Agricultural Reviews

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Deep Learning based Image Processing Solutions in Food Engineering: A Review

Ninja Begum, Manuj Kumar Hazarika
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1Department of Food Engineering and Technology, Tezpur University, Sonitpur-784 028, Assam, India.
Cite article:- Begum Ninja, Hazarika Kumar Manuj (2022). Deep Learning based Image Processing Solutions in Food Engineering: A Review. Agricultural Reviews. 43(3): 267-277. doi: 10.18805/ag.R-2182.
Image based assessment of food quality for wholesomeness, nutritional composition, suitability as raw material for processing, degree of processing, product aesthetics, consumer attractiveness etc., are some of the aspirations for applying machine learning in food technology. The initial contributions made by machine learning in the field of artificial intelligence are now more prominent through the techniques of deep learning. This review presents the contributions of machine learning in obtaining image processing based solutions in food technology and the relative advantages of deep learning over machine learning as the technique for solving complex problems like image recognition and image classification. The deep learning based solutions to the problems of image processing are highlighted as the enablers of disruptions in the design and development of different sorting, grading and dietary assessment tools.

  1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G. and Isard, M. (2016). Tensorflow: A system for large scale machine learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI’ 16). pp. 265-283.

  2. Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Van Esesn, B.C., Awwal, A.A.S. and Asari, V.K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv: 1803. 01164.

  3. Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A. and Belopolsky, A. (2016). Theano: A python framework for fast computation of mathematical expressions, arXiv: arXiv-1605.

  4. Aziz, A.S.B.A. and Habeeb, R.A.A. (2019). Classification of food nutrients composition using deep learning. url: https://www. of_Food_Nutrients_Composition _using_Deep_ Learning.

  5. Borah, S. (2005). Machine vision for tea quality monitoring with special emphasis on fermentation and grading. Tezpur University, Assam. url: 10603/100305.

  6. Bossard, L., Guillaumin, M. and Gool, L.V. (2014). Food-101-mining discriminative components with random forests. In: European conference on computer vision, Springer. pp. 446-461.

  7. El-Bendary, N., Hariri, E.El, Hassanien, A.E. and Badr, A. (2015). Using machine learning techniques for eva-luating tomato ripeness. Expert Systems with Applications. 42(4): 1892-1905.

  8. Fan, S., Li, J., Zhang, Y., Tian, X., Wang, Q., He, X., Zhang, C. and Huang, W. (2020). On line detection of defective apples using computer vision system combined with deep learning methods. Journal of Food Engineering. 286: 110102.

  9. Farinella, G.M., Allegra, D. and Stanco, F. (2014). A benchmark dataset to study the representation of food images. In: European Conference on Computer Vision, Springer. pp. 584-599.

  10. Femling, F., Olsson, A. and Alonso-Fernandez, F. (2018). Fruit and vegetable identification using machine learning for retail applications. In: 14th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), IEEE. pp. 9-15.

  11. Gandhi, R.S., Monalisa, D., Dongre, V.B., Ruhil, A.P., Singh, A. and Sachdeva, G.K. (2012). Prediction of first lactation 305-day milk yield based on monthly test day records using artificial neural networks in Sahiwal cattle. Indian Journal of Dairy Science. 65(3).

  12. Giefer, L.A., Lutjen, M., Rohde, A.K., Freitag, M. (2019). Determination of the optimal state of dough fermentation in bread production by using optical sensors and deep learning. Applied Sciences. 9(20): 4266.

  13. Goswami, A. and Liu, H. (2017). Deep dish: Deep learning for classifying food dishes, Stanford University Reports. url:

  14. Gupta, Y. (2018). Selection of important features and predicting wine quality usingmachine learning techniques. Procedia Computer Science. 125: 305-312. /j.procs.2017.12.041.

  15. Guresen, E. and Kayakutlu, G. (2011).  Definition of artificial neural networks with comparison to other networks. Procedia Computer Science. 3: 426-433. j.procs.2010.12.071.

  16. He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770-778.

  17. Ireri, D., Belal, E., Okinda, C., Makange, N. and Ji, C. (2019). A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artificial Intelligence in Agriculture. 2: 28-37. https://

  18. Isleroglu, H. and Beyhan, S. (2020). Prediction of baking quality using machine learning based intelligent models. Heat and Mass Transfer, Springer. 56: 2045–2055. https://dx.

  19. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S. and Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia. pp. 675-678.

  20. Kagaya, H. and Aizawa, K. (2015). Highly accurate food/non-food image classification based on a deep convolutional neural network. In: International conference on image analysis and processing, Springer. pp. 350-357. 10.1007/978-3-319-23222-5_43.

  21. Kamrul, M.H., Rahman, M., Robin, M.R.I., Hossain, M.S., Hasan, M.H., Paul, P. (2020). A Deep Learning based Approach on Categorization of Tea Leaf. In: Proceedings of the International Conference on Computing Advancements. pp. 1-8.

  22. Karmokar B.C., Ullah, M.S., Siddiquee, M.K. and Alam, K.M.R. (2015). Tea leaf diseases recognition using neural network ensemble. International Journal of Computer Applications. 114(17): 975-8887.

  23. Kawano, Y. and Yanai, K. (2014). Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation. In: European Conference on Computer Vision, Springer, Cham.

  24. Kılıç, K., Boyacı, I. H., Köksel, H. and Küsmenoğlu, İ. (2007). A classification system for beans using computer vision system and artificial neural networks. Journal of Food Engineering. 78(3):897-904.

  25. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P. (1998). Gradient-based learning applied to document recognition. In: Proceedings of the IEEE. 86(11): 2278-2324.

  26. Mezgec, S. and Koroušić Seljak, B. (2017). NutriNet: A deep learning food and drink image recognition system for dietary assessment. Nutrients 9(7): 657.

  27. Militante, S. (2019). Fruit grading of Garcinia Binucao (batuan) using image processing. International Journal of Recent Technology and Engineering (IJRTE). 8(2): 1829-1832.

  28. Muresan, H. and Oltean, M. (2018). Fruit recognition from images using deep learning. Acta Universitatis Sapientiae. Informatica. 10(1): 26-42.

  29. Önler, E., Çelen, I.H., Gulhan, T. and Boynukara, B. (2017). A study regarding the fertility discrimination of eggs by using ultrasound. Indian Journal of Animal Research. 51(2): 322-326.

  30. Ordukaya, E. and Karlik, B. (2016). Fruit juice-alcohol mixture analysis using machine learning and electronic nose. IEEJ Transactions on Electrical and Electronic Engineering. 11(S1): S171-S176.

  31. Pan, L., Pouyanfar, S., Chen, H., Qin J. and Chen, S.C. (2017). Deep Food: Automatic multi-class classification of food ingredients using deep learning. In: 2017 IEEE 3rd international conference on collaboration and internet computing (CIC), IEEE. pp. 181-189.

  32. Pandey, P., Deepthi, A., Mandal, B. and Puhan, N.B. (2017). Foodnet: Recognizing foods using ensemble of deep networks. IEEE Signal Processing Letters. 24(12): 1758-1762.

  33. Patil, O. and Gaikwad, V. (2018). Classification of vegetables using Tensorflow. International Journal for Research in Applied Science and Engineering Technology. 6(4): 2926-2934.

  34. Rafiq, A., Makroo, H.A. and Hazarika, M.K. (2016).  Artificial neural network-based image analysis for evaluation of quality attributes of agricultural produce. Journal of Food Processing and Preservation. 40(5): 1010-1019.

  35. Rodríguez, F.J., García, A., Pardo, P.J., Chávez, F. and Luque-Baena, R.M. (2018). Study and classification of plum varieties using image analysis and deep learning techniques. Progress in Artificial Intelligence. 7(2): 119-127. http://

  36. Semary, N.A., Tharwat, A., Elhariri, E. and Hassanien, A.E. (2015).  Fruit-based tomato grading system using features fusion and support vector machine. in: Intelligent Systems’ 2014, Springer, 2015, pp. 401-410. 978-3-319-11310-4_35.

  37. Shanmugamani, R. (2018). Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras. Packt Publishing Ltd.

  38. Singla, A., Yuan, L. and Ebrahimi, T. (2016). Food/non-food Image Classification and Food Categorization using Pre-trained Googlenet Model. In: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management. pp: 3-11.

  39. Sonobe, R., Hirono, Y. and Oi, A. (2020). Non-destructive detection of tea leaf chlorophyll content using hyperspectral reflectance and machine learning algorithms. Plants. 9(3): 368.

  40. Soofi, A.A. and Awan, A. (2017). Classification techniques in machine learning: Applications and issues. Journal of Basic and Applied Sciences. 13: 459-465.

  41. Tripathi, M.K. and Maktedar, D.D. (2020). A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: A survey. Information Processing in Agriculture. 7(2): 183-203.

  42. Tu, S., Xue, Y., Zheng, C., Qi, Y., Wan, H. and Mao, L. (2018). Detection of passion fruits and maturity classificat-ion using red-green- blue depth images. Biosystems Engineering. 175: 156-167.

  43. Vaviya, H., Yadav, A., Vishwakarma, V. and Shah, N. (2019). Identification of Artificially Ripened Fruits using Machine Learning. In: 2nd International Conference on Advances in Science and Technology (ICAST). url:

  44. Viejo, C.G., Fuentes, S., Torrico, D., Howell, K. and Dunshea, F.R. (2018). Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms. Journal of the Science of Food and Agriculture. 98(2): 618-627.

  45. Wan, P., Toudeshki, A., Tan, H. and Ehsani, R. (2018). A methodology for fresh tomato maturity detection using computer vision. Computers and Electronics in Agriculture. 146: 43-50.

  46. Yordi, E.G., Koelig, R., Matos, M.J., Mota, Y.C., Uriarte, E., Martinez, A.P., Santana, L. and Molina, E. (2016). Machine-learning models to predict the antioxidant capacity of food. In: International Conference on Multidisciplinary Sciences (MOL2NET 2016), 2nd edition.

  47. Yunus, R., Arif, O., Afzal, H., Amjad, M.F., Abbas, H., et al. (2018). A framework to estimate the nutritional value of food in real time using deep learning techniques. IEEE Access. 7: 2643-2652.

  48. Zhu, L., Li, Z., Li, C. Wu, J. and Yue, J. (2018). High performance vegetable classification from images based on alexnet deep learning model. International Journal of Agricultural and Biological Engineering. 11(4): 217-223.

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