-
CiteScore
0.09
Impact Factor
Volume 2, Issue 2, Agricultural Science and Food Processing
Volume 2, Issue 2, 2025
Submit Manuscript Edit a Special Issue
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
Agricultural Science and Food Processing, Volume 2, Issue 2, 2025: 68-88

Open Access | Review Article | 16 May 2025
Application of Artificial Intelligence in Food Industry: A Review
1 College of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 102488, China
* Corresponding Author: Yiyi Wang, [email protected]
Received: 27 January 2025, Accepted: 18 March 2025, Published: 16 May 2025  
Abstract
As people’s living standards continue to rise, the food processing industry is facing many challenges such as improving production efficiency, ensuring food safety, and reducing processing costs. The emergence of artificial intelligence (AI) technology has brought new opportunities for this industry. This paper describes AI applications in food processing, especially focuses on machine learning (ML) and deep learning (DL) techniques. These techniques are used for grading and sorting of raw materials, production optimization during food processing, quality inspection, and food safety assurance. For example, ML algorithms can be combined with non-destructive testing techniques. This allows for the effective identification of adulterated meats. The implementation of AI not only improves production efficiency but also enhances food safety and quality control through real-time monitoring and rapid, non-destructive detection methods. Future developments in AI technologies are expected to further promote the sustainable development of the food processing industry by improving data quality, developing more interpretable models, and reducing costs.

Graphical Abstract
Application of Artificial Intelligence in Food Industry: A Review

Keywords
artificial intelligence
machine learning
deep learning
food processing

Data Availability Statement
Data will be made available on request.

Funding
This work was supported without any funding.

Conflicts of Interest
The author declare no conflicts of interest.

Ethical Approval and Consent to Participate
Not applicable.

References
  1. Zhu, L., Spachos, P., Pensini, E., & Plataniotis, K. N. (2021). Deep learning and machine vision for food processing: A survey. Current Research in Food Science, 4, 233-249.
    [CrossRef]   [Google Scholar]
  2. Lin, Y., Ma, J., Wang, Q., & Sun, D. W. (2023). Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Critical Reviews in Food Science and Nutrition, 63(12), 1649-1669.
    [CrossRef]   [Google Scholar]
  3. Jadhav, H. B., Alaskar, K., Desai, V., Sane, A., Chaudhary, P., Annapure, U., ... & Nayik, G. A. (2024). Transformative impact: Artificial intelligence in the evolving landscape of processed food-a concise review focusing on some food processing sectors. Food Control, 110803.
    [CrossRef]   [Google Scholar]
  4. Wang, M., & Li, X. (2024). Application of artificial intelligence techniques in meat processing: A review. Journal of Food Process Engineering, 47(3), e14590.
    [CrossRef]   [Google Scholar]
  5. Thapa, A., Nishad, S., Biswas, D., & Roy, S. (2023). A comprehensive review on artificial intelligence assisted technologies in food industry. Food Bioscience, 103231.
    [CrossRef]   [Google Scholar]
  6. Hasan, M. S. (2017, December). An application of pre-trained CNN for image classification. In 2017 20th international conference of computer and information technology (ICCIT) (pp. 1-6). IEEE.
    [CrossRef]   [Google Scholar]
  7. Zhu, L., & Spachos, P. (2021). Support vector machine and YOLO for a mobile food grading system. Internet of Things, 13, 100359.
    [CrossRef]   [Google Scholar]
  8. Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
    [CrossRef]   [Google Scholar]
  9. Usama, M., Qadir, J., Raza, A., Arif, H., Yau, K. L. A., Elkhatib, Y., ... & Al-Fuqaha, A. (2019). Unsupervised machine learning for networking: Techniques, applications and research challenges. IEEE access, 7, 65579-65615.
    [CrossRef]   [Google Scholar]
  10. Szturo, K., & Szczypiński, P. M. (2017, September). Ontology based expert system for barley grain classification. In 2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) (pp. 360-364). IEEE.
    [CrossRef]   [Google Scholar]
  11. Nayak, J., Vakula, K., Dinesh, P., Naik, B., & Pelusi, D. (2020). Intelligent food processing: Journey from artificial neural network to deep learning. Computer Science Review, 38, 100297.
    [CrossRef]   [Google Scholar]
  12. Guiné, R. P., Barroca, M. J., Gonçalves, F. J., Alves, M., Oliveira, S., & Mendes, M. (2015). Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry, 168, 454-459.
    [CrossRef]   [Google Scholar]
  13. Fazaeli, M., Emam-Djomeh, Z., Omid, M., & Kalbasi-Ashtari, A. (2013). Prediction of the physicochemical properties of spray-dried black mulberry (Morus nigra) juice using artificial neural networks. Food and Bioprocess Technology, 6, 585-590.
    [CrossRef]   [Google Scholar]
  14. Sargolzaei, J., & Moghaddam, A. H. (2013). Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks and an adaptive-network-based fuzzy inference system. Frontiers of Chemical Science and Engineering, 7, 357-365.
    [CrossRef]   [Google Scholar]
  15. Bhagya Raj, G. V. S., & Dash, K. K. (2020). Comprehensive study on applications of artificial neural network in food process modeling. Critical Reviews in Food Science and Nutrition, 62(10), 2756–2783.
    [CrossRef]   [Google Scholar]
  16. Pan, L., Pouyanfar, S., Chen, H., Qin, J., & Chen, S. C. (2017, October). Deepfood: Automatic multi-class classification of food ingredients using deep learning. In 2017 IEEE 3rd international conference on collaboration and internet computing (CIC) (pp. 181-189). IEEE.
    [CrossRef]   [Google Scholar]
  17. Fan, S., Li, J., Zhang, Y., Tian, X., Wang, Q., He, X., ... & Huang, W. (2020). On line detection of defective apples using computer vision system combined with deep learning methods. Journal of Food Engineering, 286, 110102.
    [CrossRef]   [Google Scholar]
  18. Rauf, H. T., Lali, M. I. U., Zahoor, S., Shah, S. Z. H., Rehman, A. U., & Bukhari, S. A. C. (2019). Visual features based automated identification of fish species using deep convolutional neural networks. Computers and electronics in agriculture, 167, 105075.
    [CrossRef]   [Google Scholar]
  19. Chen, D., Wu, P., Wang, K., Wang, S., Ji, X., Shen, Q., ... & Tang, G. (2022). Combining computer vision score and conventional meat quality traits to estimate the intramuscular fat content using machine learning in pigs. Meat Science, 185, 108727.
    [CrossRef]   [Google Scholar]
  20. Fowler, S. M., Wheeler, D., Morris, S., Mortimer, S. I., & Hopkins, D. L. (2021). Partial least squares and machine learning for the prediction of intramuscular fat content of lamb loin. Meat Science, 177, 108505.
    [CrossRef]   [Google Scholar]
  21. Dewi, T., Risma, P., & Oktarina, Y. (2020). Fruit sorting robot based on color and size for an agricultural product packaging system. Bulletin of Electrical Engineering and Informatics, 9(4), 1438-1445.
    [CrossRef]   [Google Scholar]
  22. Phate, V. R., Malmathanraj, R., & Palanisamy, P. (2021). Classification and indirect weighing of sweet lime fruit through machine learning and meta-heuristic approach. International Journal of Fruit Science, 21(1), 528-545.
    [CrossRef]   [Google Scholar]
  23. Kumar, S. D., Esakkirajan, S., Bama, S., & Keerthiveena, B. (2020). A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier. Microprocessors and Microsystems, 76, 103090.
    [CrossRef]   [Google Scholar]
  24. Bhat, I., Umadevi, V., Jagadeesh, N., Bhat, S., & Shenoy, R. S. (2023, March). Tender Coconut Classification using Decision Tree and Deep Learning Technique. In 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 395-398). IEEE.
    [CrossRef]   [Google Scholar]
  25. Albarrak, K., Gulzar, Y., Hamid, Y., Mehmood, A., & Soomro, A. B. (2022). A deep learning-based model for date fruit classification. Sustainability, 14(10), 6339.
    [CrossRef]   [Google Scholar]
  26. Momeny, M., Jahanbakhshi, A., Jafarnezhad, K., & Zhang, Y. D. (2020). Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach. Postharvest Biology and Technology, 166, 111204.
    [CrossRef]   [Google Scholar]
  27. Hu, G., Zhang, E., Zhou, J., Zhao, J., Gao, Z., Sugirbay, A., ... & Chen, J. (2021). Infield apple detection and grading based on multi-feature fusion. Horticulturae, 7(9), 276.
    [CrossRef]   [Google Scholar]
  28. Chakraborty, S., Shamrat, F. J. M., Billah, M. M., Al Jubair, M., Alauddin, M., & Ranjan, R. (2021, June). Implementation of deep learning methods to identify rotten fruits. In 2021 5th international conference on trends in electronics and informatics (ICOEI) (pp. 1207-1212). IEEE.
    [CrossRef]   [Google Scholar]
  29. Xie, W., Wei, S., Zheng, Z., & Yang, D. (2021). A CNN-based lightweight ensemble model for detecting defective carrots. Biosystems Engineering, 208, 287-299.
    [CrossRef]   [Google Scholar]
  30. Jahanbakhshi, A., Momeny, M., Mahmoudi, M., & Zhang, Y. D. (2020). Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks. Scientia Horticulturae, 263, 109133.
    [CrossRef]   [Google Scholar]
  31. Taner, A., Öztekin, Y. B., & Duran, H. (2021). Performance analysis of deep learning CNN models for variety classification in hazelnut. Sustainability, 13(12), 6527.
    [CrossRef]   [Google Scholar]
  32. Taheri-Garavand, A., Fatahi, S., Banan, A., & Makino, Y. (2019). Real-time nondestructive monitoring of Common Carp Fish freshness using robust vision-based intelligent modeling approaches. Computers and Electronics in Agriculture, 159, 16-27.
    [CrossRef]   [Google Scholar]
  33. Matthews, D., Pabiou, T., Evans, R. D., Beder, C., & Daly, A. (2022). Predicting carcass cut yields in cattle from digital images using artificial intelligence. Meat Science, 184, 108671.
    [CrossRef]   [Google Scholar]
  34. Gonçalves, D. N., de Moares Weber, V. A., Pistori, J. G. B., da Costa Gomes, R., de Araujo, A. V., Pereira, M. F., ... & Pistori, H. (2021). Carcass image segmentation using CNN-based methods. Information Processing in Agriculture, 8(4), 560-572.
    [CrossRef]   [Google Scholar]
  35. Yang, Y., Liu, Z., Huang, M., Zhu, Q., & Zhao, X. (2023). Automatic detection of multi-type defects on potatoes using multispectral imaging combined with a deep learning model. Journal of Food Engineering, 336, 111213.
    [CrossRef]   [Google Scholar]
  36. Mohi-Alden, K., Omid, M., Firouz, M. S., & Nasiri, A. (2023). A machine vision-intelligent modelling based technique for in-line bell pepper sorting. Information Processing in Agriculture, 10(4), 491-503.
    [CrossRef]   [Google Scholar]
  37. Da Costa, A. Z., Figueroa, H. E., & Fracarolli, J. A. (2020). Computer vision based detection of external defects on tomatoes using deep learning. Biosystems Engineering, 190, 131-144.
    [CrossRef]   [Google Scholar]
  38. Chakraborty, S. K., Subeesh, A., Potdar, R., Chandel, N. S., Jat, D., Dubey, K., & Shelake, P. (2023). AI‐enabled farm‐friendly automatic machine for washing, image‐based sorting, and weight grading of citrus fruits: Design optimization, performance evaluation, and ergonomic assessment. Journal of Field Robotics, 40(6), 1581-1602.
    [CrossRef]   [Google Scholar]
  39. Chen, Y., An, X., Gao, S., Li, S., & Kang, H. (2021). A deep learning-based vision system combining detection and tracking for fast on-line citrus sorting. Frontiers in Plant Science, 12, 622062.
    [CrossRef]   [Google Scholar]
  40. Azarmdel, H., Jahanbakhshi, A., Mohtasebi, S. S., & Muñoz, A. R. (2020). Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biology and Technology, 166, 111201.
    [CrossRef]   [Google Scholar]
  41. Xiao, B., Nguyen, M., & Yan, W. Q. (2021). Apple ripeness identification using deep learning. In Geometry and Vision: First International Symposium, ISGV 2021, Auckland, New Zealand, January 28-29, 2021, Revised Selected Papers 1 (pp. 53-67). Springer International Publishing.
    [CrossRef]   [Google Scholar]
  42. Roy, K., Chaudhuri, S. S., & Pramanik, S. (2021). Deep learning based real-time Industrial framework for rotten and fresh fruit detection using semantic segmentation. Microsystem Technologies, 27(9), 3365-3375.
    [CrossRef]   [Google Scholar]
  43. Dai, F., Shi, J., Yang, C., Li, Y., Zhao, Y., Liu, Z., ... & Dong, C. (2023). Detection of anthocyanin content in fresh Zijuan tea leaves based on hyperspectral imaging. Food Control, 152, 109839.
    [CrossRef]   [Google Scholar]
  44. Hafiz, R., Haque, M. R., Rakshit, A., & Uddin, M. S. (2022). Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning. Journal of King Saud University-Computer and Information Sciences, 34(5), 1775-1784.
    [CrossRef]   [Google Scholar]
  45. Moses, K., Miglani, A., & Kankar, P. K. (2022). Deep CNN-based damage classification of milled rice grains using a high-magnification image dataset. Computers and Electronics in Agriculture, 195, 106811.
    [CrossRef]   [Google Scholar]
  46. Ketwongsa, W., Boonlue, S., & Kokaew, U. (2022). A new deep learning model for the classification of poisonous and edible mushrooms based on improved AlexNet convolutional neural network. Applied Sciences, 12(7), 3409.
    [CrossRef]   [Google Scholar]
  47. Nguyen, K. T., Medjaher, K., & Tran, D. T. (2023). A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines. Artificial Intelligence Review, 56(4), 3659-3709.
    [CrossRef]   [Google Scholar]
  48. Singh, P., Pandey, V. K., Singh, R., Negi, P., Maurya, S. N., & Rustagi, S. (2024). Substantial Enhancement of Overall Efficiency and Effectiveness of the Pasteurization and Packaging Process Using Artificial Intelligence in the Food Industry. Food and Bioprocess Technology, 1-16.
    [CrossRef]   [Google Scholar]
  49. Samodro, B., Mahesworo, B., Suparyanto, T., Atmaja, D. B. S., & Pardamean, B. (2020, February). Maintaining the quality and aroma of coffee with fuzzy logic coffee roasting machine. In IOP Conference Series: Earth and Environmental Science (Vol. 426, No. 1, p. 012148). IOP Publishing.
    [CrossRef]   [Google Scholar]
  50. Barbut, S. (2020). Meat industry 4.0: A distant future?. Animal Frontiers, 10(4), 38-47.
    [CrossRef]   [Google Scholar]
  51. Ozturk, S., Bowler, A., Rady, A., & Watson, N. J. (2023). Near-infrared spectroscopy and machine learning for classification of food powders during a continuous process. Journal of Food Engineering, 341, 111339.
    [CrossRef]   [Google Scholar]
  52. Yu, Q., Zhang, M., Mujumdar, A. S., & Li, J. (2024). AI-based additive manufacturing for future food: Potential applications, challenges and possible solutions. Innovative Food Science & Emerging Technologies, 103599.
    [CrossRef]   [Google Scholar]
  53. da Silva Cotrim, W., Felix, L. B., Minim, V. P. R., Campos, R. C., & Minim, L. A. (2021). Development of a hybrid system based on convolutional neural networks and support vector machines for recognition and tracking color changes in food during thermal processing. Chemical Engineering Science, 240, 116679.
    [CrossRef]   [Google Scholar]
  54. Fuentes, M. S., Zelaya, N. A. L., & Avila, J. L. O. (2020, April). Coffee fruit recognition using artificial vision and neural networks. In 2020 5th International Conference on Control and Robotics Engineering (ICCRE) (pp. 224-228). IEEE.
    [CrossRef]   [Google Scholar]
  55. Nazari, S., Karami, A., Bahiraei, M., Olfati, M., Goodarzi, M., & Khorasanizadeh, H. (2020). A novel technique based on artificial intelligence for modeling the required temperature of a solar bread cooker equipped with concentrator through experimental data. Food and Bioproducts Processing, 123, 437-449.
    [CrossRef]   [Google Scholar]
  56. Fabani, M. P., Capossio, J. P., Román, M. C., Zhu, W., Rodriguez, R., & Mazza, G. (2021). Producing non-traditional flour from watermelon rind pomace: Artificial neural network (ANN) modeling of the drying process. Journal of Environmental Management, 281, 111915.
    [CrossRef]   [Google Scholar]
  57. Sadhu, T., Banerjee, I., Lahiri, S. K., & Chakrabarty, J. (2020). Modeling and optimization of cooking process parameters to improve the nutritional profile of fried fish by robust hybrid artificial intelligence approach. Journal of Food Process Engineering, 43(9), e13478.
    [CrossRef]   [Google Scholar]
  58. Zhou, M., Wang, L., Wu, H., Li, Q., Li, M., Zhang, Z., ... & Zou, Z. (2022). Machine learning modeling and prediction of peanut protein content based on spectral images and stoichiometry. Lwt, 169, 114015.
    [CrossRef]   [Google Scholar]
  59. Wang, Y., Li, L., Liu, Y., Cui, Q., Ning, J., & Zhang, Z. (2021). Enhanced quality monitoring during black tea processing by the fusion of NIRS and computer vision. Journal of Food Engineering, 304, 110599.
    [CrossRef]   [Google Scholar]
  60. Jia, H., Yuan, W., Ren, Z., Ning, J., Xu, Y. Q., Wang, Y., & Deng, W. W. (2023). Cost-effective and sensitive indicator-displacement array (IDA) assay for quality monitoring of black tea fermentation. Food Chemistry, 403, 134340.
    [CrossRef]   [Google Scholar]
  61. Behera, S. K., Rath, A. K., & Sethy, P. K. (2021). Fruits yield estimation using Faster R-CNN with MIoU. Multimedia Tools and Applications, 80(12), 19043-19056.
    [CrossRef]   [Google Scholar]
  62. Mu, Y., Chen, T. S., Ninomiya, S., & Guo, W. (2020). Intact detection of highly occluded immature tomatoes on plants using deep learning techniques. Sensors, 20(10), 2984.
    [CrossRef]   [Google Scholar]
  63. Zhang, L., Wang, Y., Wei, Y., & An, D. (2022). Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel. Food Chemistry, 370, 131047.
    [CrossRef]   [Google Scholar]
  64. Yang, J., Huang, Y., Xu, H., Gu, D., Xu, F., Tang, J., ... & Yang, Y. (2020). Optimization of fungi co-fermentation for improving anthraquinone contents and antioxidant activity using artificial neural networks. Food Chemistry, 313, 126138.
    [CrossRef]   [Google Scholar]
  65. Li, B., Lin, Y., Yu, W., Wilson, D. I., & Young, B. R. (2021). Application of mechanistic modelling and machine learning for cream cheese fermentation pH prediction. Journal of Chemical Technology & Biotechnology, 96(1), 125-133.
    [CrossRef]   [Google Scholar]
  66. Mavani, N. R., Lim, C. Y., Hashim, H., Rahman, N. A., & Ali, J. M. (2021). Fuzzy Mamdani based user-friendly interface for food preservatives determination. Food and Bioproducts Processing, 126, 282-292.
    [CrossRef]   [Google Scholar]
  67. Fan, N., Ma, X., Liu, G., Ban, J., Yuan, R., & Sun, Y. (2021). Rapid determination of TBARS content by hyperspectral imaging for evaluating lipid oxidation in mutton. Journal of Food Composition and Analysis, 103, 104110.
    [CrossRef]   [Google Scholar]
  68. Liu, Z., Zhang, R., Yang, C., Hu, B., Luo, X., Li, Y., & Dong, C. (2022). Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 271, 120921.
    [CrossRef]   [Google Scholar]
  69. Wang, Y., Liu, Y., Cui, Q., Li, L., Ning, J., & Zhang, Z. (2021). Monitoring the withering condition of leaves during black tea processing via the fusion of electronic eye (E-eye), colorimetric sensing array (CSA), and micro-near-infrared spectroscopy (NIRS). Journal of Food Engineering, 300, 110534.
    [CrossRef]   [Google Scholar]
  70. Jin, G., Wang, Y., Li, L., Shen, S., Deng, W. W., Zhang, Z., & Ning, J. (2020). Intelligent evaluation of black tea fermentation degree by FT-NIR and computer vision based on data fusion strategy. Lwt, 125, 109216.
    [CrossRef]   [Google Scholar]
  71. Jie, D., Wu, S., Wang, P., Li, Y., Ye, D., & Wei, X. (2021). Research on Citrus grandis granulation determination based on hyperspectral imaging through deep learning. Food Analytical Methods, 14, 280-289.
    [CrossRef]   [Google Scholar]
  72. Zhang, M., Jiang, Y., Li, C., & Yang, F. (2020). Fully convolutional networks for blueberry bruising and calyx segmentation using hyperspectral transmittance imaging. biosystems engineering, 192, 159-175.
    [CrossRef]   [Google Scholar]
  73. Zhao, S., Jiao, T., Adade, S. Y. S. S., Wang, Z., Wu, X., Li, H., & Chen, Q. (2024). Based on vis-NIR combined with ANN for on-line detection of bacterial concentration during kombucha fermentation. Food Bioscience, 60, 104346.
    [CrossRef]   [Google Scholar]
  74. Wang, S., Zhao, S., Wang, N., Lu, Q., Zhao, H., Liu, Y., ... & Fan, L. (2024). Intelligence detection of oil absorption in French fries by surface profiles. Food Research International, 178, 113906.
    [CrossRef]   [Google Scholar]
  75. Liu, Y., Pu, H., & Sun, D. W. (2021). Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices. Trends in Food Science & Technology, 113, 193-204.
    [CrossRef]   [Google Scholar]
  76. Anderssen, K. E., Stormo, S. K., Skåra, T., Skjelvareid, M. H., & Heia, K. (2020). Predicting liquid loss of frozen and thawed cod from hyperspectral imaging. LWT, 133, 110093.
    [CrossRef]   [Google Scholar]
  77. Rahman, M. F., Hashem, M. A., Mustari, A., Goswami, P. K., Hasan, M. M., & Rahman, M. M. (2023). Predict the quality and safety of chicken sausage through computer vision technology. Meat Research, 3(1).
    [CrossRef]   [Google Scholar]
  78. Ma, J., & Sun, D. W. (2020). Prediction of monounsaturated and polyunsaturated fatty acids of various processed pork meats using improved hyperspectral imaging technique. Food Chemistry, 321, 126695.
    [CrossRef]   [Google Scholar]
  79. Feng, Y., Li, X., Zhang, Y., & xie, T. (2023). Detection of Atlantic salmon residues based on computer vision. Journal of Food Engineering, 358, 111658.
    [CrossRef]   [Google Scholar]
  80. xie, T., Li, X., Zhang, X., Hu, J., & Fang, Y. (2021). Detection of Atlantic salmon bone residues using machine vision technology. Food Control, 123, 107787.
    [CrossRef]   [Google Scholar]
  81. Dutta, J., Deshpande, P., & Rai, B. (2021). AI-based soft-sensor for shelf life prediction of ‘Kesar’mango. SN Applied Sciences, 3(6), 657.
    [CrossRef]   [Google Scholar]
  82. Ullah, A., Liu, Y., Wang, Y., Gao, H., Wang, H., Zhang, J., & Li, G. (2022). E-Taste: Taste sensations and flavors based on tongue’s electrical and thermal stimulation. Sensors, 22(13), 4976.
    [CrossRef]   [Google Scholar]
  83. El-Mesery, H. S., Qenawy, M., Li, J., El-Sharkawy, M., & Du, D. (2024). Predictive modeling of garlic quality in hybrid infrared-convective drying using artificial neural networks. Food and Bioproducts Processing, 145, 226-238.
    [CrossRef]   [Google Scholar]
  84. Gutiérrez, P., Godoy, S. E., Torres, S., Oyarzún, P., Sanhueza, I., Díaz-García, V., ... & Coelho, P. (2020). Improved antibiotic detection in raw milk using machine learning tools over the absorption spectra of a problem-specific nanobiosensor. Sensors, 20(16), 4552.
    [CrossRef]   [Google Scholar]
  85. Zhou, Y., Lentz, E., Michelson, H., Kim, C., & Baylis, K. (2022). Machine learning for food security: Principles for transparency and usability. Applied Economic Perspectives and Policy, 44(2), 893-910.
    [CrossRef]   [Google Scholar]
  86. Barthwal, R., Kathuria, D., Joshi, S., Kaler, R. S. S., & Singh, N. (2024). New trends in the development and application of artificial intelligence in food processing. Innovative Food Science & Emerging Technologies, 103600.
    [CrossRef]   [Google Scholar]
  87. Wang, C., Hao, T., Wang, Z., Lin, H., Wei, W., Hu, Y., ... & Guo, Z. (2023). Machine learning-assisted cell-imprinted electrochemical impedance sensor for qualitative and quantitative analysis of three bacteria. Sensors and Actuators B: Chemical, 384, 133672.
    [CrossRef]   [Google Scholar]
  88. Du, Y., Han, D., Liu, S., Sun, X., Ning, B., Han, T., ... & Gao, Z. (2022). Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria. Talanta, 237, 122901.
    [CrossRef]   [Google Scholar]
  89. Ma, J., Guan, Y., Xing, F., Eltzov, E., Wang, Y., Li, X., & Tai, B. (2023). Accurate and non-destructive monitoring of mold contamination in foodstuffs based on whole-cell biosensor array coupling with machine-learning prediction models. Journal of Hazardous Materials, 449, 131030.
    [CrossRef]   [Google Scholar]
  90. Liu, W., Deng, H., Shi, Y., Liu, C., & Zheng, L. (2022). Application of multispectral imaging combined with machine learning methods for rapid and non-destructive detection of zearalenone (ZEN) in maize. Measurement, 203, 111944.
    [CrossRef]   [Google Scholar]
  91. Jiang, S., He, H., Ma, H., Chen, F., Xu, B., Liu, H., ... & Zhao, S. (2021). Quick assessment of chicken spoilage based on hyperspectral NIR spectra combined with partial least squares regression. International Journal of Agricultural and Biological Engineering, 14(1), 243-250.
    [CrossRef]   [Google Scholar]
  92. Li, H., Geng, W., Hassan, M. M., Zuo, M., Wei, W., Wu, X., ... & Chen, Q. (2021). Rapid detection of chloramphenicol in food using SERS flexible sensor coupled artificial intelligent tools. Food Control, 128, 108186.
    [CrossRef]   [Google Scholar]
  93. Yu, K., Fang, S., & Zhao, Y. (2021). Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 245, 118917.
    [CrossRef]   [Google Scholar]
  94. Park, B., Shin, T., Kang, R., Fong, A., McDonogh, B., & Yoon, S. C. (2023). Automated segmentation of foodborne bacteria from chicken rinse with hyperspectral microscope imaging and deep learning methods. Computers and Electronics in Agriculture, 208, 107802.
    [CrossRef]   [Google Scholar]
  95. Ziyaee, P., Farzand Ahmadi, V., Bazyar, P., & Cavallo, E. (2021). Comparison of different image processing methods for segregation of peanut (Arachis hypogaea L.) seeds infected by aflatoxin-producing fungi. Agronomy, 11(5), 873.
    [CrossRef]   [Google Scholar]
  96. Gao, J., Zhao, L., Li, J., Deng, L., Ni, J., & Han, Z. (2021). Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level. Food Chemistry, 360, 129968.
    [CrossRef]   [Google Scholar]
  97. Wang, D., Greenwood, P., & Klein, M. S. (2021). Deep learning for rapid identification of microbes using metabolomics profiles. Metabolites, 11(12), 863.
    [CrossRef]   [Google Scholar]
  98. Kolosov, D., Fengou, L. C., Carstensen, J. M., Schultz, N., Nychas, G. J., & Mporas, I. (2023). Microbiological quality estimation of meat using deep CNNs on embedded hardware systems. Sensors, 23(9), 4233.
    [CrossRef]   [Google Scholar]
  99. Zhu, J., Sharma, A. S., Xu, J., Xu, Y., Jiao, T., Ouyang, Q., ... & Chen, Q. (2021). Rapid on-site identification of pesticide residues in tea by one-dimensional convolutional neural network coupled with surface-enhanced Raman scattering. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 246, 118994.
    [CrossRef]   [Google Scholar]
  100. Seo, Y., Kim, G., Lim, J., Lee, A., Kim, B., Jang, J., ... & Kim, M. S. (2021). Non-destructive detection pilot study of vegetable organic residues using VNIR hyperspectral imaging and deep learning techniques. Sensors, 21(9), 2899.
    [CrossRef]   [Google Scholar]
  101. Sha, X., Fang, G., Cao, G., Li, S., Hasi, W., & Han, S. (2022). Qualitative and quantitative detection and identification of two benzodiazepines based on SERS and convolutional neural network technology. Analyst, 147(24), 5785-5795.
    [CrossRef]   [Google Scholar]
  102. Salam, S., Kheiralipour, K., & Jian, F. (2022). Detection of unripe kernels and foreign materials in chickpea mixtures using image processing. Agriculture, 12(7), 995.
    [CrossRef]   [Google Scholar]
  103. Sánchez, C. N., Orvañanos-Guerrero, M. T., Domínguez-Soberanes, J., & Álvarez-Cisneros, Y. M. (2023). Analysis of beef quality according to color changes using computer vision and white-box machine learning techniques. Heliyon, 9(7).
    [CrossRef]   [Google Scholar]
  104. Tang, Y., Wang, F., Zhao, X., Yang, G., Xu, B., Zhang, Y., ... & Li, L. (2023). A nondestructive method for determination of green tea quality by hyperspectral imaging. Journal of Food Composition and Analysis, 123, 105621.
    [CrossRef]   [Google Scholar]
  105. Ren, G., Wang, Y., Ning, J., & Zhang, Z. (2020). Using near-infrared hyperspectral imaging with multiple decision tree methods to delineate black tea quality. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 237, 118407.
    [CrossRef]   [Google Scholar]
  106. Li, L., Wang, Y., Cui, Q., Liu, Y., Ning, J., & Zhang, Z. (2022). Qualitative and quantitative quality evaluation of black tea fermentation through noncontact chemical imaging. Journal of Food Composition and Analysis, 106, 104300.
    [CrossRef]   [Google Scholar]
  107. Wang, Y. J., Li, T. H., Li, L. Q., Ning, J. M., & Zhang, Z. Z. (2020). Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 237, 118403.
    [CrossRef]   [Google Scholar]
  108. Li, L., Wang, Y., Jin, S., Li, M., Chen, Q., Ning, J., & Zhang, Z. (2021). Evaluation of black tea by using smartphone imaging coupled with micro-near-infrared spectrometer. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 246, 118991.
    [CrossRef]   [Google Scholar]
  109. Pradana-López, S., Pérez-Calabuig, A. M., Otero, L., Cancilla, J. C., & Torrecilla, J. S. (2022). Is my food safe?–AI-based classification of lentil flour samples with trace levels of gluten or nuts. Food Chemistry, 386, 132832.
    [CrossRef]   [Google Scholar]
  110. Kalinichenko, A., & Arseniyeva, L. (2020). Electronic nose combined with chemometric approaches to assess authenticity and adulteration of sausages by soy protein. Sensors and Actuators B: Chemical, 303, 127250.
    [CrossRef]   [Google Scholar]
  111. Yang, X., Chen, J., Jia, L., Yu, W., Wang, D., Wei, W., ... & Wu, D. (2020). Rapid and non-destructive detection of compression damage of yellow peach using an electronic nose and chemometrics. Sensors, 20(7), 1866.
    [CrossRef]   [Google Scholar]
  112. Munera, S., Gómez-Sanchís, J., Aleixos, N., Vila-Francés, J., Colelli, G., Cubero, S., ... & Blasco, J. (2021). Discrimination of common defects in loquat fruit cv.‘Algerie’using hyperspectral imaging and machine learning techniques. Postharvest Biology and Technology, 171, 111356.
    [CrossRef]   [Google Scholar]
  113. Hitchman, S., Loeffen, M. P. F., Reis, M. M., & Craigie, C. R. (2021). Robustness of hyperspectral imaging and PLSR model predictions of intramuscular fat in lamb M. longissimus lumborum across several flocks and years. Meat Science, 179, 108492.
    [CrossRef]   [Google Scholar]
  114. Wang, Y., He, H., Jiang, S., & Ma, H. (2022). Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics. International Journal of Agricultural and Biological Engineering, 15(1), 277-284.
    [CrossRef]   [Google Scholar]
  115. Zhuang, Q., Peng, Y., Yang, D., Wang, Y., Zhao, R., Chao, K., & Guo, Q. (2022). Detection of frozen pork freshness by fluorescence hyperspectral image. Journal of Food Engineering, 316, 110840.
    [CrossRef]   [Google Scholar]
  116. An, T., Yu, H., Yang, C., Liang, G., Chen, J., Hu, Z., ... & Dong, C. (2020). Black tea withering moisture detection method based on convolution neural network confidence. Journal of Food Process Engineering, 43(7), e13428.
    [CrossRef]   [Google Scholar]
  117. Hakim, M., Djatna, T., & Yuliasih, I. (2020, October). Deep learning for roasting coffee bean quality assessment using computer vision in mobile environment. In 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 363-370). IEEE.
    [CrossRef]   [Google Scholar]
  118. Thazin, Y., Pobkrut, T., & Kerdcharoen, T. (2018, January). Prediction of acidity levels of fresh roasted coffees using e-nose and artificial neural network. In 2018 10th International Conference on Knowledge and Smart Technology (KST) (pp. 210-215). IEEE.
    [CrossRef]   [Google Scholar]
  119. Rong, D., Wang, H., Xie, L., Ying, Y., & Zhang, Y. (2020). Impurity detection of juglans using deep learning and machine vision. Computers and Electronics in Agriculture, 178, 105764.
    [CrossRef]   [Google Scholar]
  120. Kong, D., Shi, Y., Sun, D., Zhou, L., Zhang, W., Qiu, R., & He, Y. (2022). Hyperspectral imaging coupled with CNN: A powerful approach for quantitative identification of feather meal and fish by-product meal adulterated in marine fishmeal. Microchemical Journal, 180, 107517.
    [CrossRef]   [Google Scholar]
  121. Jiang, H., Cheng, F., & Shi, M. (2020). Rapid identification and visualization of jowl meat adulteration in pork using hyperspectral imaging. Foods, 9(2), 154.
    [CrossRef]   [Google Scholar]
  122. Jiang, H., Jiang, X., Ru, Y., Wang, J., Xu, L., & Zhou, H. (2020). Application of hyperspectral imaging for detecting and visualizing leaf lard adulteration in minced pork. Infrared Physics & Technology, 110, 103467.
    [CrossRef]   [Google Scholar]
  123. Moosavi-Nasab, M., Khoshnoudi-Nia, S., Azimifar, Z., & Kamyab, S. (2021). Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis. Scientific reports, 11(1), 5094.
    [CrossRef]   [Google Scholar]
  124. Zhou, X., Zhao, C., Sun, J., Cao, Y., Yao, K., & Xu, M. (2023). A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging. Food Chemistry, 409, 135251.
    [CrossRef]   [Google Scholar]
  125. Pu, H., Wei, Q., & Sun, D. W. (2023). Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications. Critical Reviews in Food Science and Nutrition, 63(10), 1297-1313.
    [CrossRef]   [Google Scholar]
  126. Cai, C., Zhou, F., Chu, R., Ye, H., Zhang, C., Shui, L., & Liu, Y. (2024). Rapid and sensitive in-situ detection of pesticide residues in real tea soup with optical fiber SERS probes. Journal of Food Composition and Analysis, 134, 106520.
    [CrossRef]   [Google Scholar]
  127. Li, H., Luo, X., Haruna, S. A., Zareef, M., Chen, Q., Ding, Z., & Yan, Y. (2023). Au-Ag OHCs-based SERS sensor coupled with deep learning CNN algorithm to quantify thiram and pymetrozine in tea. Food Chemistry, 428, 136798.
    [CrossRef]   [Google Scholar]
  128. Wang, Y., Li, M., Li, L., Ning, J., & Zhang, Z. (2021). Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer. Food Chemistry, 345, 128816.
    [CrossRef]   [Google Scholar]
  129. Ren, G., Gan, N., Song, Y., Ning, J., & Zhang, Z. (2021). Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics. Microchemical Journal, 160, 105600.
    [CrossRef]   [Google Scholar]
  130. Li, L., Xie, S., Ning, J., Chen, Q., & Zhang, Z. (2019). Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems. Journal of the Science of Food and Agriculture, 99(4), 1787-1794.
    [CrossRef]   [Google Scholar]
  131. Wei, Q., Lv, M., Wang, B., Sun, J., & Wang, D. (2023). A comparative study of optimized conditions of QuEChERS to determine the pesticide multiresidues in Lycium barbarum using response surface methodology and genetic algorithm-artificial neural network. Journal of Food Composition and Analysis, 120, 105356.
    [CrossRef]   [Google Scholar]
  132. Metri-Ojeda, J., Solana-Lavalle, G., Rosas-Romero, R., Palou, E., & Baigts-Allende, D. (2023). Rapid screening of mayonnaise quality using computer vision and machine learning. Journal of Food Measurement and Characterization, 17(3), 2792-2804.
    [CrossRef]   [Google Scholar]
  133. Han, Y., Liu, Z., Khoshelham, K., & Bai, S. H. (2021). Quality estimation of nuts using deep learning classification of hyperspectral imagery. Computers and Electronics in Agriculture, 180, 105868.
    [CrossRef]   [Google Scholar]
  134. Pradana-Lopez, S., Perez-Calabuig, A. M., Cancilla, J. C., Garcia-Rodriguez, Y., & Torrecilla, J. S. (2022). Convolutional capture of the expansion of extra virgin olive oil droplets to quantify adulteration. Food Chemistry, 368, 130765.
    [CrossRef]   [Google Scholar]
  135. Wu, X., Du, Z., Ma, R., Zhang, X., Yang, D., Liu, H., & Zhang, Y. (2024). Qualitative and quantitative studies of phthalates in extra virgin olive oil (EVOO) by surface-enhanced Raman spectroscopy (SERS) combined with long short term memory (LSTM) neural network. Food Chemistry, 433, 137300.
    [CrossRef]   [Google Scholar]
  136. Zhang, X., Sun, J., Li, P., Zeng, F., & Wang, H. (2021). Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method. Lwt, 152, 112295.
    [CrossRef]   [Google Scholar]

Cite This Article
APA Style
Wang, Y. (2025). Application of Artificial Intelligence in Food Industry: A Review. Agricultural Science and Food Processing, 2(2), 68–88. https://doi.org/10.62762/ASFP.2025.552607

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 242
PDF Downloads: 45

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
CC BY Copyright © 2025 by the Author(s). Published by Institute of Emerging and Computer Engineers. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Agricultural Science and Food Processing

Agricultural Science and Food Processing

ISSN: 3066-1579 (Online) | ISSN: 3066-1560 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2025 Institute of Emerging and Computer Engineers Inc.