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

Abstract

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.