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Proceedings Paper

Grading of alfalfa cubes using imaging and texture analysis
Author(s): Philip W. Winter; Shahab Sokhansanj; Hugh C. Wood
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Paper Abstract

Alfalfa cubes are grade according to many factors, including overall color, appearance, and particle size. The grading process is currently done manually, and particle sizing is an off-line process done by physically breaking apart the cubes and sieving the particles. This paper presents research which may lead to an automated on-line grading system for alfalfa cubes. A machine vision system was used to extract texture and color features of alfalfa cubes. A neural network algorithm was used to discriminate two types of cubes of varying particle sizes by the use of their texture and color features. The network correctly classified all 22 test cubes using all color and texture features, and correctly classified 21 out of 22 test cubes suing only the texture features.

Paper Details

Date Published: 14 January 1999
PDF: 6 pages
Proc. SPIE 3543, Precision Agriculture and Biological Quality, (14 January 1999); doi: 10.1117/12.336887
Show Author Affiliations
Philip W. Winter, Univ. of Saskatchewan (Canada)
Shahab Sokhansanj, Univ. of Saskatchewan (Canada)
Hugh C. Wood, Univ. of Saskatchewan (Canada)


Published in SPIE Proceedings Vol. 3543:
Precision Agriculture and Biological Quality
George E. Meyer; James A. DeShazer, Editor(s)

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