Title

Wheat classification using image analysis and crush-force parameters

Authors

Authors

I. Y. Zayas; C. R. Martin; J. L. Steele;A. Katsevich

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Trans. ASAE

Keywords

wheat; digital imaging; machine vision; pattern recognition; multivariate analysis; hardness; DISCRIMINATION; KERNELS; Agricultural Engineering

Abstract

A study was conducted to develop methodology for wheat classes and variety identification by combination of image analysis techniques with wheat hardness physical measurements. Wheat kernel morphometrical parameters were extracted from digitized images and hardness parameters were obtained from force-deformation curves from a single kernel wheat characterization system which also provided a kernel weight. Pattern recognition methods were applied to the data base of combined parameters for wheat kernels of six classes and seventeen varieties of soft and hard wheat. Recognition rates for parameter combinations of shape, size, and hardness scores were higher than hardness or imaging alone or when combined with weight. Hard and soft recognition rates of 94% was achieved with shape and hardness of the wheat kernels A PC version of the developed algorithm was written and tested with the same data set. Satisfactory performance in the PC version confirmed the practicality of the method developed.

Journal Title

Transactions of the Asae

Volume

39

Issue/Number

6

Publication Date

1-1-1996

Document Type

Article

Language

English

First Page

2199

Last Page

2204

WOS Identifier

WOS:A1998YH96200015

ISSN

0001-2351

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