Logo PTI Logo FedCSIS

Communication Papers of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 49

A Holistic CPS for Smart Precision Livestock Farming: Multi-Actor Collaborative Decision-Making Across Ecosystem Domains

, ,

DOI: http://dx.doi.org/10.15439/2026F3040

Citation: Sonia Hajri-Gabouj, ,

Full text

Abstract. Precision Livestock Farming (PLF) increasingly relies on integrated technologies to improve animal health, welfare, reproduction, and productivity within a complex ecosystem. However, current approaches often deal with specific use cases, single domain, and remain fragmented, relying on siloed analytics tools and unilateral decision-making. This paper proposes a holistic Cyber-Physical System (CPS) for smart PLF that enables seamless end-to-end processes, spanning several stakeholders' perspectives and farm domains: animals, environment, production, and logistics. The proposed layered and modular architecture leverages adaptive AI capabilities enabling intelligence-driven functionalities within an integrated farm workflow, and supports automated, human-in-the-loop and hybrid modes. Both domain-specific and shared views are provided to foster collaborative decision-making, and control across operations. The CPS capabilities are illustrated through various use cases, highlighting their relevance across different operational settings. By integrating sensing, data, analytics, human perspectives, and collaborative decision-making, the proposed CPS enhances visibility, transparency, and efficiency in livestock management and monitoring.

References

  1. C. Aquilani, A. Confessore, R. Bozzi, F. Sirtori, & C. Pugliese, “Review: Precision Livestock Farming technologies in pasturebased livestock systems”, Animal, vol. 16, 100429, 2022. https://doi.org/10.1016/j.animal.2021.100429
  2. C.J. Rutten, A.G.J. Velthuis, W. Steeneveld, & H. Hogeveen, “Invited review: sensors to support decision-making in precision livestock farming”, J. Dairy Sci., vol. 96, no. 4, pp. 1928–1952, 2013. https://doi.org/10.3168/jds.2012-6107
  3. P. Niloofar, I. Halachmi, & D. Berckmans, et al., “Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges”, Computers and Electronics in Agriculture, vol. 190, no. 5, 106406, 2021. https://doi.org/10.1016/j.compag.2021.106406
  4. S. Wolfert, L. Ge, C. Verdouw, & M. J. Bogaardt„ “Big data in smart farming—A review”, Agricultural Systems, vol. 153, pp. 69–80, 2017. https://doi.org/10.1016/j.agsy.2017.01.023
  5. Krampe, C., Ingenbleek, P. T., Niemi, J. K., & Serratosa, J., “Designing precision livestock farming system innovations: A farmer perspective”, Journal of Rural Studies, vol. 111, 103397, 2024. https://doi.org/10.1016/j.jrurstud.2024.103397
  6. ClearFarm Project, “Co-designed Welfare Monitoring Platform for Pig and Dairy Cattle”, Horizon 2020 Research and Innovation Programme, Grant Agreement No. 862919, 2019-2024. https://clearfarm.eu
  7. C. L. Sumner, M. A. G. von Keyserlingk, & D. M. Weary, “Perspectives of farmers and veterinarians concerning dairy cattle welfare”, Animal Frontiers, vol. 8, no. 1, pp. 8–13, 2018. https://doi.org/10.1093/af/vfx006
  8. B. E. Akinyemi, L. Jessiman, S. P. Turner, & J. M. Siegford, “Beyond the farm: stakeholder perspectives on precision livestock farming in the swine industry”. Frontiers in Animal Science, vol. , 1710969, 2025. https://doi.org/10.3389/fanim.2025.1710969
  9. B. Jiang, W. Tang, L. Cui, & X. Deng, “Precision livestock farming research: A global scientometric review”, Animals, vol. 13, no. 13, 2096, 2023. https://doi.org/10.3390/ani13132096
  10. R. Mkadmi, R.Y. Douss, & A. Benazza-Benyahia, “Cow monitoring system based on deep learning models for multiple objects detection and tracking”. In 2024 IEEE/ACS 21st International Conference on Computer Systems and Applications (AICCSA), 1–8. https://doi.org/10.1109/AICCSA63423.2024.10912629
  11. J. L. Kleen & R. Guatteo, “Precision Livestock Farming: What Does It Contain and What Are the Perspectives?”, Animals, vol. 136, no. 5, 779, 2023. https://doi.org/10.3390/ani13050779
  12. D. Distante, C. Albanello, H. Zaffar, S. Faralli, and D. Amalfitano, “Artificial intelligence applied to precision livestock farming: a tertiary study”, Smart Agricultural Technology, vol. 11, 100889, 2025. https://doi.org/10.1016/j.atech.2025.100889
  13. Boyer, C. N., Cavasos, K. E., Greig, J. A., & Schexnayder, S. M., “Influence of risk and trust on beef producers’ use of precision livestock farming”. Computers and Electronics in Agriculture, vol. 218, pp. 108641, 2024. https://doi.org/10.1016/j.compag.2024.108641
  14. X. Du, M. Yu, Z. Zhang, M. Tong, Y. Zhu & C. Xue, “A Task- and Role-Oriented Design Method for Multi-User Collaborative Interfaces”. Sensors, vol. 25, no. 6, 1760, 2025. https://doi.org/10.3390/s25061760
  15. A. Rohan, M. S. Rafaq, M. J. Hasan, F. Asghar, A. K. Bashir, & T. Dottorini, “Application of deep learning for livestock behaviour recognition: a systematic literature review”, Computers and Electronics in Agriculture, vol. 224, 109115, 2024. https://doi.org/10.1016/j.compag.2024.109115
  16. Y. Wang, S. Mücher, W. Wang, L. Guo, & L. Kooistra, “A review of three-dimensional computer vision used in precision livestock farming for cattle growth management”, Computers and Electronics in Agriculture, vol. 206, 107687, 2023.https://doi.org/10.1016/j.compag.2023.107687
  17. H. Zhao, D. Hong, J. Wang, & R. Ma„ “Advancing livestock facial recognition with AI: from algorithm innovation to end-to-end precision farming application”, AgriEngineering, vol. 8, no. 3, 77, 2026. https://doi.org/10.1016/j.compag.2023.107687
  18. A. Montalvo, O. Camacho, & D. Chavez, “The impacts of precision livestock farming tools on the greenhouse gas emissions of an average Scottish dairy farm”, Sustainability, vol. 17, no. 14, 6393, 2025. https://doi.org/10.3389/fsufs.2024.1385672
  19. A. Nsabiyeze, M. Zhang, J. Li, Q. Zhao, & X. Zhang, “Precision livestock farming for climate-resilient livestock management: a review of real-time monitoring and decision support systems”, Journal of Cleaner Production, vol. 524, 146454, 2025. https://doi.org/10.1016/j.jclepro.2025.146454
  20. U. Kaur, “Cyber-Physical Systems with robots and AI for precision dairy farming”, Journal of Animal Science, vol. 102, pp. 297–298, 2024. https://doi.org/10.1093/jas/skae234.340
  21. S. J. Russell & P. Norvig, Artificial Intelligence: A Modern Approach , (4th ed.), Pearson, 2020.
  22. S. Greco, M. Ehrgott, & J. R. Figueira, Eds., Multiple Criteria Decision Analysis: State of the Art Surveys., New York, NY, USA: Springer, 2016.
  23. S. S. Narli, H. Schmidt, A. Firouzabadi, L. Schönnagel, M.S. Reich, & S. Reitmaier, “Automated detection of lameness in dairy cattle through computer vision analysis of back shape characteristics”, Computers in Biology and Medicine, vol. 197, 111038, 2025. https://doi.org/10.1016/j.compbiomed.2025.111038
  24. T. Lodkaew, K. Pasupa, & C.K. Loo, “CowXNet: An automated cow estrus detection system”. Expert Systems with Applications, vol. 211, 118550, 2023. https://doi.org/10.1016/j.eswa.2022.118550
  25. R. Lardy, Q. Ruin, Q., I. & Veissier, “Discriminating pathological, reproductive or stress conditions in cows using machine learning on sensor-based activity data”. Computers and Electronics in Agriculture, vol. 204, 107556, 2023. https://doi.org/10.1016/j.compag.2022.107556
  26. G. Provolo, C. Brandolese, M. Grotto, A. Marinucci, N. Fossati, O. Ferrari, E. Beretta, & E. Riva, “An Internet of Things Framework for Monitoring Environmental Conditions in Livestock Housing to Improve Animal Welfare and Assess Environmental Impact”. Animals, vol. 15, no.5, 644, 2025. https://doi.org/10.3390/ani15050644
  27. F. M. Tangorra, E. Buoio, A. Calcante, A. Bassi, & A. Costa, “Internet of Things (IoT): Sensors Application in Dairy Cattle Farming”. Animals, vol. 14, no.21, 3071, 2024. https://doi.org/10.3390/ani14213071
  28. M. Mansour, A, Gamal, A. I., Ahmed, L. A. Said, A., Elbaz, N., Herencsar, & A. Soltan, “Internet of Things: A Comprehensive Overview on Protocols, Architectures, Technologies, Simulation Tools, and Future Directions”. Animals, vol. 16, no.8, 3465, 2023. https://doi.org/10.3390/en16083465
  29. S. Karatsiolis, P. Panagi, V. Vassiliades, A. Kamilaris, N. Nicolaou & E. StavrakisR, “Towards understanding animal welfare by observing collective flock behaviors via AI-powered Analytics”. In 2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS), pp. 643-648. http://dx.doi.org/10.15439/2024F2064