Accurately measuring soil carbon is essential for carbon credit programs and sustainable land management, but current methods are expensive and infrequent, limiting their scalability. This project develops an AI-powered, low-cost soil sensing system for frequent, automated carbon monitoring.
The research will focus on adaptive AI calibration models and integrating geospatial mapping to improve accuracy and scalability. By generating a rich, open-access soil carbon database, the project will contribute new scientific insights into soil carbon dynamics, enhancing the accuracy and transparency of carbon credit verification. This will lay the foundation for more farms to participate in carbon markets and adopt regenerative practices.
In the long term, the technology and data produced here could help set new standards for cost-effective carbon verification, particularly for underrepresented small and mid-sized farms who are currently excluded from participating due to high verification costs.