Introducing an Automatic Bread Quality Assessment Algorithm using Image Processing Techniques
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Submitted Oct 4, 2022
Published Nov 19, 2022
Published Nov 19, 2022
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In this research, an automatic algorithm of bread quality assessment using image processing techniques, is proposed. First, color images of bread with different qualities are photographed and a database of 1250 bread images is prepared. Then 2320 color and texture features are extracted from each bread images. Then, from this number of features, only 15 features containing sufficient information are selected. In addition, 54 appearance features are extracted from each bread image to determine its shape and size. Finally, bread images are classified using the multilevel Support Vector Machine classifier. The classification process is divided into five "one-against-all" classification problems. The proposed algorithm correctly identifies the bread appearance defects, including cuts, fractures, folds, non-uniformity, black and burnt areas in baking, deformity, color and size. The proposed algorithm, considering the extraction of only 15 features per an image, has a speed that guarantees its use in a machine vision system. The performance success of the proposed algorithm on the bread database, despite its very simple implementation, is 96.95%.
Keywords: Bread Quality Assessment, Classification, Feature Extraction, Feature Selection.
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How to Cite
[1]
Archandani, R.B., Mohanna, F. and Ahsani, M.J. 2022. Introducing an Automatic Bread Quality Assessment Algorithm using Image Processing Techniques. European Journal of Electrical Engineering and Computer Science. 6, 6 (Nov. 2022), 31–38. DOI:https://doi.org/10.24018/ejece.2022.6.6.470.
This work is licensed under a Creative Commons Attribution 4.0 International License.
