SBIR Phase I: Development of AI and Software Platform for Cost-Effective Soil Carbon Measurement and Assessment

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The broader impact/ commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable accurate soil health measurements. The proposed project will develop new machine learning (ML) models for accurate soil carbon predictions. The system will generate a statistically- and agronomically-significant soil carbon sampling design customizable for landowner preferences and the parcel’s characteristics. The envisioned system will integrate a handheld measurement probe and a cloud-based data analysis and management platform for automated, scalable, cost-effective, and authoritative soil carbon sampling. The proposed project will develop a novel machine learning (ML) model for the in-situ prediction and analysis of soil carbon and development of automated sampling plans. The proposed system integrates a soil stratification and sampling plan design algorithm; software integrated with a handheld probe; and an integrated cloud-based analysis and data management platform. The research will address technical challenges including: (1) detecting complex covariates; (2) aligning stratification with soil carbon verification protocols; (3) developing novel digital pedology techniques; and (4) developing machine learning tools able to iterate measurement plans in near real-time. The system's outputs will enable parallel regional models and adaptive sampling plans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.