AgSlicing: Predictable RAN and Spectrum Slicing for Precision Agriculture

AgSlicing is developing predictable radio access network (RAN) slicing and spectrum slicing so that farm machinery, sensors, and AI applications can share rural wireless networks with the reliability and timing they need. The project is funded by the National Science Foundation’s Verticals-enabling Intelligent Network Systems (VINES) program and is led by Iowa State University in collaboration with the University of Virginia and industry partners.

The Challenge

Precision agriculture depends on “last-acre” connectivity: moving data and AI models among machines in the field and real-time analytics in the edge and cloud. Agriculture applications have very different communication needs, and today’s wireless networks cannot serve them predictably or offer affordable, predictable access to spectrum.

Our Approach

• Predictable slicing. RAN and spectrum slicing that give each farm application the capacity, latency, and reliability it requires.
• Reliable, timely delivery. Predictable interference control and per-packet reliability and timeliness guarantees.
• Scalable, intelligent radio access. Scalable massive MIMO, accurate agricultural channel modeling, and reinforcement learning for capacity allocation.
• Open and deployable. A standards-compliant, software-based design with O-RAN support.
• From lab to field. Maturing these building blocks from early prototypes (TRL 3–4) into a deployment-ready system (TRL 8).

Field Validation

AgSlicing will be evaluated in the ARA Wireless Living Lab, spanning 500+ square kilometers of farmland and rural communities in central Iowa, and through commercial pilots in rural America, including rural Montana and Washington.  Example use cases include crop scouting and precision spot spraying in precision weed control, which require predictable low latency, high reliability, and high throughput.

Expected Impact

• Agriculture and the economy. Economic benefits for U.S. agriculture and beyond, along with less hunger and lower fuel use, pollution, water use, and chemical application.
• Data for the community. First-of-its-kind real-world measurement data and channel models for agricultural wireless networks, with curated experiments for reproducibility.
• Standards and policy. Engagement with the NextG Alliance, O-RAN Alliance, OnGo Alliance, and ARA-OTIC to inform 6G, rural broadband, and agricultural policy, with relevance to utilities, public safety, and defense.
• Education and outreach. Undergraduate and graduate research, K-12 outreach, and broadening participation at Iowa State University and the University of Virginia.

This material is based upon work supported by the U.S. National Science Foundation under Grant No. 2554530. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.