Color classifier for symptomatic soybean seeds using image processing

Irfan S. Ahmad, John F. Reid, Marvin R. Paulsen, James B. Sinclair

Research output: Contribution to journalArticlepeer-review

Abstract

Symptoms associated with fungal damage, viral diseases, and immature soybean (Glycine max) seeds were characterized using image processing techniques. A Red, Green, Blue (RGB) color feature-based multivariate decision model discriminated between asymptomatic and symptomatic seeds for inspection and grading. The color analysis showed distinct color differences between the asymptomatic and symptomatic seeds. A model comprising six color features including averages, minimums, and variances for RGB pixel values was developed for describing the seed symptoms. The color analysis showed that color alone did not adequately describe some of the differences among symptoms. Overall classification accuracy of 88% was achieved using a linear discriminant function with unequal priors for asymptomatic and symptomatic seeds with highest probability of occurrence. Individual classification accuracies were asymptomatic 97%, Alternaria spp. 30%, Cercospora spp. 83%, Fusarium spp. 62%, green immature seeds 91%, Phomopsis spp. 45%, soybean mosaic potyvirus (black) 81%, and soybean mosaic potyvirus (brown) 87%. The classifier performance was independent of the year the seed was sampled. The study was successful in developing a color classifier and a knowledge domain based on color for future development of intelligent automated grain grading systems.

Original languageEnglish (US)
Pages (from-to)320-327
Number of pages8
JournalPlant disease
Volume83
Issue number4
DOIs
StatePublished - Apr 1999

Keywords

  • Feature space
  • Grain quality
  • Machine vision

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Plant Science

Fingerprint Dive into the research topics of 'Color classifier for symptomatic soybean seeds using image processing'. Together they form a unique fingerprint.

Cite this