The future of big data and artificial intelligence on dairy farms: A proposed dairy data ecosystem.

[en] The dairy sector should overcome challenges in productivity, sustainability, and data management by adopting intelligent, scalable, and privacy-preserving technological solutions. Adopting data and artificial intelligence (AI) technologies is essential to ensure efficient operations and informed decision making and to keep a competitive market advantage. This paper proposes an integrated, multimodal AI framework to support data-intensive dairy farm operations by leveraging big data principles and advancing them through AI technologies. The proposed architecture incorporates edge computing, autonomous AI agents, and federated learning to enable real-time, privacy-preserving analytics at the farm level and promote knowledge sharing and refinement through research farms and cloud collaboration. Farms collect heterogeneous data, which can be transformed into embeddings for both local inference and cloud analysis. These embeddings form the input of AI agents that support health monitoring, risk prediction, operational optimization, and decision making. Privacy is preserved by sharing only model weights or anonymized data externally. The edge layer handles time-sensitive tasks and communicates with a centralized enterprise cloud hosting global models and distributing updates. A research and development cloud linked to research farms ensures model testing and validation. The entire system is orchestrated by autonomous AI agents that manage data, choose models, and interact with stakeholders, and human oversight ensures safe decisions, as illustrated in the practical use case of mastitis management. This architecture could support data integrity, scalability, and real-time personalization, along with opening up space for partnerships between farms, research institutions, and regulatory bodies to promote secure, cross-sector innovation.

Disciplines :

Agriculture & agronomy
Computer science

Author, co-author :

Hostens, Miel ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853 ; Faculty of Bioscience Engineering, Department of Animal Science and Aquatic Ecology, Ghent University, Merelbeke, 9000 Ghent, Belgium

Franceschini, Sébastien ; Université de Liège - ULiège > Département GxABT > Modélisation et développement ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853

van Leerdam, Meike ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853

Yang, Haiyu ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853

Pokharel, Sabina ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853

Liu, Enhong ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853

Niu, Puchun ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853

Zhang, Hanlu ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853

Noor, Saba ; Faculty of Bioscience Engineering, Department of Animal Science and Aquatic Ecology, Ghent University, Merelbeke, 9000 Ghent, Belgium

Hermans, Kristof ; Faculty of Veterinary Medicine, Department of Internal Medicine, Reproduction and Population Medicine, Ghent University, 9820 Merelbeke, Ghent, Belgium

Salamone, Matthieu ; Faculty of Veterinary Medicine, Department of Internal Medicine, Reproduction and Population Medicine, Ghent University, 9820 Merelbeke, Ghent, Belgium

Sharma, Sumit ; Department of Animal Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853

Language :

English

Title :

The future of big data and artificial intelligence on dairy farms: A proposed dairy data ecosystem.

Publication date :

December 2025

Journal title :

JDS Communications

eISSN :

2666-9102

Publisher :

Elsevier B.V.

Volume :

Issue :

Suppl 1

Pages :

S9 - S14

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S2666910225001917?httpAccept=text/xml

Available on ORBi :

since 07 September 2026

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