# A Hybrid Edge Cloud Digital Twin for Welfare-Constrained Control in Poultry Production

arXiv: https://arxiv.org/abs/2608.20367 · HTML: https://mlsanity.com/paper/2608.20367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Digital Twin in Industries: A Comprehensive Survey](https://mlsanity.com/paper/2412.00209/) (2024)
- [Evaluating the Impact of Data Availability on Machine Learning-augmented MPC for a Building Energy Management System](https://mlsanity.com/paper/2407.13308/) (2024)
- [Cloud-Fog Automation: The New Paradigm towards Autonomous Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2504.04908/) (2025)

## Abstract (arXiv)

Poultry production operates under tightly coupled environmental and biological dynamics, yet commercial climate control remains largely heuristic, limiting welfare assurance and operational efficiency. We introduce an edge-cloud digital twin framework for real-time, welfare-constrained environmental control in poultry facilities. The framework integrates distributed sensing, on-device state estimation, a hybrid physics-data model, and model predictive control to enable anticipatory and adaptive management under practical farm constraints. A grey-box thermodynamic and mass-balance formulation is augmented with a learned residual that captures unmodeled biological variability, including activity-dependent metabolic heat. This hybrid model is embedded within a state-space representation for real-time estimation and control at the edge, while cloud coordination supports cross-farm learning and long-horizon optimization. Bandwidth-aware processing and asynchronous synchronization enable deployment in connectivity-limited environments. Evaluation in a high-fidelity broiler production testbed demonstrates substantial gains over rule-based control and physics-only modeling. Temperature prediction error is reduced from 1.8 degrees Celsius to 0.4 degrees Celsius, ammonia constraint violations decrease by 90 percent, and communication requirements are lowered approximately 30-fold through edge-first processing. A Domain Transfer Score of 0.92 further indicates strong robustness across facility conditions. These results show that physically grounded digital twins, coupled with real-time control, enable scalable and welfare-aware management of biological production systems.