Use of machine vision and fuzzy sets to classify soft fruit

Ning Lu; Alan Tredgold; Edward R. Fielding

doi:10.1117/12.217566

28 August 1995 Use of machine vision and fuzzy sets to classify soft fruit

Ning Lu, Alan Tredgold, Edward R. Fielding

Proceedings Volume 2620, International Conference on Intelligent Manufacturing; (1995) https://doi.org/10.1117/12.217566
Event: International Conference on Intelligent Manufacturing, 1995, Wuhan, China

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.

Citation Download Citation

Ning Lu, Alan Tredgold, and Edward R. Fielding "Use of machine vision and fuzzy sets to classify soft fruit", Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); https://doi.org/10.1117/12.217566

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