A real-time grading method of apples based on features extracted from defects

Abstract

This paper presents a hierarchical grading method applied to Jonagold apples. Several images covering the whole surface of the fruits were acquired thanks to a prototype grading machine. These images were then segmented and the features of the defects were extracted. During a learning procedure, the objects were classified into clusters by k-mean clustering. The classification probabilities of the objects were summarised and on this basis the fruits were graded using quadratic discriminant analysis. The fruits were correctly graded with a rate of 73%. The errors were found having origins in the segmentation of the defects or for a particular wound, in a confusion with the calyx end. 2003 Elsevier Ltd. All rights reserved.

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@inproceedings{Leemans2003ARG, title={A real-time grading method of apples based on features extracted from defects}, author={Vincent Leemans and M.-F. Destain}, year={2003} }