Introducing an Automatic Bread Quality Assessment Algorithm using Image Processing Techniques


  •   Roya Biglari Archandani

  •   Farahnaz Mohanna

  •   Mohammad Javad Ahsani


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.


Pahlavan A, Kamani MH, Elhamirad AH, Sheikholeslami Z, Armin M, Amani H. Rapid quality assessment of bread using developed multivariate models: A simple predictive modeling approach. Progress in Agricultural Engineering Sciences. 2020 April;16(1):1-10. doi:10.1556/446.2020.00001.

Adamczyk G, Ivanisova E, Kaszuba J, Bobet I, Khvastenko K, Chmiel M, et al. Quality assessment of wheat bread incorporating chia seeds. Foods, 2021 Oct;10(10):2376. doi:10.3390/foods10102376.

Khan B, Han F, Wang Z, Iqbal A. Automatic quality inspection of bakery products based on shape and color information. Harbin Institute of Technology. 2017 Oct;24(5): 88-96. doi:10.11916/j.issn.1005-9113.16179.

Scheuer PM, Ferreira JAS, Mattion B, de Miranda MZ, de Francisco A. Optimization of image analysis techniques for quality assessment of whole-wheat breads made with fat replacer. Food Science and Technology. 2015 Jan-Mar;35(1): 133-142. doi:10.1590/1678-457X.6560.

Srivastava S, Vaddadi S, Sadistap S. Quality Assessment of Commercial Bread Samples Based on Breadcrumb Features and Freshness Analysis Using an Ultrasonic Machine Vision System. Food Measurement and Characterization. 2015 Dec;9(4): 525-540. doi:10.1007/S11694-015-9261-4.

Amani Nia S, Gale SMA, Ranji A, Nekahi A. Investigation of physical characteristics of bread by processing digital images (machine vision). Life Science. 2012 January;9(3): 1674-1678.

Fan Y, Zhang H. Application of Gabor filter and multi-class SVM in baking bread quality classification. IEEE International Proceedings of Mechatronics and Automation Conference, pp. 1498-1502, Luoyang, 2006 25-26 June. doi:10.1109/ICMA.2006.257396.

Gonzales-Barron U, Butler F. A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis. Food Engineering. 2006 May;74(2): 268-278. doi:10.1016/j.jfoodeng.2005.03.007.

Farrera-Rebollo RR, Salgado-Cruz MDP, Chanona-perez J, Gulierrez-lopez F. Evaluation of image analysis tools for characterization of sweet bread crumb structure. Food Bioprocess Technology. 2012 January;5(2): 474-484. doi:10.1007/s11947-011-0513-7.

Shibata M, Tsuta M, Sugiyama J, Fujita K, Araki T, et al. Image analysis of bread crumb structure in relation to mechanical properties. Food Engineering. 2013 January;9(1): 115-120. doi:10.1515/ijfe-2012-0163.

Peri G, Romaniello R. Quality inspection of industrial bread by image analysis. International Proceedings of Information Systems in Sustainable Agriculture, Agro environment and Food Technology Conference, pp. 311-319, Volos(Giorgio), 2006 September.

Whitworth MB, Cauvain SP, Cliffe D. Measurement of bread cell structure by image analysis. In in Woodhead Publishing Series in Food Science, Technology and Nutrition, pp. 193-198, 2005. doi:10.1533/9781845690632.5.193.

Kalthum Ibrahim U, Mohd salleh R, Zhou W. The effect of oven surface on bread color development during baking process. In 2013 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC), pp. 453-458, 2013 April. doi:10.1109/BEIAC.2013.6560169.

Mery D, Pedreschi F. Segmentation of color food images using a robust algorithm. Food Engineering. 2005 Februray;66(3): 353-360. doi:10.1016/jfoodeng.2004.04.001.

Shapiro L, Stockman G. Computer Vision. Prentice Hall, Inc., Upper Saddle River, NJ, USA, 2001.

Maheshan CM, Prasanna Kumar H. Investigation and analysis of real time transformer oil images using Haralick texture features. Processing of the Third International Conference on Multimedia, Communication & Information Technology (MPCIT), 2020 December 11-12; Shivamogga, India. doi:10.1109/MPCIT51588.2020.9350502.

Fan H, Zhu H. Separation of vehicle detection area using Fourier descriptor under internet of things monitoring. IEEE Access. 2018 August;6: 47600-47609. doi:10.1109/ACCESS.2018.2865209.

Wang M, Shang X. A fast image fusion with discrete Cosine transform. IEEE Signal Processing Letters. 2020 June;27: 990-994. doi:10.1109/LSP.2020.2999788.

Luo L, Yang Z, Li S, Wu Y. FPCB surface defect detection: a decoupled two-stage object detection framework. IEEE Transactions on Instrumentation and Measurement. 2021 June;70. doi:10.1109/TIM.2021.3092510.

Kim J, Um S, Min D. Fast 2D complex filter with kernel decomposition. IEEE Transactions on Image Processing, 2018 December;27(4): 1713-1722. doi:10.1109/TIP.2017.2783621.

Xie L, Tian Q, Zhang B. Simple techniques make sense: feature pooling and normalization for image classification. IEEE Transactions on Circuits and Systems for Video Technology. 2016 July;26(7): 1251–1264. doi:10.1109/TCSVT.2015.2461978.

Liu C, Hirota K, Ma J, Jia Z, Dai Y. Facial expression recognition using hybrid features of pixel and geometry. IEEE Access. 2021 January;9: 18876-18889. doi:10.1109/ACCESS.2021.3054332.

Xie G, Guo B, Huang Z, Zheng Y, Yan Y. Combination of dominant color descriptor and Hu moment in consistent zone for content based image retrieval. IEEE Access. 2020 August;8: 146284-146299. doi: 10.1109/ACCESS.2020.3015285.

Zhang Z, Dong M, Ota K, Zhang Y, Ren Y. LBCF: A link-based collaborative filtering for overfitting problem in recommender system. IEEE Transactions on Computational Social Systems. 2021 May;8(6): 1450-1464. doi:10.1109/TCSS.2021.3081424.

Mao Y, Yang Y. A wrappers feature subset selection method based on randomized search and multilayer structure. Biomedical Research International. 2019 November;9864213. doi: 10.1155/2019/9864213.

Xu J, Han J, Nie F, Li X. Multi-view scaling Support Vector Machine for classification and feature selection. IEEE Transactions on Knowledge and Data Engineering. 2020 July;32(7): 1419-1430. doi:10.1109/TKDE.2019.2904256.

Mahmood A, Uzair M, Al-Maadeed S. Multi-order statistical descriptors for real-time face recognition and object classification. IEEE Access. 2018 January;6: 12993-13004. doi:10. 1109/ACCESS.2018.2794357.


Download data is not yet available.


How to Cite
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: