some references valuable to the selection of DUS traits, peanuts varieties, origin recognition, and peanut pedigree research.

Key words: Peanuts variety recognition, Principal component analysis, Artificial neural network, Support vector machine, K-means clustering, DUS test

HAN Zhong-Zhi, ZHAO You-Gang. Variety Identification and Seed Test by Peanut Pod Image Characteristics[J].Acta Agron Sin, 2012, 38(03): 535-540.

References

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Variety Identification and Seed Test by Peanut Pod Image Characteristics

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