Applications of Artificial Intelligence in Dairy Cow Management

Document Type : Scientific-Extensional Article

Authors

1 M.Sc. Student of Animal and Poultry Breeding & Genetics, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Alborz, Iran

2 Professor of Animal Breeding and Genetics, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Alborz, Iran

3 Associate Professor of Animal Breeding and Genetics, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Alborz, Iran

Abstract

In modern dairy farming, artificial intelligence (AI) has emerged as a transformative force, fundamentally redefining livestock management practices. By leveraging advanced data analytics and machine learning algorithms, AI-driven systems contribute to enhanced productivity, optimized resource allocation, and improved herd health, addressing critical industry challenges.  A key application of AI lies in early disease detection, particularly for mastitis and lameness, where high-precision image processing and motion analysis enable proactive health monitoring, reducing treatment costs and minimizing production losses. AI also facilitates predictive analytics for optimal artificial insemination timing, utilizing deep learning models to improve fertility rates while identifying pregnancy-associated genetic markers, thereby advancing selective breeding strategies. Moreover, intelligent milking optimization systems employ machine learning to regulate pulsation parameters, streamline the milking process, and minimize physiological stress on dairy cattle. AI-driven feeding behavior analysis further enables precise diet formulation, ensuring optimal nutritional intake. Additionally, robotic automation in dairy operations, including autonomous milking units and precision livestock monitoring, has significantly improved farm efficiency while reducing human labor dependency. Despite these advancements, AI adoption in dairy farming faces economic, infrastructural, and ethical challenges, including high implementation costs, digital infrastructure requirements, and workforce displacement concerns. Research highlights the need for continuous investment in AI-driven innovation, ensuring technological refinement and practical implementation across livestock management. This study underscores the pivotal role of AI in modernizing dairy farming, advocating for the strategic integration of intelligent technologies to enhance operational efficiency, sustainability, and precision-driven decision-making.

Keywords

References

جعفری‌فر، ن. (1403). تولیدات علمی هوش مصنوعی (AI) در ایران: با تأکید بر حوزه‌های موضوعی. مطالعات علم‌سنجی کاربردی، 1(1)، 101-127.
خجسته کی، م.، صادقی پناه، ا.، اسدزاده، ن.، آقاشاهی، ع.، کیخا صابر، م.، بیطرف ثانی، م و اسماعیل خانیان، س. (1401). پیش بینی وزن بدن گاوهای سیستانی با استفاده از بینایی رایانه‌ای. تحقیقات تولیدات دامی، 11(3)، 55-66.
Bhat, R., Di Pasquale, J., Bánkuti, F. I., Siqueira, T. T. D. S., Shine, P., & Murphy, M. D. (2022). Global dairy sector: trends, prospects, and challenges. Sustainability, 14(7), 4193.
Bosale, S., Pujari, V., & Multani, Z. (2020). Advantages and disadvantages of artificial intelligence. Aayushi International Interdisciplinary Research Journal, 9(1), 227-230.
Dhaliwal, Y., Bi, H., & Neethirajan, S. (2025). Bimodal data analysis for early detection of lameness in dairy cows using artificial intelligence. Journal of Agriculture and Food Research, 21, 101837.
Fuentes, S., Gonzalez Viejo, C., Tongson, E., Lipovetzky, N., & Dunshea, F. R. (2021). Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence. Sensors, 21(20), 6844.
Gökçearslan, S., Tosun, C., & Erdemir, Z. G. (2024). Benefits, challenges, and methods of artificial intelligence (AI) chatbots in education: A systematic literature review. International Journal of Technology in Education, 7(1), 19-39.
Gulak, A. M. (2024). Research paper by Gulak 23 September 2024 Artificial Intelligence. Artif Intell.
Hajkowicz, S., Sanderson, C., Karimi, S., Bratanova, A., & Naughtin, C. (2023). Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021. Technology in Society, 74, 102260.
Hill, J. (2024). Science, technology, and innovation in the dairy sector. International Journal of Food Science and Technology, 59(9), 6717-6723.
Hoorn, Q. A., Rabaglino, M. B., Amaral, T. F., Maia, T. S., Yu, F., Cole, J. B., & Hansen, P. J. (2024). Machine learning to identify endometrial biomarkers predictive of pregnancy success following artificial insemination in dairy cows. Biology of Reproduction, 111(1), 54-62.
Kawagoe, Y., Kobayashi, I., & Zin, T. T. (2023). Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation. Agriculture, 13(5), 1016.
Liyin, Z. H. A. N. G., Ji, Z. H. A. N. G., Qinglu, Y. A. N. G., Yudao, L. I., Zhenwei, Y. U., Fuyang, T. I. A. N., & Sufang, Y. U. (2024). Detection of dairy cow feeding behavior based on video and BCE-YOLO model. Journal of South China Agricultural University, 45(5), 782-792.
Mahato, S., & Neethirajan, S. (2024). Integrating artificial intelligence in dairy farm management− biometric facial recognition for cows. Information Processing in Agriculture.
Melak, A., Aseged, T., & Shitaw, T. (2024). The Influence of Artificial Intelligence Technology on the Management of Livestock Farms. International Journal of Distributed Sensor Networks, 2024(1), 8929748.
Milani, M., Macit, M., & Hepkarşı, F. (2023). Ration Preparation of Dairy Cows with an Innovative Method: A Multi-Objective Optimization Approach. Electronic Letters on Science and Engineering, 19(2), 90-108.
Monteiro, H. F., Figueiredo, C. C., Mion, B., et al. (2024). An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows. Animal Microbiome, 6(5).
Mukhamediev, R. I., Popova, Y., Kuchin, Y., Zaitseva, E., Kalimoldayev, A., Symagulov, A., Levashenko, V., Abdoldina, F., Gopejenko, V., Yakunin, K., Muhamedijeva, E., & Yelis, M. (2022). Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges. Mathematics, 10(15), 2552.
Nagahara, M., Tatemoto, S., Ito, T., Fujimoto, O., Ono, T., Taniguchi, M., ... & Otoi, T. (2024). Designing a diagnostic method to predict the optimal artificial insemination timing in cows using artificial intelligence. Frontiers in Animal Science, 5, 1399434.
Neethirajan, S. (2023). Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. Sensors, 23(16), 7045.
Rebez, E. B., Sejian, V., Silpa, M. V., Kalaignazhal, G., Thirunavukkarasu, D., Devaraj, C., Nikhil, K. T., Ninan, J., Sahoo, A., Lacetera, N., & Dunshea, F. R. (2024). Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock. Sensors, 24(18), 5890.
Saghiri, A. M., Vahidipour, S. M., Jabbarpour, M. R., Sookhak, M., & Forestiero, A. (2022). A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions. Applied Sciences, 12(8), 4054.
Silva, R.A.B.D., Pandorfi, H., Cordeiro, F.R., Soares, R.G.F., Medeiros, V.W.C.D., Almeida, G.L.P.D., Barbosa Filho, J.A.D., Marinho, G.T.B. and Silva, M.V.D. (2024). A New Way to Identify Mastitis in Cows Using Artificial Intelligence. AgriEngineering, 6(4), 4220-4232.
Stahl, B. C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., ... & Wright, D. (2023). A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, 56(11), 12799-12831.
Strelkova, O. (2017). Three types of artificial intelligence.
Strong, A. I. (2016). Applications of artificial intelligence & associated technologies. Science [ETEBMS-2016], 5(6), 64-67.
Wang, J., Lovarelli, D., Rota, N., Shen, M., Lu, M., & Guarino, M. (2022). The potentialities of machine learning for cow-specific milking: Automatically setting variables in milking machines. Animals, 12(13), 1614
Wang, X., Shi, S., Ali Khan, M., et al. (2024). Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework. Journal of Animal Science and Biotechnology, 15(87).
Zhao, C., Wang, D., Teng, J., et al. (2023). Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data. Journal of Animal Science and Biotechnology, 14, 85.
Professional Journal of Domestic
Volume 25, Issue 2 - Serial Number 33
September 2025
Pages 33-43

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