Grading of alfalfa cubes using imaging and texture analysis

Philip W. Winter; Shahab Sokhansanj; Hugh C. Wood

doi:10.1117/12.336887

14 January 1999 Grading of alfalfa cubes using imaging and texture analysis

Philip W. Winter, Shahab Sokhansanj, Hugh C. Wood

Proceedings Volume 3543, Precision Agriculture and Biological Quality; (1999) https://doi.org/10.1117/12.336887
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

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.

Citation Download Citation

Philip W. Winter, Shahab Sokhansanj, and Hugh C. Wood "Grading of alfalfa cubes using imaging and texture analysis", Proc. SPIE 3543, Precision Agriculture and Biological Quality, (14 January 1999); https://doi.org/10.1117/12.336887

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