A decomposition machine-learning strategy for automated fruit grading

Teo Susnjak, Andre Barczak, Napoleon Reyes

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

8 Citations (Scopus)


Automated grading of fruit is an important industrial task that is expanding rapidly in its uptake. Machine learning-based techniques are increasingly being applied to this domain in order to formulate effective solutions for complex classification tasks. The inherent variability in the visual appearance of fruit and its quality-determining features, contributes to it often being a challenging classification task with much potential for improving the predictive accuracies for many fruit varieties. Additionally, the usability of many sophisticated machine learning algorithms in the form of tunable parameters and interpretable outputs is low, thus presenting a real barrier for the uninitiated. We address these problems by decomposing the overall machine learning task into subproblems. We propose combining a more sophisticated boosting algorithm (AdaBoost. ECC) with low interpretability for the learning of fruit-surface characteristics, whose outputs can then be combined with rule induction algorithms (RIPPER and FURIA) that learns the overall fruit grading rules with outputs of high interpretability for the operators to both review and revise. Our initial experiments considered four fruit datasets. We compared the results of our approach with that from a commercial system using manual calibration of the fruit grading parameters and found that our strategy can improve the accuracy over the current industry methods while providing high usability and interpretability of outputs.

Original languageEnglish
Title of host publicationProceedings of the World Congress on Engineering and Computer Science 2013
EditorsS. I. Ao, C. Douglas, W.S. Grundfest, J. Burgstone
PublisherNewswood Limited
Number of pages7
ISBN (Print)9789881925244
Publication statusPublished - 2013
Externally publishedYes
Event2013 World Congress on Engineering and Computer Science, WCECS 2013 - San Francisco, CA, United States
Duration: 23 Oct 201325 Oct 2013

Publication series

NameLecture Notes in Engineering and Computer Science
ISSN (Print)2078-0958


Conference2013 World Congress on Engineering and Computer Science, WCECS 2013
Abbreviated titleWCECS
Country/TerritoryUnited States
CitySan Francisco, CA


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