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Proceedings Paper

Use of machine vision and fuzzy sets to classify soft fruit
Author(s): Ning Lu; Alan Tredgold; Edward R. Fielding
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Paper Abstract

The manual inspection and classification of soft fruit is a subjective activity, prone to human error. A fuzzy logic methodology has been developed to facilitate the use of automated inspection. The methodology is based on capturing and analyzing the digital images of soft fruit items. By identifying descriptive features for a specific type of fruit, and associating each feature with a set of possible membership values, a fuzzy logic classification scheme was applied. Preliminary tests were conducted on a sample of green apples, using a microcomputer-based, image processing system. The results indicate that the proposal is feasible and time efficient. Based on the average processing time for discrete items, a throughput rate of 2 items/second is predicted for a low-cost installation. Further testing and refinement of the fuzzy logic methodology is necessary before a commercial application can be developed. The use of direct color classification instead of the grayscale spectrum, is also investigated.

Paper Details

Date Published: 28 August 1995
PDF: 7 pages
Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); doi: 10.1117/12.217566
Show Author Affiliations
Ning Lu, Univ. of the Witwatersrand (South Africa)
Alan Tredgold, Univ. of the Witwatersrand (South Africa)
Edward R. Fielding, Univ. of the Witwatersrand (South Africa)

Published in SPIE Proceedings Vol. 2620:
International Conference on Intelligent Manufacturing
Shuzi Yang; Ji Zhou; Cheng-Gang Li, Editor(s)

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