Active research & field projects

Regenerative Farming Digital Twin

A Model-Based Systems Engineering (MBSE) project integrating LoRaWAN soil sensors, IoT water monitoring, Raspberry Pi edge computing, and satellite imagery into a unified digital twin of a regenerative agricultural operation in Hugesville, MO. The project bridges academic research, field engineering, and practical farm management — building infrastructure that can be replicated across other regenerative operations.

The core of this project is a living, data-driven model of the farm system — soil health, water movement, and land-use patterns — that researchers, engineers, and farm operators can query, simulate, and act on in near real-time. Raspberry Pi units deployed across the farm run Python data pipelines at the edge, handling sensor ingestion and preprocessing before routing structured data to the digital twin. LoRaWAN gateways provide long-range, low-power connectivity across field zones where cellular coverage is unreliable. Satellite data layers — pulled via API and processed through Python scripts — provide macro-scale context including NDVI vegetation indices and soil carbon proxies that complement the ground-level sensor readings. The "Soil Daddy" soil interface, presented to MBSE engineers in Spring 2026, serves as the primary data bridge between physical soil instrumentation and the systems model. This summer the team is actively pursuing sensor funding to expand the network density and begin the data validation phase. The longer-term goal is a replicable framework — a digital twin template that other regenerative farms and research institutions can adopt with minimal customization.