The paper presents a hybrid edge‑cloud digital twin for welfare‑constrained environmental control in poultry farms. It combines distributed sensing, on‑device state estimation, a physics‑based thermodynamic model with a learned residual, and model predictive control to adaptively manage temperature and ammonia levels. Experiments in a broiler testbed show significant improvements: temperature prediction error drops from 1.8 °C to 0.4 °C, ammonia violations fall by 90 %, and communication needs are reduced 30‑fold, with a Domain Transfer Score of 0.92 indicating strong cross‑facility robustness.
arXiv:2607. 28652v1 Announce Type: cross Abstract: Early-life monitoring in laying hens remains constrained by fragmented single-modality sensing and the absence of formal system-level state representations.
arXiv:2608. 09949v1 Announce Type: new Abstract: This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving from data analysis to autonomous, AI-guided experimentation.
arXiv:2605. 02524v2 Announce Type: replace Abstract: Accurate reconstruction of greenhouse climate variables from sparse sensor measurements is essential for intelligent environmental monitoring, automated climate control, and precision agriculture.
arXiv:2506. 03168v2 Announce Type: replace-cross Abstract: Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing.
arXiv:2609.14036v1 Announce Type: cross Abstract: Fresh fruits and vegetables (FFVs) are highly perishable, and cold-chain breaks contribute significantly to global food waste. While Machine Learning...
The paper introduces FAIRY, a full-stack smart‑agriculture agent system deployed on a soybean research farm at Harbin Institute of Technology. FAIRY executes and evaluates end‑to‑end agronomic operations—from ridge preparation to storage—using an event‑driven world model that integrates machinery, sensors, drones, satellite data, weather, crop models, and historical yields. The system implements a comprehensive agentic stack and is used to benchmark nine state‑of‑the‑art agent controllers across 100 full‑season soybean scenarios, assessing success, spatiotemporal correctness, token cost, and edge‑device runtime.
arXiv:2607. 22548v1 Announce Type: new Abstract: Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metrics.
arXiv:2504. 18451v2 Announce Type: replace Abstract: Rapid global population growth underscores the need for digitally enabled agricultural systems that support sustainable food production and data-driven resource management for farmers and stakeholders.
arXiv:2608.30186v1 Announce Type: cross Abstract: Digital twin technologies have the potential to improve operational flexibility and responsiveness capabilities of nuclear systems. To provide decisi...
The paper introduces a closed‑loop cyber‑physical system for autonomous model lifecycle management in automotive manufacturing, deployed since 2023. It manages paired physics and reinforcement‑learning models, selecting the best candidate through competitive retraining cycles and a Conductor orchestrator that handles plant‑wide inventories and fallback controls. The system incorporates an operator‑trust gate that rejects 23% of policies that deviate from established practice, achieving 28‑45% process stability improvements with no safety incidents.
arXiv:2505. 18201v2 Announce Type: replace-cross Abstract: Controlling flapping-wing drones requires controllers that handle time-varying, nonlinear, underactuated dynamics from incomplete, noisy sensor data.
arXiv:2607. 18164v1 Announce Type: cross Abstract: Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift.