Determining optimal wavebands using genetic algorithm for detection of internal insect infestation in tart cherry

  • Juan XingEmail author
  • Daniel Guyer
  • Diwan Ariana
  • Renfu Lu
Original Paper


This paper reports the results of waveband selection for detecting internal insect infestation in tart cherries as a precursor to development of a dedicated multispectral vision system. A genetic algorithm (GA) approach was applied on hyperspectral transmittance images (580–980 nm) and reflectance spectral data (590–1,550 nm) acquired from both intact and infested tart cherries. The GA analysis indicates that the ability of using transmittance imaging approach for detecting internal insect infestation in tart cherries would be limited. According to the GA analysis on the reflectance spectra, visible wavelengths were of less importance than NIR wavelengths for the purpose of distinguishing intact cherries from infested ones. The PLSDA results indicate that models built with three or four GA selected wavelength regions gave similar classification accuracy to the model built with full wavelength region, which demonstrates the efficiency of the GA variable selection procedure. However, due to the stochastic nature of the GA, the efficiency of using these wavebands in a multispectral vision system needs to be verified in future work.


Insect Detection Multispectral Wavebands selection Genetic algorithm 



The authors would like to thank the financial support of USDA-CSREES; Integrated Research, Education and Extension Competitive Grants Program.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Juan Xing
    • 1
    Email author
  • Daniel Guyer
    • 1
  • Diwan Ariana
    • 2
  • Renfu Lu
    • 2
  1. 1.Department of Biosystems and Agricultural EngineeringMichigan State UniversityEast LansingUSA
  2. 2.USDA-ARSEast LansingUSA

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