Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation...

Area of Science:

• Agricultural Engineering
• Animal Science
• Computational Biology

Background:

• Precision dairy nutrition is evolving with sensor technology and digital twin (DT) integration.
• Existing research lacks a structured framework for comparing diverse DT architectures in dairy cattle.

Purpose of the Study:

• To systematically review and classify sensor-enabled digital twin architectures for dairy cattle.
• To identify optimal DT designs, challenges, and future research directions for precision dairy farming.

Main Methods:

• Systematic literature review of 122 studies (2010-2025) on dairy cattle DTs.
• Development of a novel five-dimensional classification framework for DTs.
• Analysis of hybrid edge-cloud architectures and AI/ML model performance.

Main Results:

• Hybrid edge-cloud DTs with embedded CNN-LSTM models achieve >90% accuracy in behavior recognition.
• Cloud-based simulations optimize feed, reduce emissions, and balance nutrition.
• Prototypes show 15-20% feed conversion efficiency improvement and up to 40% water use reduction.

Conclusions:

• Sensor-enabled DTs offer substantial agronomic and environmental benefits for dairy farming.
• Key challenges include data fusion, network synchronization, and AI recommendation validation.
• Future work requires explainable AI, federated learning, and standardized validation for robust implementation.