Selection of spectum feature wavelength and recognition of different ages of manilensis

Lin Li, Mingming Zhao, Zhu Wang, Fan Peng, Dehai Zhu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Manilensis is one of the major pests in China. A method for recognizing different ages of manilensis was presented based on K-means clustering and principal component analysis (PCA) with selected feature wavelength. The hyperspectral images in the range of 400~1 000 nm of manilensis back at differnet ages among adult, 5-age, 4-age and 3-age were collected and the average spectral information of target region on manilensis back with the size of 15 pixel×15 pixel was extracted. A wavelength secleting method with combined PCA algorithm and K-means clustering (K-PCA) was proposed. The model for identifying manilensis ages was built by using Fisher algorithm and then compared with K-PCA algorithm and successive projections algorithm (SPA). The experiment results showed that the K-PCA algorithm needed fewer wavelengths but with the higher accuracy of 98.25%. The final feature wavelengths of K-PCA algorithm were 468 nm, 555 nm, 635 nm, 710 nm, 729 nm, 750 nm, 786 nm and 899 nm. The proposed method provides a certain technology support for manilensis monitoring and precention.

Original languageEnglish (US)
Pages (from-to)249-253
Number of pages5
JournalNongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Volume47
Issue number3
DOIs
StatePublished - Mar 25 2016

Keywords

  • Characteristic wavelength
  • Hyperspectral image
  • K-means clustering
  • Manilensis
  • Principal component analysis

Fingerprint Dive into the research topics of 'Selection of spectum feature wavelength and recognition of different ages of manilensis'. Together they form a unique fingerprint.

Cite this