Application of image processing and artificial neural networks as a non-destructive approach to prediction of fat content and classification of camel meat

Document Type : Research Article


1 Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, University of Jiroft, Jiroft, Iran.

2 Department of Food Science and Technology, Tuyserkan Faculty of Engineering and Natural Resources, Bu-Ali Sina University, Tuyserkan, Iran.

3 Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, University of Urmia, Urmia, Iran


Camel meat can be a suitable alternative for other red meat types in human nutrition, due to its low cholesterol and low-fat content and the appropriate protein content. This research aims to investigate and evaluate the fat content and freshness of camel meat using machine vision technology as a non-destructive method. Therefore, using image processing as a non-destructive method and Soxhlet device as a destructive method, the amount of fat content was predicted, and also the freshness was classified for camel meat. In the image processing section, 108 textual features and 39 color features were extracted in the RGB, HSV, HIS, and CIElab color spaces. Moreover, to predict the freshness and quality of meat, feed-forward back propagation artificial neural networks with one and two hidden layers, a various number of neurons, and threshold functions were used. Also, according to the regression diagram of fat content obtained from the destructive method (fat content obtained from Soxhlet device) with fat content obtained from non-destructive method (machine vision), the coefficient of determination and accuracy between them achieved 0.841. The results of the evaluation of the neural networks showed that the best desirable network for classification based on freshness is a one-hidden layer network with topology 147-3-1, tangent-sigmoid transfer function at hidden layer and purelin transfer function at output layer (R2= 0.996), and also to prete of fat content the best network is two-hidden layer network with linear, log-sigmoid, log-sigmoid transfer function at first hidden layer, second hidden layer and output layer respectively (R2= 0.99). Therefore, the results of this study show that the proposed system with the help of machine vision technology can predict the freshness and fat content of camel meat with acceptable accuracy.

Graphical Abstract

Application of image processing and artificial neural networks as a non-destructive approach to prediction of fat content and classification of camel meat


  • The first special study to estimate the freshness and fat content of camel meat based on machine vision and image processing as a new approach in the food industry.
  • Suggest a fundamental, non-destructive, fast, low cost, easy and practical method in creating an intelligent system for online monitoring of meat characteristics in the food and meat industry.
  • Simultaneous use of image processing and feed-forward back propagation ANNs.
  • Using the analysis of texture and texture-color features of images to improve the results.


Main Subjects

[1] Mohammed, H. H. H., Jin, G., Ma, M., Khalifa, I., Shukat, R., Elkhedir, A. E., Zeng, Q. & Noman, A. E. (2020). Comparative characterization of proximate nutritional compositions, microbial quality and safety of camel meat in relation to mutton, beef, and chicken. LWT - Food Sci. Technol., 118, 108714 (1-7). ‏
[2] Ali, A., Baby, B., & Vijayan, R. (2019). From desert to medicine: a review of camel genomics and therapeutic products. Front. genet.10 (17), 1-20.
[3] Baba, W. N., Rasool, N., Selvamuthukumara, M., & Maqsood, S. (2021). A review on nutritional composition, health benefits, and technological interventions for improving consumer acceptability of camel meat: an ethnic food of Middle East. J. Ethn. Foods8(1), 1-13.‏
[4] Dowlati, M., de la Guardia, M., & Mohtasebi, S. S. (2012). Application of machine-vision techniques to fish-quality assessment. TrAC Trends Anal. Chem., 40, 168-179.
[5] Rahman, M.F., Abdullah Iqbal, M., Hashem, A. & Adedeji, A.A. (2020). Quality Assessment of Beef Using Computer Vision Technology. Food Sci. Anim. Resour., 40(6), 896-910.
[6] Singh, T. P., & Chatli., M. K. (2013). Advances in computer vision technology for foods of animal and aquatic origin. J. Meat Sci. Technol.. 1(2), 40-49.
[7] Xiong, Z., Sun, D.W., Pu, H., Xie, A., Han, Z. & Luo, M. (2015). Non-destructive prediction of  thiobarbituric acid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chem., 179, 175–181.
[8] Lawrie, R.A. (2006).  Lawrie’s Meat Science (7th ed.). Woodhead Publishing Limited, Cambridge, Uk.
[9] Multan, W. K., Ali, S. K., Aydam, Z. M., & Taher, B. H. (2020). Feature Extraction Methods: A Review. In Journal of Physics: Conf. Ser. (Vol. 1591, No. 1, p. 012028). IOP Publishing.‏
[10] Long, F., Zhang, H., & Feng, D. D. (2003). Fundamentals of content-based image retrieval. In: D. Feng, W. C. Siu, & H. J. Zhang, (Eds.) Multimedia information retrieval and management (pp. 1-26). Springer, Berlin, Heidelberg.‏
[11] Nekoie, N., Dowlati, M. & Golpour, I. (2016). Identification and classification of persian Cumin (Bunium persicum Boiss) landraces using image processing in combination with artificial neural networks. J. Res. Mech. Agric. Mach., 5(8), 37. [In Persian]
[12] Shiranita, K., Hayashi, K., Otsubo, A., Miyajima, T. & Takiyama, R. (2000). Grading meat quality by image processing. Pattern Recognit., 33, 97-104.
[13] Chmiel, M., Slowinski, M. & Dasiewiez, K. (2011). Application of computer vision systems for estimation of fat content in poultry meat. Food Control 22(8), 1424-1427.
[14] Dousti Irani, A. & Golzarian, M.R. (2013). Design and evaluation of image processing algorithm for estimating red meat fat content.  In 8th Natl. Congr. Agric. Mach. Eng. (Biosyst.) Mech. Iran. (pp. 3036-3046), Ferdowsi University of Mashhad. [In Persian]
[15] Putra, G. B., & Prakasa, E. (2020). Classification of Chicken Meat Freshness using Convolutional Neural Network Algorithms. In Int. Conf. Innov. Intell. Inform. Comput. Technol. (3ICT) (pp. 1-6). IEEE.‏
[16] Taheri-Garavand, A., Fatahi, S., Omid, M., & Makino, Y. (2019). Meat quality evaluation based on computer vision technique: A review. Meat sci.156, 183-195.‏
[17] Penning, B. W., Snelling, W. M., & Woodward-Greene, M. J. (2020). Machine learning in the assessment of meat quality. IT Prof.22(3), 39-41.
[18] Neelamma, K . P., Virendra, S . M. & Ravi, M.Y. (2011) .Color and texture based identification and classification of food Grains using different Color Models and Haralick features. IJCSE. 3(12), 3669-3680.
[19] Golpour, I. (2012). Predicting, diagnosing and investigating the drying kinetics of rice cultivars using image processing and artificial neural networks. Master thesis in Biosystem mechanics. School of Agriculture, Bu-Ali Sina University. [In Persian]
[20] AOAC International 2002. Official methods of analysis,  end, AOAC International, Arlington.
[21] Dowlati, M., Mohtasebi, S. S., Omid, M., Razavi, S. H., Jamzad, M., & De La Guardia, M. (2013). Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. J. Food Eng., 119(2), 277-287.
[22] Sheibani tazrachi, A. (2015). Detection of quality and grading of ostrich meat using artificial technique. Master thesis Biosyst. mech., Sch. Agric. Jiroft Univ., [In Persian]
[23] Jouki, M. & Khazaei, N. (2012). Color and oxidation changes in camel meat during storage. Int. J. Pharma and Bio Sci., 3(1), 164-170.
[24] Liao, Q., Wei, C., Li, Y., & Ouyang, H. (2021). Developing a Machine Vision System Equipped with UV Light to Predict Fish Freshness Based on Fish-Surface Color. Food Nutr. Sci., 12(3), 239-248.‏
[25] Lu, J., Tan, J., Shatadal, P. & Gerrard, D.  (2000). Evaluation of pork color by using computer vision. Meat Sci., 56(1), 57-60.
[26] Bacus, J. A. (2021). Identification of Pork Meat Freshness Using Neural Networks. In 2021 IEEE: Int. Conf. Electron. Technol. Commun. Inf. (ICETCI) (pp. 402-405).