Autonomous Agricultural Robots: Physical AI in Agriculture
Autonomous tractors, robots, and connected fleets are moving from the lab to the fields, while the energy demand of the AI data centers that train and control these systems grows in parallel. Physical AI, meaning AI systems that cause physical changes, is revolutionizing agriculture through autonomous machines that autonomously navigate, recognize plants, and perform tasks. At the same time, the increasing power consumption of AI data centers requires new energy architectures, including microgrids and 800 V DC systems, to ensure efficiency and resilience.
Quelle: Own Representation
Introduction
The integration of Artificial Intelligence (AI) into physical systems, known as Physical AI, is transforming industries like agriculture. Autonomous working machines take over tasks in the fields, while the underlying AI infrastructure in data centers develops a significant energy requirement. This development requires innovative solutions in both application and energy supply.
Current Status
On October 28, 2025, Agtonomy closed an 18 million USD Series B funding round . Led by DBL Partners and with participation from Nuveen, the company’s Physical AI platform is set to be scaled in agricultural and off-road applications. Industry media such as Precision Farming Dealer confirm the amount and the goal of expanding OEM integrations and fleet deployments. Previously, WineBusiness reported on the expansion of autonomous fleets into the Southeastern USA and Australia, suggesting real pilot projects in viticulture and fruit growing.
Quelle: saarland-informatics-campus.de
Autonomous agricultural robots like this one are already in use on many fields, supplementing or replacing traditional agricultural machinery.
On the system side, Delta today presented a Microgrid solution specifically for AI data centers at Energy Taiwan 2025. This solution couples renewable sources, storage, and diesel/gas generators to ensure voltage quality under highly dynamic AI loads. New features include solid-state transformers with up to 98.5% efficiency for 800 V DC. Already in September, Delta showcased a Data Center Microgrid Architecture at RE+ with uninterrupted on/off-grid operation and fast regulation.
Quelle: agtecher.com
The variety of autonomous agricultural robots ranges from small weed control units to larger machines for sowing and harvesting.
The context of these developments is the rapidly increasing energy demand of data centers. The IEA expects the worldwide electricity consumption of data centers to approximately double by 2030 , with a forecast of 945 TWh and beyond. In the US, data centers could consume up to EPRI-Analysen of electricity by 2030, according to 9%.
Analysis
The motivation for using Physical AI in agriculture lies in rising labor costs and labor shortages in specialty crops. Autonomous devices promise predictable, safe, and repeatable operations. . For manufacturers, Physical AI offers the opportunity to realize software margins in traditionally hardware-heavy markets .
On the energy side, the dynamics of training and inference loads in AI data centers necessitate a mix of renewables, storage, and conventional sources. These are orchestrated in a microgrid to manage quality, resilience, and costs. The introduction of 800 V DC as a new backbone voltage and solid-state transformers streamlines the conversion chain and reduces losses, which is strategically interesting for operators facing space and copper constraints.
Quelle: YouTube
Brief Field View: The clip shows an autonomous working machine performing typical tasks such as mowing and spraying – helpful for seeing the reality of deployment.
Facts & Claims
Confirmed is the USD 18 million financing round of Agtonomy , its goals and investors. Also confirmed is Delta's presentation of the AI Data Center Microgrid solution with 800 V DC orientation and solid-state transformer values. The IEA forecast for the growing electricity demand of data centers is publicly available.
It is unclear how quickly 800 V DC and microgrids will be broadly implemented in existing data centers. This depends on permits, grid situations, and Capex cycles. Manufacturers and semiconductor providers describe the benefits, but concrete migration paths vary by location, as STMicroelectronics shows.
The claim that AI already consumes the majority of data center electricity is false. Reliable classifications currently see AI as a contributing factor that can grow significantly by 2030, as Carbon Brief explains. Likewise, the assumption that microgrids are only off-grid solutions is false; the DOE-Definition explicitly emphasizes regular grid operation with optional island mode.
Quelle: sydney.edu.au
The combination of autonomous field robots and drones enables precise monitoring and cultivation of agricultural areas.
Impact & Recommendations
For farms with specialty crops, a pilot with autonomous devices is worthwhile where repetitive, safety-critical, or time-critical work dominates. Safety standards such as ISO 18497-1 to -4 and training concepts for mixed fleets should be reviewed.
For IT and energy managers in data centers, a microgrid feasibility study is useful. Load profiles, on-site generation/storage, island operation, protection concepts, and possible 800 V DC roadmaps belong in a structured approach such as the NREL-CORE-Design. To classify numbers and narratives, it is recommended to mirror forecasts against primary sources like the IEA and the NREL .
Quelle: YouTube
Briefly Explained: The trade fair tour visually summarizes Delta’s microgrid and power path approach for AI data centers.
Conclusion
Physical AI brings autonomous working machines into the field, not as a show effect, but as an answer to real bottlenecks in agriculture and off-road environments, as the financing of Agtonomy shows. At the same time, the AI boom in the data center forces new power architectures, where microgrids and 800 V DC combine efficiency, quality, and resilience, driven by the increasing energy demand . Those who start setting up pilot areas and energy roadmaps today learn quickly and reduce risks. This creates the foundation for AI to not only make smart decisions but operate reliably and sustainably in the physical world.