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Visual detection of blemishes in potatoes using minimalist boosted classifiers

By Michael Barnes, Tom Duckett, Grzegorz Cielniak, Graeme Stroud and Glyn Harper


This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image.\ud A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted.\ud Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes.\ud With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc.\ud The results show that the method is able to build ``minimalist'' classifiers that optimise detection performance at low computational cost.\ud In experiments, blemish detectors were trained for both white and red potato varieties, achieving 89.6\% and 89.5\% accuracy, respectively

Topics: G400 Computer Science, D610 Food Science, G740 Computer Vision
Publisher: Elsevier
Year: 2010
DOI identifier: 10.1016/j.jfoodeng.2010.01.010
OAI identifier:

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