Minimalist AdaBoost for Blemish Identification in Potatoes

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Abstract

We present a multi-class solution based on minimalist AdaBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to first reduce the feature set by selecting five features for each class, then train binary classifiers for each class, classifying each testing example according to the binary classifier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry defined criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively.