From Decision-Support Tools to Digital Twins: A Review of Digital Farming, Data Platforms, and AI for Sustainable Dairy Systems

This project gives step‑by‑step guidance for building digital decision‑support systems and digital‑twin frameworks for sustainable dairy production. It examines the shift from traditional farm management to real‑time digital twins and creates a complete data‑to‑decision pipeline. The pipeline brings together: – data‑foundation layers, – interoperability protocols, – governance frameworks, and – machine‑learning decision engines that aim to improve dairy‑farm operations. The research also tackles common problems such as data drift, fragmented identifiers, and weak ground truth in livestock monitoring. For B.Tech students, the material offers a clear method to design and simulate a local digital‑twin prototype for monitoring a dairy herd. The framework uses sensor data, predictive analytics for milk yield,

and resource‑optimization algorithms to raise sustainability metrics. With this review and implementation support, students can see how IoT sensors, cloud data platforms, and predictive models work together for real‑time decision‑making in dairy technology. The project stresses practical system architecture, data‑flow design, and validation strategies to ensure the solution works on modern dairy farms.

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