Carbon-credit markets represent a potential revenue
channel for climate-positive agriculture, but high verification
costs create economic barriers for smallholder farmers. Project
overheads can exceed potential credit revenues,and individual
landholdings may be smaller than the minimum project size.
CarbonUnity resolves these issues by combining several small
farmers into a single carbon project and keeping track of
land-specific contributions. The platform features geometrybased
land validation, provenance-aware data ingestion, source
priority selection based on the health condition of a device,
automated monitoring, deterministic reasoning, and multilingual
agricultural advisory. Provenance is preserved for heterogeneous
observations when they enter the reasoning pipeline, while the
areas land are calculated from registered geometries instead of
self-reported numbers. The reported quantities are calculated
prior to generation, and an output guard validates generated
numerical claims against the computed state.The platform
incorporates a machine-learning-based spectral SOC estimator
into its monitoring and reasoning pipeline, thus extending the
scope of the system beyond data collection to soil property
estimation through modeling. The estimator is benchmarked
as a single-run baseline using standardized targets; its predictions
should not be considered physical measurements of SOC stock
until the conversion is validated. System evaluation includes
output guard regression tests and a simulated sensor-monitoring
scenario. These outcomes illustrate the operational capabilities of
the platform, while further validation of the sensing and estimation
pipeline and implementation of the proposed verification and
settlement workflow represent essential steps in deploying carbon
credits.

carbon credits, smallholder agriculture, soil organic carbon, measurement reporting and verification, data provenance, intelligent sensing, multilingual agentic agricultural advisory