Informatics & dairy industry coalition: AI trends and present challenges

TL;DR

The paper surveys the integration of industrial informatics and AI within the dairy industry, focusing on how IIoT-enabled data from sensors, wearables, and dairy automation can be harnessed for per-animal management and process optimization. It reviews existing ML approaches—ranging from traditional supervised methods to deep learning—and discusses data fusion strategies and the rise of explainable AI to promote operator trust. Key contributions include a synthesis of dairy-specific AI applications (e.g., calving, lameness, mastitis, estrus detection) and a candid assessment of current limitations such as data quality, interoperability, and cost barriers. The work offers actionable insights for researchers and industry stakeholders to advance precision dairy farming while considering animal welfare, data governance, and sustainability.

Abstract

Artificial Intelligence (AI) can potentially transform the industry, enhancing the production process and minimizing manual, repetitive tasks. Accordingly, the synergy between high-performance computing and powerful mathematical models enables the application of sophisticated data analysis procedures like Machine Learning. However, challenges exist regarding effective, efficient, and flexible processing to generate valuable knowledge. Consequently, this work comprehensively describes industrial challenges where AI can be exploited, focusing on the dairy industry. The conclusions presented can help researchers apply novel approaches for cattle monitoring and farmers by proposing advanced technological solutions to their needs.

Figures (4)

• Figure 1: Funding trend on precision dairy farming.
• Figure 2: Use trend of ai technologies (patent applications, issue rates, trend forecast).
• Figure 3: Opportunities and limitations of precision dairy farming.
• Figure 4: Elements of a precision farming solution.