AI-DRIVEN DAIRY FARMING: A CASE STUDY OF TECHNOLOGICAL ADOPTION AND ORGANIZATIONAL ADAPTATION IN A CZECH SME
DOI:
https://doi.org/10.59267/ekoPolj25041437SKeywords:
Artificial intelligence, smart farming, livestock production, animal welfare, sustainable agriculture, automation, dairy cattleAbstract
This paper analyzes the use of artificial intelligence (AI) and smart technologies in dairy cattle farming through a case study of the Czech SME Agrosoft. The study assesses how AI-based management systems affect operational efficiency, animal welfare, and environmental sustainability, and identifies challenges in their implementation. It focuses on automated milking, thermal imaging for health monitoring, and IoT-supported environmental control. A qualitative approach was applied, combining semi structured interviews with developers and implementation specialists with an analysis of internal documentation. The findings show that AI enables earlier detection of health issues, more efficient feeding and barn-climate control, and reduced labor demands, contributing to improved herd welfare and lower operating costs. At the same time, the research highlights organizational barriers, especially the need for staff training and gradual workflow adaptation. The paper concludes that AI can significantly advance sustainable, welfare-oriented livestock farming, provided that farms ensure sufficient technological readiness and human-centered change management.
Downloads
References
Ahmadi, B. V., D’haese, M., Dessein, J., Duquenne, P., & Verbeke, W. (2018). Machine learning in predicting energy use of social housing. Applied Energy, 222, 890-904.
Al-Ahmed, S.A., Ahamed, T. (2024). AI × IoT: Increasing Agricultural Productivity of Crops, Orchards, and Livestock Management Using Smart Agricultural Space for Achieving SDGs. In: Ahamed, T. (eds) IoT and AI in Agriculture. Springer, Singapore.
Alguliyev, R., et al. (2021). Plant disease detection based on a deep model. Soft Comput, 25, 13229–13242.
Araujo SO, Peres RS, Barata J, Lidon F, Ramalho JC (2021) Characterising the agriculture 4.0 landscape – emerging trends, challenges and opportunities. Agronomy 11(4):667.
Arsevska E, Valentin S, Rabatel J, de Goe¨r de Herve´ J, Falala S, Lancelot R, et al. (2018) Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System. PLoS ONE 13(8): e0199960.
Bannink, A., & France, J. (2016). Nutritional approaches to reduce enteric methane formation in ruminants. Journal of Dairy Science, 99(6), 4521-4532.
Bao, J., & Xie, Q. (2022). Artificial intelligence in animal farming: A systematic literature review. Journal of Cleaner Production, 331, 129956.
Berckmans, D. (2017). Precision livestock farming technologies for welfare management in intensive livestock systems. Revue Scientifique et TechniqueOffice International des Epizooties, 36(1), 111-120.
Bhagat, P. R., Naz, F., & Magda, R. (2022). Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis. PloS one, 17(6), e0268989.
Bök, P. B., & Micucci, D. (2024). The future of human and animal digital health platforms. Journal of Reliable Intelligent Environments, 1-12.
Bumbalek, R., Havelka, Z., Olsan, P., Smutny, L., & Stehlik, R. (2021). A brief overview of artificial intelligence and its applications. In Aktuální otázky bioklimatologie zvířat 2021 (pp. 10–13). Praha: Výzkumný ústav živočišné výroby.
Bumbalek, R., Smutny, D., Smutny, L., Stehlik, R., & Koziskova, M. (2022). The use of a robotic hand as a substitute for the manual work of milkers. In Aktuální otázky bioklimatologie zvířat 2022 (pp. 86–88). Praha: Výzkumný ústav živočišné výroby.
Carabaño, M. J., Kadarmideen, H. N., & Sørensen, P. (2020). Applications and perspectives of precision livestock farming tools for sustainable cattle production. Journal of Animal Breeding and Genetics, 137(1), 3-17.
Charfeddine, M., & Jemai, Z. (2018). Forecasting electricity consumption: A review of the most recent methods with an application. Renewable and Sustainable Energy Reviews, 81, 1222-1233.
Contla Hernández, B., Lopez-Villalobos, N., Vignes, M. (2021). Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods. Animals, 11, 2154.
Dayoub, M.; Shnaigat, S.; Tarawneh, R.A.; Al-Yacoub, A.N.; Al-Barakeh, F.; AlNajjar, K. Enhancing Animal Production through Smart Agriculture: Possibilities, Hurdles, Resolutions, and Advantages. Ruminants 2024, 4, 22-46.
De Vries, A., Bliznyuk, N., & Pinedo, P. (2023). Invited Review: Examples and opportunities for artificial intelligence (AI) in dairy farms. Applied Animal Science, 39(1), 14-22.
Dhanaraju, M.; Chenniappan, P.; Ramalingam, K.; Pazhanivelan, S.; Kaliaperumal, R. (2022). Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture, 12, 1745.
Ezanno, P., Picault, S., Beaunée, G. et al. Research perspectives on animal health in the era of artificial intelligence. Vet Res 52, 40 (2021).
Gai, R., Chen, N., Yuan, H. (2021). A detection algorithm for cherry fruits based on the improved YOLO-v4 model. Neural Comput & Applic.
García, A., Sánchez, A. J., & Borrajo, L. (2018). A review on the application of artificial intelligence to the livestock sector. Computers in Industry, 102, 110-123.
Gerdan Koc, D., Koc, C., Polat, H.E. et al. Artificial intelligence-based camel face identification system for sustainable livestock farming. Neural Comput & Applic 36, 3107–3124 (2024). https://doi.org/10.1007/s00521-023-09238-w
Gras, M. A., Untea, A. E., & Lefter, N. A. (2024). Advancing livestock technology: Intelligent systemization for enhanced productivity, welfare, and sustainability. AgriEngineering, 6(2), 1479-1496.
Hruska, J., Kuchtik, J., Zidek, J., & Ederová, E. (2019). Application of artificial intelligence in precision agriculture: A review. Computers and Electronics in Agriculture, 161, 272-287.
Hu, W., Murphy, M. R., & Baldwin, R. L. (2005). Artificial neural network models for prediction of ruminal fermentation in continuous culture. Journal of Dairy Science, 88(5), 1836-1843.
Kawagoe, Y., Kobayashi, I., & Zin, T. T. (2023). Facial region analysis for individual identification of cows and feeding time estimation. Agriculture, 13(5), 1016.
Kumar, S., et al. (2018). Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement, 116, 1-17.
Li, N. et al., (2020). Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal, 3(14), 617-625.
Liakos, K.G.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine learning in agriculture: A review. Sensors 2018, 18, 2674.
Liu Y, Ma X, Shu L, Hancke GP, Abu-Mahfouz AM (2021) From industry 4.0 to agriculture 4.0: current status, enabling technologies, and research challenges. IEEE Trans Industr Inform 17(6):4322–4334.
Massei, G., Hartley, S. E., & Smith, L. A. (2020). The use of chemical repellents and deterrents in vertebrate management. International Journal of Pest Management, 66(4), 309-317.
Melfsen, A., Lepsien, A., Bosselmann, J., Koschmider, A., & Hartung, E. (2023). Describing behavior sequences of fattening pigs using process mining on video data and automated pig behavior recognition. Agriculture, 13(8), 1639.
Monteiro, A.; Santos, S.; Gonçalves, P. Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals 2021, 11, 2345.
Neethirajan, S. (2017). Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research, 12, 15-29.
Pavlovic, D. et al. (2021). Classification of Cattle Behaviours Using NeckMounted Accelerometer-Equipped Collars and Convolutional Neural Networks. Sensors, 21, 4050.
Parico, A.I.B., Ahamed, T. (2021). Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT. Sensors, 21, 4803.
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. https://doi.org/10.3390/s24185890
Sindiramutty, S. R., Jhanjhi, N. Z., Tan, C. E., Tee, W. J., Lau, S. P., Jazri, H., Ray, S. K., & Zaheer, M. A. (2024). IoT and AI-Based Smart Solutions for the Agriculture Industry. In M. Ghonge, N. Pradeep, N. Jhanjhi, & P. Kulkarni (Eds.), Advances in Explainable AI Applications for Smart Cities (pp. 317-351). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-6361-1.ch012
Venkatesan, R., & Thirunavukkarasu, I. (2019). Real-time data analytics in agriculture using IoT and big data. Computers and Electronics in Agriculture, 161, 272-287.
Verschae, R. (2023). Smart technologies in agriculture. In Encyclopedia of Smart Agriculture Technologies (pp. 1-11). Cham: Springer International Publishing.
Wang, F., Fu, X., Duan, W., Wang, B., & Li, H. (2023). Visual Detection of Lost Ear Tags in Breeding Pigs in a Production Environment Using the Enhanced Cascade Mask R-CNN. Agriculture, 13(10), 2011.
Zhang, F.; Zhang, Y.; Lu, W.; Gao, Y.; Gong, Y.; Cao, J. 6G-Enabled Smart Agriculture: A Review and Prospect. Electronics 2022, 11, 2845.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Economic of Agriculture

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.