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Integration of AI based tools in dairy quality control: Enhancing pathogen detection efficiency

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Abstract

The traditional methods of safety and quality check are effective but often entail time consuming processes and may lack sensitivity required for early detection. This warrants ways to improve pathogen detection efficiency by investigating the application of artificial intelligence (AI) in dairy quality control. The increase in milk output emphasizes the necessity of updated quality control procedures. AI solutions give farmers previously unheard-of chances to boost output, improve sustainability, and use less resources. AI is used in dairy farming for a variety of purposes, including data entry, economic analysis, and enhancing animal health. Exploring AI's historical foundations, the paper highlights AI's function as a collaborator rather than a rival in human association. It discusses several uses of AI in the dairy sector, such as milking robots, drones, and the Internet of Things (IoT). Support vector machines, machine learning, and artificial neural networks are investigated as AI approaches that improve the productivity of dairy farms. In this review, traditional techniques for identifying pathogens in milk such as PCR, ELISA, and culture-based approaches are examined. The paper also examines case studies and empirical evidence highlighting the efficacy of AI-based tools in pathogen detection across various stages of dairy production and distribution. Additionally, it discusses the regulatory landscape and potential challenges associated with the widespread adoption of AI technologies in dairy quality control. The assessment ends with future prospects, highlighting the potential for cooperation between AI developers and dairy stakeholders to enhance supply chain security, operational procedures, and business models. All things considered, the application of AI to dairy quality control shows potential for improving productivity and ensuring the security of dairy products in the constantly shifting dairy market.

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Data Availability

This review article does not involve the generation or analysis of new data. All information discussed in this manuscript is derived from previously published sources, which are appropriately cited in the references list.

References

  1. S.A. Sotohy, S.M. Emam, R.M. Ewida, Prevalence of salmonella species in milk and milk products in New Valley governorate. New. Valley. Vet. J. 5(1), 57–60 (2025). https://doi.org/10.21608/nvvj.2024.292218.1046

    Article  Google Scholar 

  2. O. Tomar, G. Akarca, Critical control points and food pathogen presence in dairy plants from Turkey. Food. Sci. Technol. 39(2), 444–450 (2019). https://doi.org/10.1590/fst.29717

    Article  Google Scholar 

  3. O.S. Chukwu et al., Isolation, identification and antibiotic resistance profile of public health threat enteric bacteria from milk and dairy products retail in Abakaliki, South-East, Nigeria. J. Pure. Appl. Microbiol. 17(3), 1620–1627 (2023). https://doi.org/10.22207/JPAM.17.3.23

    Article  Google Scholar 

  4. B. Ang et al., Rapid concentration and detection of bacteria in milk using a microfluidic surface acoustic wave activated nanosieve. ACS Sens. 9(6), 3105–3114 (2024). https://doi.org/10.1021/acssensors.4c00291

    Article  CAS  PubMed  Google Scholar 

  5. Q. Li, J. Zhang, X. Chen, T. Jiang, L. Lin, L. Zhao, Real-time and visual detection of viable Salmonella in milk from remote pasture via IMS-LAMP-NALFS. Microchem. J. 197, 109732 (2024). https://doi.org/10.1016/j.microc.2023.109732

    Article  CAS  Google Scholar 

  6. A. El Sheikha, DNAFoil, a novel technology for the rapid detection of food pathogens: Preliminary validation on Salmonella and Listeria monocytogenes. Ital. J. Food. Sci. 33(SP1), 43–54 (2021). https://doi.org/10.15586/ijfs.v33iSP1.1995

    Article  Google Scholar 

  7. H. Patel, A. Samad, M. Hamza, A. Muazzam, M.K. Harahap, Role of artificial intelligence in livestock and poultry farming. SinkrOn 7(4), 2425–2429 (2022). https://doi.org/10.33395/sinkron.v7i4.11837

    Article  Google Scholar 

  8. S. Neethirajan, Affective state recognition in livestock—artificial intelligence approaches. Animals 12(6), 759 (2022). https://doi.org/10.3390/ani12060759

    Article  PubMed  PubMed Central  Google Scholar 

  9. D.A. Isabelle, M. Westerlund, A review and categorization of artificial intelligence-based opportunities in wildlife, ocean and land conservation. Sustainability 14(4), 1979 (2022). https://doi.org/10.3390/su14041979

    Article  Google Scholar 

  10. P. Ezanno et al., Research perspectives on animal health in the era of artificial intelligence. Vet. Res. 52(1), 40 (2021). https://doi.org/10.1186/s13567-021-00902-4

    Article  PubMed  PubMed Central  Google Scholar 

  11. A. Zuraw, F. Aeffner, Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: an updated introduction and review. Vet. Pathol. 59(1), 6–25 (2022). https://doi.org/10.1177/03009858211040484

    Article  PubMed  Google Scholar 

  12. S. Fuentes, C. Gonzalez Viejo, E. Tongson, F.R. Dunshea, The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence. Anim. Health. Res. Rev. 23(1), 59–7 (2022). https://doi.org/10.1017/S1466252321000177

    Article  PubMed  Google Scholar 

  13. J.V. Congdon, M. Hosseini, E.F. Gading, M. Masousi, M. Franke, S.E. MacDonald, The future of artificial intelligence in monitoring animal identification, health, and behaviour. Animals 12(13), 1711 (2022). https://doi.org/10.3390/ani12131711

    Article  PubMed  PubMed Central  Google Scholar 

  14. Y. Liu, Research on the application of artificial intelligence technology in animal embryo transfer. J. Phys. Conf.: Ser 1852(4), 042035 (2021). https://doi.org/10.1088/1742-6596/1852/4/042035

    Article  Google Scholar 

  15. R. Amin, M. Rahman, Artificial intelligence and IoT in dairy farm. Malays. J. Med. Biol. Res. 5(2), 131–140 (2018). https://doi.org/10.18034/mjmbr.v5i2.516

    Article  Google Scholar 

  16. S. Agatonovic-Kustrin, R. Beresford, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 22(5), 717–727 (2000). https://doi.org/10.1016/S0731-7085(99)00272-1

    Article  CAS  PubMed  Google Scholar 

  17. A. De Vries, N. Bliznyuk, P. Pinedo, Invited review: examples and opportunities for artificial intelligence (AI) in dairy farms*. Appl. Anim. Sci. 39(1), 14–22 (2023). https://doi.org/10.15232/aas.2022-02345

    Article  Google Scholar 

  18. K.J. McKay, C. Li, C. Sotomayor-Castillo, P.E. Ferguson, M. Wyer, R.Z. Shaban, Health care workers’ experiences of video-based monitoring of hand hygiene behaviors: a qualitative study. Am. J. Infect. Control. 51(1), 83–88 (2023). https://doi.org/10.1016/j.ajic.2022.03.010

    Article  PubMed  Google Scholar 

  19. S.J. Denholm et al., Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning. J. Dairy. Sci. 103(10), 9355–9367 (2020). https://doi.org/10.3168/jds.2020-18328

    Article  CAS  PubMed  Google Scholar 

  20. Z. Xudong, K. Xi, F. Ningning, L. Gang, Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector. Comput. Electron. Agric. 178, 105754 (2020). https://doi.org/10.1016/j.compag.2020.105754

    Article  Google Scholar 

  21. G. Van Steenkiste, I. Van Den Brulle, S. Piepers, S. De Vliegher, In-line detection of clinical mastitis by identifying clots in milk using images and a neural network approach. Animals 13(24), 3783 (2023). https://doi.org/10.3390/ani13243783

    Article  PubMed  PubMed Central  Google Scholar 

  22. R. Reissbrodt, New chromogenic plating media for detection and enumeration of pathogenic Listeria spp.—an overview. Int. J. Food. Microbiol. 95(1), 1–9 (2004). https://doi.org/10.1016/j.ijfoodmicro.2004.01.025

    Article  CAS  PubMed  Google Scholar 

  23. S.L. Scotter et al., Validation of ISO method 11290 Part 1 — detection of listeria monocytogenes in foods. Int. J. Food. Microbiol. 64(3), 295–306 (2001). https://doi.org/10.1016/S0168-1605(00)00462-1

    Article  CAS  PubMed  Google Scholar 

  24. V. Velusamy, K. Arshak, O. Korostynska, K. Oliwa, C. Adley, An overview of foodborne pathogen detection: In the perspective of biosensors. Biotechnol. Adv. 28(2), 232–254 (2010). https://doi.org/10.1016/j.biotechadv.2009.12.004

    Article  CAS  PubMed  Google Scholar 

  25. J. Navas, S. Ortiz, P. Lopez, M.M. Jantzen, V. Lopez, J.V. Martinez-Suarez, Evaluation of effects of primary and secondary enrichment for the detection of listeria monocytogenes by real-time pcr in retail ground chicken meat. Foodborne. Pathog. Dis. 3(4), 347–354 (2006). https://doi.org/10.1089/fpd.2006.3.347

    Article  CAS  PubMed  Google Scholar 

  26. S. Ueda, T. Maruyama, Y. Kuwabara, Detection of listeria monocytogenes from food samples by PCR after IMS-plating. Biocontrol. Sci. 11(3), 129–134 (2006). https://doi.org/10.4265/bio.11.129

    Article  CAS  PubMed  Google Scholar 

  27. B. Byrne, E. Stack, N. Gilmartin, R. O’Kennedy, Antibody-based sensors: principles, problems and potential for detection of pathogens and associated toxins. Sensors 9(6), 4407–4445 (2009). https://doi.org/10.3390/s90604407

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. S. Xiulan, Z. Xiaolian, T. Jian, J. Zhou, F.S. Chu, Preparation of gold-labeled antibody probe and its use in immunochromatography assay for detection of aflatoxin B1. Int. J. Food. Microbiol. 99(2), 185–194 (2005). https://doi.org/10.1016/j.ijfoodmicro.2004.07.021

    Article  CAS  PubMed  Google Scholar 

  29. J. Wang et al., A novel, universal and sensitive lateral-flow based method for the detection of multiple bacterial contamination in platelet concentrations. Anal. Sci. 28(3), 237–241 (2012). https://doi.org/10.2116/analsci.28.237

    Article  CAS  PubMed  Google Scholar 

  30. 서성민 et al, An ELISA-on-a-chip biosensor system for early screening of listeria monocytogenes in contaminated food products. Bull. Korean Chem. Soc. 30(12), pp. 2993–2998 (2009). https://doi.org/10.5012/BKCS.2009.30.12.2993

  31. P.M. Fratamico, Comparison of culture, polymerase chain reaction (PCR), TaqMan Salmonella, and Transia card Salmonella assays for detection of Salmonella spp. in naturally-contaminated ground chicken, ground turkey, and ground beef. Mol. Cell. Probes. 17(5), 215–221 (2003). https://doi.org/10.1016/S0890-8508(03)00056-2

    Article  CAS  PubMed  Google Scholar 

  32. A.K. Deisingh, M. Thompson, Strategies for the detection of Escherichia coli O157:H7 in foods. J. Appl. Microbiol. 96(3), 419–429 (2004). https://doi.org/10.1111/j.1365-2672.2003.02170.x

    Article  CAS  PubMed  Google Scholar 

  33. K. Mullis, F. Faloona, S. Scharf, R. Saiki, G. Horn, H. Erlich, Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold. Spring. Harb. Symp. Quant. Biol. 51, 263–273 (1986). https://doi.org/10.1101/SQB.1986.051.01.032

    Article  CAS  PubMed  Google Scholar 

  34. D. Rodríguez-Lázaro, M. D’Agostino, A. Herrewegh, M. Pla, N. Cook, J. Ikonomopoulos, Real-time PCR-based methods for detection of mycobacterium avium subsp. paratuberculosis in water and milk. Int. J. Food. Microbiol. 101(1), 93–104 (2005). https://doi.org/10.1016/j.ijfoodmicro.2004.09.005

    Article  CAS  PubMed  Google Scholar 

  35. A. Jofré et al., Simultaneous detection of listeria monocytogenes and Salmonella by multiplex PCR in cooked ham. Food. Microbiol. 22(1), 109–115 (2005). https://doi.org/10.1016/j.fm.2004.04.009

    Article  CAS  Google Scholar 

  36. J. R. Crowther. Basic principles of in ELISA, New Jersey: Humana press. ELISA 42 pp. 35–62. (1995) https://doi.org/10.1385/0-89603-279-5:35

  37. S. Perelle, F. Dilasser, B. Malorny, J. Grout, J. Hoorfar, P. Fach, Comparison of PCR-ELISA and light cycler real-time PCR assays for detecting Salmonella spp. in milk and meat samples. Mol. Cell. Probes. 18(6), 409–420 (2004). https://doi.org/10.1016/j.mcp.2004.07.001

    Article  CAS  PubMed  Google Scholar 

  38. S. Leo, A. Cherkaoui, G. Renzi, J. Schrenzel, Mini review: clinical routine microbiology in the era of automation and digital health. Front. Cell. Infect. Microbiol. 10, 582028 (2020). https://doi.org/10.3389/fcimb.2020.582028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. I. Moon, F. Yi, B. Javidi, Automated three-dimensional microbial sensing and recognition using digital holography and statistical sampling. Sensors 10(9), 8437–8451 (2010). https://doi.org/10.3390/s100908437

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. T. Fukuda and O. Hasegawa, Expert system driven image processing for recognition and identification of microorganisms. In International Workshop on Industrial Applications of Machine Intelligence and Vision, Tokyo, Japan: IEEE, pp. 33–38. (1989). https://doi.org/10.1109/MIV.1989.40518.

  41. S. Yeom, B. Javidi, Automatic identification of biological microorganisms using three-dimensional complex morphology. J. Biomed. Opt. 11(2), 024017 (2006). https://doi.org/10.1117/1.2187017

    Article  PubMed  Google Scholar 

  42. P. M. Iftikhar, M. V. Kuijpers, A. Khayyat, A. Iftikhar, and M. DeGouvia De Sa, Artificial intelligence: a new paradigm in obstetrics and gynecology research and clinical practice. Cureus, (2020). https://doi.org/10.7759/cureus.7124

  43. O. Tokel et al., Portable microfluidic integrated plasmonic platform for pathogen detection. Sci. Rep. 5(1), 9152 (2015). https://doi.org/10.1038/srep09152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. K. Hsieh, B.S. Ferguson, M. Eisenstein, K.W. Plaxco, H.T. Soh, Integrated electrochemical microsystems for genetic detection of pathogens at the point of care. Acc. Chem. Res. 48(4), 911–920 (2015). https://doi.org/10.1021/ar500456w

    Article  CAS  PubMed  Google Scholar 

  45. C.on. A. Nutrition, Nutrition Requirement of dairy cattle. Washington. National Academics Press (2001). Available at: https://www.nap.edu/catalog/9825.html

  46. V.E. Cabrera, Invited review: helping dairy farmers to improve economic performance utilizing data-driving decision support tools. Animal 12(1), 134–144 (2018). https://doi.org/10.1017/S1751731117001665

    Article  CAS  PubMed  Google Scholar 

  47. A. Kumar, G.P. Hancke, A zigbee-based animal health monitoring system. IEEE Sensors. J. 15(1), 610–617 (2015). https://doi.org/10.1109/JSEN.2014.2349073

    Article  Google Scholar 

  48. É. Hajnal, L. Kovács, G. Vakulya, Dairy cattle rumen bolus developments with special regard to the applicable artificial intelligence (AI) methods. Sensors 22(18), 6812 (2022). https://doi.org/10.3390/s22186812

    Article  PubMed  PubMed Central  Google Scholar 

  49. C.E. Cardoso Consentini, M.C. Wiltbank, R. Sartori, Factors that optimize reproductive efficiency in dairy herds with an emphasis on timed artificial insemination programs. Animals 11(2), 301 (2021). https://doi.org/10.3390/ani11020301

    Article  PubMed  PubMed Central  Google Scholar 

  50. F. Schirdewahn, H.H.K. Lentz, V. Colizza, A. Koher, P. Hövel, B. Vidondo, Early warning of infectious disease outbreaks on cattle-transport networks. PLoS. ONE. 16(1), e0244999 (2021). https://doi.org/10.1371/journal.pone.0244999

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. W. Chen et al., Invited review: advancements in lateral flow immunoassays for screening hazardous substances in milk and milk powder. J. Dairy. Sci. 102(3), 1887–1900 (2019). https://doi.org/10.3168/jds.2018-15462

    Article  CAS  PubMed  Google Scholar 

  52. W. Mu et al., Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools. Comp. Rev. Food. Sci. Food. Safe. 23(1), e13296 (2024). https://doi.org/10.1111/1541-4337.13296

    Article  Google Scholar 

  53. C. Qian, S.I. Murphy, R.H. Orsi, M. Wiedmann, How can AI help improve food safety? Annu. Rev. Food. Sci. Technol. 14(1), 517–538 (2023). https://doi.org/10.1146/annurev-food-060721-013815

    Article  CAS  PubMed  Google Scholar 

  54. H. Wang et al., Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light. Sci. Appl. 9(1), 118 (2020). https://doi.org/10.1038/s41377-020-00358-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. A. Sadilek et al., Machine-learned epidemiology: real-time detection of foodborne illness at scale. npj. Digital. Med. 1(1), 36 (2018). https://doi.org/10.1038/s41746-018-0045-1

    Article  Google Scholar 

  56. D.V. Carvalho, E.M. Pereira, J.S. Cardoso, Machine learning interpretability: a survey on methods and metrics. Electronics 8(8), 832 (2019). https://doi.org/10.3390/electronics8080832

    Article  Google Scholar 

  57. D. Weller, A. Belias, H. Green, S. Roof, M. Wiedmann, Landscape, water quality, and weather factors associated with an increased likelihood of foodborne pathogen contamination of new york streams used to source water for produce production. Front. Sustain. Food. Syst. 3, 124 (2020). https://doi.org/10.3389/fsufs.2019.00124

    Article  PubMed  PubMed Central  Google Scholar 

  58. L. Ma, J. Yi, N. Wisuthiphaet, M. Earles, N. Nitin, Accelerating the detection of bacteria in food using artificial intelligence and optical imaging. Appl. Environ. Microbiol. 89(1), e01828-e1922 (2023). https://doi.org/10.1128/aem.01828-22

    Article  CAS  PubMed  Google Scholar 

  59. D. Rivera et al., Approaches to empower the implementation of new tools to detect and prevent foodborne pathogens in food processing. Food. Microbiol. 75, 126–132 (2018). https://doi.org/10.1016/j.fm.2017.07.009

    Article  PubMed  Google Scholar 

  60. B.L.N. Garcia, C.M.D.M.R. Martins, L.F. Porto, D.B. Nobrega, M.V. Dos Santos, Accuracy of an AI-based automated plate reading mobile application for the identification of clinical mastitis-causing pathogens in chromogenic culture media. Sci. Rep. 14(1), 1208 (2024). https://doi.org/10.1038/s41598-023-50296-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. A. Garcia-Vozmediano et al., Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy. Vet. Res. 55(1), 72 (2024). https://doi.org/10.1186/s13567-024-01323-9

    Article  PubMed  PubMed Central  Google Scholar 

  62. K.M.C. Nogoy et al., Precision detection of real-time conditions of dairy cows using an advanced artificial intelligence hub. Appl. Sci. 11(24), 12043 (2021). https://doi.org/10.3390/app112412043

    Article  CAS  Google Scholar 

  63. L. Trapanese, M. Hostens, A. Salzano, N. Pasquino, Short review of current limits and challenges of application of machine learning algorithms in the dairy sector. Acta. IMEKO. 13(1), 1–7 (2024). https://doi.org/10.21014/actaimeko.v13i1.1725

    Article  Google Scholar 

  64. M.T. Linaza et al., Data-driven artificial intelligence applications for sustainable precision agriculture. Agronomy 11(6), 1227 (2021). https://doi.org/10.3390/agronomy11061227

    Article  Google Scholar 

  65. D.R. Pandey, N. Mishra, An integrated approach to dairy farming: AI and IoT-enabled monitoring of cows and crops via a mobile application. Bio. Web. Conf. 82, 05020 (2024). https://doi.org/10.1051/bioconf/20248205020

    Article  Google Scholar 

  66. A. Srikanth, Early detection of digital dermatitis in cattle using computer vision. The University of Wisconsin - Madison. Available at: https://asset.library.wisc.edu/1711.dl/5WCL5IVQYBNHX8L/R/file-09872.pdf

  67. R. U. Mhapsekar, L. Abraham, N. O’Shea, and S. Davy, Edge-AI Implementation for Milk Adulteration Detection. in 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Alamein New City, Egypt: IEEE, pp. 108–113 (2022). https://doi.org/10.1109/GCAIoT57150.2022.10019173

  68. A. Bosakova-Ardenska, Recent Trends in Computer Vision for Cheese Quality Evaluation. in CIEES 2023, MDPI, p. 12. (2024). https://doi.org/10.3390/engproc2024060012

  69. M. Durgun, Real-time milk quality control using multi-spectral sensing and edge computing: advancing on-site detection of milk components with XGBoost. Appl. Sci. 14(23), 10916 (2024). https://doi.org/10.3390/app142310916

    Article  CAS  Google Scholar 

  70. O. OztunaTaner, A.B. Çolak, Dairy factory milk product processing and sustainable of the shelf-life extension with artificial intelligence: a model study. Front. Sustain. Food Syst. 8, 1344370 (2024). https://doi.org/10.3389/fsufs.2024.1344370

    Article  Google Scholar 

  71. Z. Fang, D.L. Larson, G. Fleishmen, Exergy analysis of a milk processing system. Trans. ASAE. 38(6), 1825–1832 (1995). https://doi.org/10.13031/2013.28011

    Article  Google Scholar 

  72. M.O. Akbar et al., IoT for development of smart dairy farming. J. Food. Qual. 2020, 1–8 (2020). https://doi.org/10.1155/2020/4242805

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Agricultural Microbiology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

    Pooja Devi, Karthikeyan Subburamu & Balachandhar Dananjeyan

  2. Centre for Post Harvest Technology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

    Veeranan Arun Giridhari

  3. Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

    Thirunavukkarasu Maruthamuthu

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Devi, P., Subburamu, K., Giridhari, V.A. et al. Integration of AI based tools in dairy quality control: Enhancing pathogen detection efficiency. Food Measure 19, 4427–4438 (2025). https://doi.org/10.1007/s11694-025-03269-8

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