The Improved DBSCAN Algorithm Study on Maize Purity Identification - Computer and Computing Technologies in Agriculture V - Part II Access content directly
Conference Papers Year : 2012

The Improved DBSCAN Algorithm Study on Maize Purity Identification

Pan Wang
  • Function : Author
  • PersonId : 988293
Shuangxi Liu
  • Function : Author
  • PersonId : 988294
Mingming Liu
Qinxiang Wang
  • Function : Author
  • PersonId : 988295
Jinxing Wang
  • Function : Author
  • PersonId : 988296
Chunqing Zhang
  • Function : Author
  • PersonId : 988297

Abstract

In order to identify maize purity rapidly and efficiently, the image processing technology and clustering algorithm were studied and explored in depth focused on the maize seed and characteristics of the seed images. An improved DBSCAN on the basis of farthest first traversal algorithm (FFT) adapting to maize seeds purity identification was proposed in the paper. The color features parameters of the RGB, HIS and Lab color models of maize crown core area were extracted, while H, S and B as to be the effective characteristic vector after data analysis. The abnormal points of different density characteristic vector points were separated by FFT. Then clustering results were combined after local density cluster by DBSCAN. According to the result of test, the method plays a great role in improving the accuracy of maize purity identification.
Fichier principal
Vignette du fichier
978-3-642-27278-3_67_Chapter.pdf (4 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01361052 , version 1 (06-09-2016)

Licence

Attribution

Identifiers

Cite

Pan Wang, Shuangxi Liu, Mingming Liu, Qinxiang Wang, Jinxing Wang, et al.. The Improved DBSCAN Algorithm Study on Maize Purity Identification. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. pp.648-656, ⟨10.1007/978-3-642-27278-3_67⟩. ⟨hal-01361052⟩
56 View
100 Download

Altmetric

Share

Gmail Facebook X LinkedIn More