Machine vision detection of tetrazolium staining in corn

W. Xie, Marvin R Paulsen

Research output: Contribution to journalArticle

Abstract

A machine vision algorithm was developed to detect and quantify tetrazolium staining in sectioned corn kernels. The algorithm used two ratios: the sound area to the whole kernel, and the sound area to the approximate embryo area. The predicted seed viability compared favorably with actual tetrazolium and warm germination tests. The algorithm was used to predict heat damage to corn viability due to drying air temperature and initial corn moisture contents. The machine vision based tetrazolium test was able to predict viability loss and therefore detrimental effects of heat on corn to be used for wet milling. Corn harvested at 20% and 25% moisture was negatively affected by drying at 70°C. Corn harvested at 30% moisture was negatively affected by heat at all drying temperatures above 25°C, and was much more severely affected as drying temperature increased.

Original languageEnglish (US)
Pages (from-to)421-428
Number of pages8
JournalTransactions of the American Society of Agricultural Engineers
Volume44
Issue number2
StatePublished - Jan 1 2001

Keywords

  • Corn
  • Germination
  • Image processing
  • Machine vision
  • Viability

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)

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