Image analysis applications for grain science

Inna Y. Zayas; James L. Steele

doi:10.1117/12.25086

1 February 1991 Image analysis applications for grain science

Inna Y. Zayas, James L. Steele

Proceedings Volume 1379, Optics in Agriculture; (1991) https://doi.org/10.1117/12.25086
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

Morphometrical features of single grain kernels or particles were used to discriminate two visibly similar wheat varieties foreign material in wheat hardsoft and spring-winter wheat classes and whole from broken corn kernels. Milled fractions of hard and soft wheat were evaluated using textural image analysis. Color image analysis of sound and mold damaged corn kernels yielded high recognition rates. The studies collectively demonstrate the potential for automated classification and assessment of grain quality using image analysis.

Citation Download Citation

Inna Y. Zayas and James L. Steele "Image analysis applications for grain science", Proc. SPIE 1379, Optics in Agriculture, (1 February 1991); https://doi.org/10.1117/12.25086

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